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ICLR.cc/2022/Conference | PAC Prediction Sets Under Covariate Shift | An important challenge facing modern machine learning is how to rigorously quantify the uncertainty of model predictions. Conveying uncertainty is especially important when there are changes to the underlying data distribution that might invalidate the predictive model. Yet, most existing uncertainty quantification algorithms break down in the presence of such shifts. We propose a novel approach that addresses this challenge by constructing \emph{probably approximately correct (PAC)} prediction sets in the presence of covariate shift. Our approach focuses on the setting where there is a covariate shift from the source distribution (where we have labeled training examples) to the target distribution (for which we want to quantify uncertainty). Our algorithm assumes given importance weights that encode how the probabilities of the training examples change under the covariate shift. In practice, importance weights typically need to be estimated; thus, we extend our algorithm to the setting where we are given confidence intervals for the importance weights. We demonstrate the effectiveness of our approach on covariate shifts based on DomainNet and ImageNet. Our algorithm satisfies the PAC constraint, and gives prediction sets with the smallest average normalized size among approaches that always satisfy the PAC constraint. | Accept (Poster) |
ICLR.cc/2019/Conference | Gradient-based learning for F-measure and other performance metrics | Many important classification performance metrics, e.g. $F$-measure, are non-differentiable and non-decomposable, and are thus unfriendly to gradient descent algorithm.
Consequently, despite their popularity as evaluation metrics, these metrics are rarely optimized as training objectives in neural network community.
In this paper, we propose an empirical utility maximization scheme with provable learning guarantees to address the non-differentiability of these metrics.
We then derive a strongly consistent gradient estimator to handle non-decomposability.
These innovations enable end-to-end optimization of these metrics with the same computational complexity as optimizing a decomposable and differentiable metric, e.g. cross-entropy loss. | Reject |
ICLR.cc/2023/Conference | Anisotropic Message Passing: Graph Neural Networks with Directional and Long-Range Interactions | Graph neural networks have shown great potential for the description of a variety of chemical systems.
However, standard message passing does not explicitly account for long-range and directional interactions, for instance due to electrostatics.
In this work, an anisotropic state based on Cartesian multipoles is proposed as an addition to the existing hidden features.
With the anisotropic state, message passing can be modified to explicitly account for directional interactions.
Compared to existing models, this modification results in relatively little additional computational cost.
Most importantly, the proposed formalism offers as a distinct advantage the seamless integration of (1) anisotropic long-range interactions, (2) interactions with surrounding fields and particles that are not part of the graph, and (3) the fast multipole method.
As an exemplary use case, the application to quantum mechanics/molecular mechanics (QM/MM) systems is demonstrated. | Accept: poster |
ICLR.cc/2021/Conference | Convolutional Neural Networks are not invariant to translation, but they can learn to be | When seeing a new object, humans can immediately recognize it across different retinal locations: we say that the internal object representation is invariant to translation. It is commonly believed that Convolutional Neural Networks (CNNs) are architecturally invariant to translation thanks to the convolution and/or pooling operations they are endowed with. In fact, several works have found that these networks systematically fail to recognise new objects on untrained locations. In this work we show how, even though CNNs are not 'architecturally invariant' to translation, they can indeed 'learn' to be invariant to translation. We verified that this can be achieved by pretraining on ImageNet, and we found that it is also possible with much simpler datasets in which the items are fully translated across the input canvas. Significantly, simply training everywhere on the canvas was not enough. We investigated how this pretraining affected the internal network representations, finding that the invariance was almost always acquired, even though it was some times disrupted by further training due to catastrophic forgetting/interference.
These experiments show how pretraining a network on an environment with the right 'latent' characteristics (a more naturalistic environment) can result in the network learning deep perceptual rules which would dramatically improve subsequent generalization. | Reject |
ICLR.cc/2018/Conference | Detecting Statistical Interactions from Neural Network Weights | Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights. Depending on the desired interactions, our method can achieve significantly better or similar interaction detection performance compared to the state-of-the-art without searching an exponential solution space of possible interactions. We obtain this accuracy and efficiency by observing that interactions between input features are created by the non-additive effect of nonlinear activation functions, and that interacting paths are encoded in weight matrices. We demonstrate the performance of our method and the importance of discovered interactions via experimental results on both synthetic datasets and real-world application datasets. | Accept (Poster) |
ICLR.cc/2020/Conference | How to 0wn the NAS in Your Spare Time | New data processing pipelines and novel network architectures increasingly drive the success of deep learning. In consequence, the industry considers top-performing architectures as intellectual property and devotes considerable computational resources to discovering such architectures through neural architecture search (NAS). This provides an incentive for adversaries to steal these novel architectures; when used in the cloud, to provide Machine Learning as a Service (MLaaS), the adversaries also have an opportunity to reconstruct the architectures by exploiting a range of hardware side-channels. However, it is challenging to reconstruct novel architectures and pipelines without knowing the computational graph (e.g., the layers, branches or skip connections), the architectural parameters (e.g., the number of filters in a convolutional layer) or the specific pre-processing steps (e.g. embeddings). In this paper, we design an algorithm that reconstructs the key components of a novel deep learning system by exploiting a small amount of information leakage from a cache side-channel attack, Flush+Reload. We use Flush+Reload to infer the trace of computations and the timing for each computation. Our algorithm then generates candidate computational graphs from the trace and eliminates incompatible candidates through a parameter estimation process. We implement our algorithm in PyTorch and Tensorflow. We demonstrate experimentally that we can reconstruct MalConv, a novel data pre-processing pipeline for malware detection, and ProxylessNAS-CPU, a novel network architecture for the ImageNet classification optimized to run on CPUs, without knowing the architecture family. In both cases, we achieve 0% error. These results suggest hardware side channels are a practical attack vector against MLaaS, and more efforts should be devoted to understanding their impact on the security of deep learning systems. | Accept (Poster) |
ICLR.cc/2020/Conference | Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control | Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics. A promising approach is to embed the high-dimensional observations into a lower-dimensional latent representation space, estimate the latent dynamics model, then utilize this model for control in the latent space. An important open question is how to learn a representation that is amenable to existing control algorithms? In this paper, we focus on learning representations for locally-linear control algorithms, such as iterative LQR (iLQR). By formulating and analyzing the representation learning problem from an optimal control perspective, we establish three underlying principles that the learned representation should comprise: 1) accurate prediction in the observation space, 2) consistency between latent and observation space dynamics, and 3) low curvature in the latent space transitions. These principles naturally correspond to a loss function that consists of three terms: prediction, consistency, and curvature (PCC). Crucially, to make PCC tractable, we derive an amortized variational bound for the PCC loss function. Extensive experiments on benchmark domains demonstrate that the new variational-PCC learning algorithm benefits from significantly more stable and reproducible training, and leads to superior control performance. Further ablation studies give support to the importance of all three PCC components for learning a good latent space for control. | Accept (Poster) |
ICLR.cc/2022/Conference | Fully differentiable model discovery | Model discovery aims at autonomously discovering differential equations underlying a dataset. Approaches based on Physics Informed Neural Networks (PINNs) have shown great promise, but a fully-differentiable model which explicitly learns the equation has remained elusive. In this paper we propose such an approach by integrating neural network-based surrogates with Sparse Bayesian Learning (SBL). This combination yields a robust model discovery algorithm, which we showcase on various datasets. We then identify a connection with multitask learning, and build on it to construct a Physics Informed Normalizing Flows (PINFs). We present a proof-of-concept using a PINF to directly learn a density model from single particle data. Our work expands PINNs to various types of neural network architectures, and connects neural network-based surrogates to the rich field of Bayesian parameter inference. | Reject |
ICLR.cc/2019/Conference | Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks | Recent work has introduced attacks that extract the architecture information of deep neural networks (DNN), as this knowledge enhances an adversary’s capability to conduct attacks on black-box networks. This paper presents the first in-depth security analysis of DNN fingerprinting attacks that exploit cache side-channels. First, we define the threat model for these attacks: our adversary does not need the ability to query the victim model; instead, she runs a co-located process on the host machine victim ’s deep learning (DL) system is running and passively monitors the accesses of the target functions in the shared framework. Second, we introduce DeepRecon, an attack that reconstructs the architecture of the victim network by using the internal information extracted via Flush+Reload, a cache side-channel technique. Once the attacker observes function invocations that map directly to architecture attributes of the victim network, the attacker can reconstruct the victim’s entire network architecture. In our evaluation, we demonstrate that an attacker can accurately reconstruct two complex networks (VGG19 and ResNet50) having only observed one forward propagation. Based on the extracted architecture attributes, we also demonstrate that an attacker can build a meta-model that accurately fingerprints the architecture and family of the pre-trained model in a transfer learning setting. From this meta-model, we evaluate the importance of the observed attributes in the fingerprinting process. Third, we propose and evaluate new framework-level defense techniques that obfuscate our attacker’s observations. Our empirical security analysis represents a step toward understanding the DNNs’ vulnerability to cache side-channel attacks. | Reject |
ICLR.cc/2020/Conference | Few-Shot One-Class Classification via Meta-Learning | Although few-shot learning and one-class classification have been separately well studied, their intersection remains rather unexplored. Our work addresses the few-shot one-class classification problem and presents a meta-learning approach that requires only few data examples from only one class to adapt to unseen tasks. The proposed method builds upon the model-agnostic meta-learning (MAML) algorithm (Finn et al., 2017) and explicitly trains for few-shot class-imbalance learning, aiming to learn a model initialization that is particularly suited for learning one-class classification tasks after observing only a few examples of one class. Experimental results on datasets from the image domain and the time-series domain show that our model substantially outperforms the baselines, including MAML, and demonstrate the ability to learn new tasks from only few majority class samples. Moreover, we successfully learn anomaly detectors for a real world application involving sensor readings recorded during industrial manufacturing of workpieces with a CNC milling machine using only few examples from the normal class. | Reject |
ICLR.cc/2022/Conference | Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks | Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif prediction and so on. Many works have recently proposed to address this problem by using random node features or node distance features. However, they suffer from either slow convergence, inaccurate prediction, or high complexity. In this work, we revisit GNNs that allow using positional features of nodes given by positional encoding (PE) techniques such as Laplacian Eigenmap, Deepwalk, etc. GNNs with PE often get criticized because they are not generalizable to unseen graphs (inductive) or stable. Here, we study these issues in a principled way and propose a provable solution, a class of GNN layers termed PEG with rigorous mathematical analysis. PEG uses separate channels to update the original node features and positional features. PEG imposes permutation equivariance w.r.t. the original node features and rotation equivariance w.r.t. the positional features simultaneously. Extensive link prediction experiments over 8 real-world networks demonstrate the advantages of PEG in generalization and scalability. Code is available at https://github.com/Graph-COM/PEG. | Accept (Poster) |
ICLR.cc/2022/Conference | Improving Out-of-Distribution Robustness via Selective Augmentation | Machine learning algorithms typically assume that training and test examples are drawn from the same distribution. However, distribution shifts is a common problem in real-world applications and can cause models to perform dramatically worse at test time. In this paper, we specifically consider the problems of domain shifts and subpopulation shifts, where learning invariant representations by aligning domain-specific representations or balancing the risks across domains with regularizers are popular solutions. However, designing regularizers that are suitable for diverse real-world datasets is challenging. Instead, we shed new light on addressing distribution shifts by directly eliminating domain-related spurious correlations with augmentation, leading to a simple technique based on mixup, called LISA (Learning Invariant Representations via Selective Augmentation). LISA selectively interpolates samples either with the same labels but different domains or with the same domain but different labels. Empirically, we study the effectiveness of LISA on nine benchmarks ranging from subpopulation shifts to domain shifts. The results indicate that LISA consistently outperforms other state-of-the-art methods with superior invariant representations. The empirical findings are further strengthened by our theoretical analysis. | Reject |
ICLR.cc/2019/Conference | Robustness May Be at Odds with Accuracy | We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization.
Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists even in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed in practice. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the features learned by robust models tend to align better with salient data characteristics and human perception. | Accept (Poster) |
ICLR.cc/2018/Conference | Connectivity Learning in Multi-Branch Networks | While much of the work in the design of convolutional networks over the last five years has revolved around the empirical investigation of the importance of depth, filter sizes, and number of feature channels, recent studies have shown that branching, i.e., splitting the computation along parallel but distinct threads and then aggregating their outputs, represents a new promising dimension for significant improvements in performance. To combat the complexity of design choices in multi-branch architectures, prior work has adopted simple strategies, such as a fixed branching factor, the same input being fed to all parallel branches, and an additive combination of the outputs produced by all branches at aggregation points.
In this work we remove these predefined choices and propose an algorithm to learn the connections between branches in the network. Instead of being chosen a priori by the human designer, the multi-branch connectivity is learned simultaneously with the weights of the network by optimizing a single loss function defined with respect to the end task. We demonstrate our approach on the problem of multi-class image classification using four different datasets where it yields consistently higher accuracy compared to the state-of-the-art ``ResNeXt'' multi-branch network given the same learning capacity. | Reject |
ICLR.cc/2020/Conference | Self-Attentional Credit Assignment for Transfer in Reinforcement Learning | The ability to transfer knowledge to novel environments and tasks is a sensible desiderata for general learning agents. Despite the apparent promises, transfer in RL is still an open and little exploited research area. In this paper, we take a brand-new perspective about transfer: we suggest that the ability to assign credit unveils structural invariants in the tasks that can be transferred to make RL more sample efficient. Our main contribution is Secret, a novel approach to transfer learning for RL that uses a backward-view credit assignment mechanism based on a self-attentive architecture. Two aspects are key to its generality: it learns to assign credit as a separate offline supervised process and exclusively modifies the reward function. Consequently, it can be supplemented by transfer methods that do not modify the reward function and it can be plugged on top of any RL algorithm. | Reject |
ICLR.cc/2021/Conference | Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds | 6D robotic grasping beyond top-down bin-picking scenarios is a challenging task. Previous solutions based on 6D grasp synthesis with robot motion planning usually operate in an open-loop setting without considering perception feedback and dynamics and contacts of objects, which makes them sensitive to grasp synthesis errors. In this work, we propose a novel method for learning closed-loop control policies for 6D robotic grasping using point clouds from an egocentric camera. We combine imitation learning and reinforcement learning in order to grasp unseen objects and handle the continuous 6D action space, where expert demonstrations are obtained from a joint motion and grasp planner. We introduce a goal-auxiliary actor-critic algorithm, which uses grasping goal prediction as an auxiliary task to facilitate policy learning. The supervision on grasping goals can be obtained from the expert planner for known objects or from hindsight goals for unknown objects. Overall, our learned closed-loop policy achieves over $90\%$ success rates on grasping various ShapeNet objects and YCB objects in simulation. The policy also transfers well to the real world with only one failure among grasping of ten different unseen objects in the presence of perception noises. | Reject |
ICLR.cc/2023/Conference | HiT-MDP: Learning the SMDP option framework on MDPs with Hidden Temporal Embeddings | The standard option framework is developed on the Semi-Markov Decision Process (SMDP) which is unstable to optimize and sample inefficient. To this end, we propose the Hidden Temporal MDP (HiT-MDP) and prove that the option-induced HiT-MDP is homomorphic equivalent to the option-induced SMDP. A novel transformer-based framework is introduced to learn options' embedding vectors (rather than conventional option tuples) on HiT-MDPs. We then derive a stable and sample efficient option discovering method under the maximum-entropy policy gradient framework. Extensive experiments on challenging Mujoco environments demonstrate HiT-MDP's efficiency and effectiveness: under widely used configurations, HiT-MDP achieves competitive, if not better, performance compared to the state-of-the-art baselines on all finite horizon and transfer learning environments. Moreover, HiT-MDP significantly outperforms all baselines on infinite horizon environments while exhibiting smaller variance, faster convergence, and better interpretability. Our work potentially sheds light on the theoretical ground of extending the option framework into a large-scale foundation model. | Accept: poster |
ICLR.cc/2022/Conference | Deep Classifiers with Label Noise Modeling and Distance Awareness | Uncertainty estimation in deep learning has recently emerged as a crucial area of interest to advance reliability and robustness in safety-critical applications. While there have been many proposed methods that either focus on distance-aware model uncertainties for out-of-distribution detection or on input-dependent label uncertainties for in-distribution calibration, both of these types of uncertainty are often necessary. In this work, we propose the HetSNGP method for jointly modeling the model and data uncertainty. We show that our proposed model affords a favorable combination between these two complementary types of uncertainty and thus outperforms the baseline methods on some challenging out-of-distribution datasets, including CIFAR-100C, Imagenet-C, and Imagenet-A. Moreover, we propose HetSNGP Ensemble, an ensembled version of our method which adds an additional type of uncertainty and also outperforms other ensemble baselines. | Reject |
ICLR.cc/2023/Conference | Eigenvalue Initialisation and Regularisation for Koopman Autoencoders | Regularising the parameter matrices of neural networks is ubiquitous in training deep models. Typical regularisation approaches suggest initialising weights using small random values, and to penalise weights to promote sparsity. However, these widely used techniques may be less effective in certain scenarios. Here, we study the Koopman autoencoder model which includes an encoder, a Koopman operator layer, and a decoder. These models have been designed and dedicated to tackle physics-related problems with interpretable dynamics and an ability to incorporate physics-related constraints. However, the majority of existing work employs standard regularisation practices. In our work, we take a step toward augmenting Koopman autoencoders with initialisation and penalty schemes tailored for physics-related settings. Specifically, we propose the "eigeninit" initialisation scheme that samples initial Koopman operators from specific eigenvalue distributions. In addition, we suggest the "eigenloss" penalty scheme that penalises the eigenvalues of the Koopman operator during training. We demonstrate the utility of these schemes on two synthetic data sets: a driven pendulum and flow past a cylinder; and two real-world problems: ocean surface temperatures and cyclone wind fields. We find on these datasets that eigenloss and eigeninit improves the convergence rate by a factor of 2 to 5, and that they reduce the cumulative long-term prediction error by up to a factor of 2.5. Such a finding points to the utility of incorporating similar schemes as an inductive bias in other physics-related deep learning approaches. | Reject |
ICLR.cc/2020/Conference | Attention over Phrases | How to represent the sentence ``That's the last straw for her''? The answer of the self-attention is a weighted sum of each individual words, i.e. $$semantics=\alpha_1Emb(\text{That})+\alpha_2Emb(\text{'s})+\cdots+\alpha_nEmb(\text{her})$$. But the weighted sum of ``That's'', ``the'', ``last'', ``straw'' can hardly represent the semantics of the phrase. We argue that the phrases play an important role in attention.
If we combine some words into phrases, a more reasonable representation with compositions is
$$semantics=\alpha_1Emb(\text{That's})+Emb_2(\text{the last straw})+\alpha_3Emb(\text{for})+\alpha_4Emb(\text{her})$$.
While recent studies prefer to use the attention mechanism to represent the natural language, few noticed the word compositions. In this paper, we study the problem of representing such compositional attentions in phrases. In this paper, we proposed a new attention architecture called HyperTransformer. Besides representing the words of the sentence, we introduce hypernodes to represent the candidate phrases in attention.
HyperTransformer has two phases. The first phase is used to attend over all word/phrase pairs, which is similar to the standard Transformer. The second phase is used to represent the inductive bias within each phrase. Specially, we incorporate the non-linear attention in the second phase. The non-linearity represents the the semantic mutations in phrases. The experimental performance has been greatly improved. In WMT16 English-German translation task, the BLEU increases from 20.90 (by Transformer) to 34.61 (by HyperTransformer). | Reject |
ICLR.cc/2021/Conference | Regularized Inverse Reinforcement Learning | Inverse Reinforcement Learning (IRL) aims to facilitate a learner’s ability to imitate expert behavior by acquiring reward functions that explain the expert’s decisions. Regularized IRLapplies strongly convex regularizers to the learner’s policy in order to avoid the expert’s behavior being rationalized by arbitrary constant rewards, also known as degenerate solutions. We propose tractable solutions, and practical methods to obtain them, for regularized IRL. Current methods are restricted to the maximum-entropy IRL framework, limiting them to Shannon-entropy regularizers, as well as proposing solutions that are intractable in practice. We present theoretical backing for our proposed IRL method’s applicability to both discrete and continuous controls, empirically validating our performance on a variety of tasks. | Accept (Spotlight) |
ICLR.cc/2018/Conference | GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders | Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks only, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sidestep hurdles associated with linearization of such discrete structures by having a decoder output a probabilistic fully-connected graph of a predefined maximum size directly at once. Our method is formulated as a variational autoencoder. We evaluate on the challenging task of conditional molecule generation. | Reject |
ICLR.cc/2019/Conference | Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning | Exploration in environments with sparse rewards is a key challenge for reinforcement learning. How do we design agents with generic inductive biases so that they can explore in a consistent manner instead of just using local exploration schemes like epsilon-greedy? We propose an unsupervised reinforcement learning agent which learns a discrete pixel grouping model that preserves spatial geometry of the sensors and implicitly of the environment as well. We use this representation to derive geometric intrinsic reward functions, like centroid coordinates and area, and learn policies to control each one of them with off-policy learning. These policies form a basis set of behaviors (options) which allows us explore in a consistent way and use them in a hierarchical reinforcement learning setup to solve for extrinsically defined rewards. We show that our approach can scale to a variety of domains with competitive performance, including navigation in 3D environments and Atari games with sparse rewards. | Reject |
ICLR.cc/2022/Conference | Understanding and Improving Graph Injection Attack by Promoting Unnoticeability | Recently Graph Injection Attack (GIA) emerges as a practical attack scenario on Graph Neural Networks (GNNs), where the adversary can merely inject few malicious nodes instead of modifying existing nodes or edges, i.e., Graph Modification Attack (GMA). Although GIA has achieved promising results, little is known about why it is successful and whether there is any pitfall behind the success. To understand the power of GIA, we compare it with GMA and find that GIA can be provably more harmful than GMA due to its relatively high flexibility. However, the high flexibility will also lead to great damage to the homophily distribution of the original graph, i.e., similarity among neighbors. Consequently, the threats of GIA can be easily alleviated or even prevented by homophily-based defenses designed to recover the original homophily. To mitigate the issue, we introduce a novel constraint – homophily unnoticeability that enforces GIA to preserve the homophily, and propose Harmonious Adversarial Objective (HAO) to instantiate it. Extensive experiments verify that GIA with HAO can break homophily-based defenses and outperform previous GIA attacks by a significant margin. We believe our methods can serve for a more reliable evaluation of the robustness of GNNs. | Accept (Poster) |
ICLR.cc/2018/Conference | Unbiased scalable softmax optimization | Recent neural network and language models have begun to rely on softmax distributions with an extremely large number of categories. In this context calculating the softmax normalizing constant is prohibitively expensive. This has spurred a growing literature of efficiently computable but biased estimates of the softmax. In this paper we present the first two unbiased algorithms for maximizing the softmax likelihood whose work per iteration is independent of the number of classes and datapoints (and does not require extra work at the end of each epoch). We compare our unbiased methods' empirical performance to the state-of-the-art on seven real world datasets, where they comprehensively outperform all competitors. | Reject |
ICLR.cc/2023/Conference | Active Learning based Structural Inference | In this paper, we propose an active-learning based framework, Active Learning based Structural Inference (ALaSI), to infer the existence of directed connections from observed agents' states over a time period in a dynamical system. With the help of deep active learning, ALaSI is competent in learning the representation of connections with relatively small pool of prior knowledge. Moreover, based on information theory, we propose inter- and out-of-scope message learning pipelines, which are remarkably beneficial to the structural inference for large dynamical systems. We evaluate ALaSI on various large datasets including simulated systems and real-world networks, to demonstrate that ALaSI is able to precisely infer the existence of connections in these systems under either supervised learning or unsupervised learning, with better performance than baseline methods. | Reject |
ICLR.cc/2022/Conference | FILM: Following Instructions in Language with Modular Methods | Recent methods for embodied instruction following are typically trained end-to-end using imitation learning. This often requires the use of expert trajectories and low-level language instructions. Such approaches assume that neural states will integrate multimodal semantics to perform state tracking, building spatial memory, exploration, and long-term planning. In contrast, we propose a modular method with structured representations that (1) builds a semantic map of the scene and (2) performs exploration with a semantic search policy, to achieve the natural language goal. Our modular method achieves SOTA performance (24.46 %) with a substantial (8.17 % absolute) gap from previous work while using less data by eschewing both expert trajectories and low-level instructions. Leveraging low-level language, however, can further increase our performance (26.49 %). Our findings suggest that an explicit spatial memory and a semantic search policy can provide a stronger and more general representation for state-tracking and guidance, even in the absence of expert trajectories or low-level instructions. | Accept (Poster) |
ICLR.cc/2022/Conference | Task-Agnostic Graph Neural Explanations | Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph structured data. Due to their broad applications, there is an increasing need to develop tools to explain how GNNs make decisions given graph structured data. Existing learning-based GNN explanation approaches are task-specific in training and hence suffer from crucial drawbacks. Specifically, they are incapable of producing explanations for a multitask prediction model with a single explainer. They are also unable to provide explanations in cases where the GNN is trained in a self-supervised manner, and the resulting representations are used in future down-stream tasks. To address these limitations, we propose a Task-Agnostic Graph Neural Explainer (TAGE) trained under self-supervision without knowledge about downstream tasks. TAGE enables the explanation of GNN embedding models without downstream tasks and allows efficient explanation of multitask models. Our extensive experiments show that TAGE can significantly speed up the explanation efficiency while achieving explanation quality as good as or even better than current state-of-the-art GNN explanation approaches. | Reject |
ICLR.cc/2020/Conference | HiLLoC: lossless image compression with hierarchical latent variable models | We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model. We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale the VAE-based 'Bits-Back with ANS' algorithm for lossless compression to large color photographs, and achieving state of the art for compression of full size ImageNet images. We release Craystack, an open source library for convenient prototyping of lossless compression using probabilistic models, along with full implementations of all of our compression results. | Accept (Poster) |
ICLR.cc/2023/Conference | A Simulation-based Framework for Robust Federated Learning to Training-time Attacks | Well-known robust aggregation schemes in federated learning (FL) are shown to be vulnerable to an informed adversary who can tailor training-time attacks [Fang et al., Xie et al.]. We frame robust distributed learning problem as a game between a server and an adversary that is able to optimize strong training-time attacks. We introduce RobustTailor, a simulation-based framework that prevents the adversary from being omniscient. The simulated game we propose enjoys theoretical guarantees through a regret analysis. RobustTailor improves robustness to training-time attacks significantly while preserving almost the same privacy guarantees as standard robust aggregation schemes in FL. Empirical results under challenging attacks show that RobustTailor performs similar to an upper bound with perfect knowledge of honest clients. | Reject |
ICLR.cc/2020/Conference | A General Upper Bound for Unsupervised Domain Adaptation | In this work, we present a novel upper bound of target error to address the problem for unsupervised domain adaptation. Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks. Furthermore, Ben-David et al. (2010) provide an upper bound for target error when transferring the knowledge, which can be summarized as minimizing the source error and distance between marginal distributions simultaneously. However, common methods based on the theory usually ignore the joint error such that samples from different classes might be mixed together when matching marginal distribution. And in such case, no matter how we minimize the marginal discrepancy, the target error is not bounded due to an increasing joint error. To address this problem, we propose a general upper bound taking joint error into account, such that the undesirable case can be properly penalized. In addition, we utilize constrained hypothesis space to further formalize a tighter bound as well as a novel cross margin discrepancy to measure the dissimilarity between hypotheses which alleviates instability during adversarial learning. Extensive empirical evidence shows that our proposal outperforms related approaches in image classification error rates on standard domain adaptation benchmarks. | Reject |
ICLR.cc/2020/Conference | Corpus Based Amharic Sentiment Lexicon Generation | Sentiment classification is an active research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews. To employ rule based sentiment classification, we require sentiment lexicons. However, manual construction of sentiment lexicon is time consuming and costly for resource-limited languages. To bypass manual development time and costs, we tried to build Amharic Sentiment Lexicons relying on corpus based approach. The intention of this approach is to handle sentiment terms specific to Amharic language from Amharic Corpus. Small set of seed terms are manually prepared from three parts of speech such as noun, adjective and verb. We developed algorithms for constructing Amharic sentiment lexicons automatically from Amharic news corpus. Corpus based approach is proposed relying on the word co-occurrence distributional embedding including frequency based embedding (i.e. Positive Point-wise Mutual Information PPMI). First we build word-context unigram frequency count matrix and transform it to point-wise mutual Information matrix. Using this matrix, we computed the cosine distance of mean vector of seed lists and each word in the corpus vocabulary. Based on the threshold value, the top closest words to the mean vector of seed list are added to the lexicon. Then the mean vector of the new sentiment seed list is updated and process is repeated until we get sufficient terms in the lexicon. Using PPMI with threshold value of 100 and 200, we got corpus based Amharic Sentiment lexicons of size 1811 and 3794 respectively by expanding 519 seeds. Finally, the lexicon generated in corpus based approach is evaluated.
| Reject |
ICLR.cc/2021/Conference | FTSO: Effective NAS via First Topology Second Operator | Existing one-shot neural architecture search (NAS) methods generally contain a giant supernet, which leads to heavy computational cost. Our method, named FTSO, separates the whole architecture search into two sub-steps. In the first step, we only search for the topology, and in the second step, we only search for the operators. FTSO not only reduces NAS’s search time from days to 0.68 seconds, but also significantly improves the accuracy. Specifically, our experiments on ImageNet show that within merely 18 seconds, FTSO can achieve 76.4% testing accuracy, 1.5% higher than the baseline, PC-DARTS. In addition, FTSO can reach 97.77% testing accuracy, 0.27% higher than the baseline, with 99.8% of search time saved on CIFAR10. | Reject |
ICLR.cc/2022/Conference | SparRL: Graph Sparsification via Deep Reinforcement Learning | Graph sparsification concerns data reduction where an edge-reduced graph of a similar structure is preferred. Existing methods are mostly sampling-based, which introduce high computation complexity in general and lack of flexibility for a different reduction objective. We present SparRL, the first general and effective reinforcement learning-based framework for graph sparsification. SparRL can easily adapt to different reduction goals and promise graph-size-independent complexity. Extensive experiments show that SparRL outperforms all prevailing sparsification methods in producing high-quality sparsified graphs concerning a variety of objectives. As graph representations are very versatile, SparRL carries the potential for a broad impact. | Reject |
ICLR.cc/2021/Conference | End-to-End Egospheric Spatial Memory | Spatial memory, or the ability to remember and recall specific locations and objects, is central to autonomous agents' ability to carry out tasks in real environments. However, most existing artificial memory modules are not very adept at storing spatial information. We propose a parameter-free module, Egospheric Spatial Memory (ESM), which encodes the memory in an ego-sphere around the agent, enabling expressive 3D representations. ESM can be trained end-to-end via either imitation or reinforcement learning, and improves both training efficiency and final performance against other memory baselines on both drone and manipulator visuomotor control tasks. The explicit egocentric geometry also enables us to seamlessly combine the learned controller with other non-learned modalities, such as local obstacle avoidance. We further show applications to semantic segmentation on the ScanNet dataset, where ESM naturally combines image-level and map-level inference modalities. Through our broad set of experiments, we show that ESM provides a general computation graph for embodied spatial reasoning, and the module forms a bridge between real-time mapping systems and differentiable memory architectures. Implementation at: https://github.com/ivy-dl/memory. | Accept (Poster) |
ICLR.cc/2023/Conference | Continual Learning with Soft-Masking of Parameter-Level Gradient Flow | Existing research on task incremental learning in continual learning has primarily focused on preventing catastrophic forgetting (CF). Several techniques have achieved learning with no CF. However, they attain it by letting each task monopolize a sub-network in a shared network, which seriously limits knowledge transfer (KT) and causes over-consumption of the network capacity, i.e., as more tasks are learned, the performance deteriorates. The goal of this paper is threefold: (1) overcoming CF, (2) encouraging KT, and (3) tackling the capacity problem. A novel and simple technique (called SPG) is proposed that soft-masks (partially blocks) parameter updating in training based on the importance of each parameter to old tasks. Each task still uses the full network, i.e., no monopoly of any part of the network by any task, which enables maximum KT and reduction of capacity usage. Extensive experiments demonstrate the effectiveness of SPG in achieving all three objectives. More notably, it attains significant transfer of knowledge not only among similar tasks (with shared knowledge) but also among dissimilar tasks (with little shared knowledge) while preventing CF. | Reject |
ICLR.cc/2019/Conference | Multi-Grained Entity Proposal Network for Named Entity Recognition | In this paper, we focus on a new Named Entity Recognition (NER) task, i.e., the Multi-grained NER task. This task aims to simultaneously detect both fine-grained and coarse-grained entities in sentences. Correspondingly, we develop a novel Multi-grained Entity Proposal Network (MGEPN). Different from traditional NER models which regard NER as a sequential labeling task, MGEPN provides a new method that proposes entity candidates in the Proposal Network and classifies entities into different categories in the Classification Network. All possible entity candidates including fine-grained ones and coarse-grained ones are proposed in the Proposal Network, which enables the MGEPN model to identify multi-grained entities. In order to better identify named entities and determine their categories, context information is utilized and transferred from the Proposal Network to the Classification Network during the learning process. A novel Entity-Context attention mechanism is also introduced to help the model focus on entity-related context information. Experiments show that our model can obtain state-of-the-art performance on two real-world datasets for both the Multi-grained NER task and the traditional NER task. | Reject |
ICLR.cc/2022/Conference | FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning | Federated learning (FL) is a privacy-aware collaborative learning paradigm that allows multiple parties to jointly train a machine learning model without sharing their private data. However, recent studies have shown that FL is vulnerable to weight poisoning attacks. In this paper, we propose a probabilistic discretization mechanism on the client side, which transforms the client's model weight into a vector that can only have two different values but still guarantees that the server obtains an unbiased estimation of the client's model weight. We theoretically analyze the utility, robustness, and convergence of our proposed discretization mechanism and empirically verify its superior robustness against various weight-based attacks under the cross-device FL setting. | Reject |
ICLR.cc/2023/Conference | Image as Set of Points |
What is an image, and how to extract latent features?
Convolutional Networks (ConvNets) consider an image as organized pixels in a rectangular shape and extract features via convolutional operation in a local region; Vision Transformers (ViTs) treat an image as a sequence of patches and extract features via attention mechanism in a global range. In this work, we introduce a straightforward and promising paradigm for visual representation, which is called Context Clusters. Context clusters (CoCs) view an image as a set of unorganized points and extract features via a simplified clustering algorithm. In detail, each point includes the raw feature (e.g., color) and positional information (e.g., coordinates), and a simplified clustering algorithm is employed to group and extract deep features hierarchically. Our CoCs are convolution- and attention-free, only relying on clustering algorithm for spatial interaction. Owing to the simple design, we show CoCs endow gratifying interpretability via the visualization of the clustering process.
Our CoCs aim at providing a new perspective on image and visual representation, which may enjoy broad applications in different domains and exhibit profound insights. Even though we are not targeting SOTA performance, COCs still achieve comparable or even better performance than ConvNets or ViTs on several benchmarks. | Accept: notable-top-5% |
ICLR.cc/2020/Conference | Automatically Learning Feature Crossing from Model Interpretation for Tabular Data | Automatically feature generation is a major topic of automated machine learning. Among various feature generation approaches, feature crossing, which takes cross-product of sparse features, is a promising way to effectively capture the interactions among categorical features in tabular data. Previous works on feature crossing try to search in the set of all the possible cross feature fields. This is obviously not efficient when the size of original feature fields is large. Meanwhile, some deep learning-based methods combines deep neural networks and various interaction components. However, due to the existing of Deep Neural Networks (DNN), only a few cross features can be explicitly generated by the interaction components. Recently, piece-wise interpretation of DNN has been widely studied, and the piece-wise interpretations are usually inconsistent in different samples. Inspired by this, we give a definition of interpretation inconsistency in DNN, and propose a novel method called CrossGO, which selects useful cross features according to the interpretation inconsistency. The whole process of learning feature crossing can be done via simply training a DNN model and a logistic regression (LR) model. CrossGO can generate compact candidate set of cross feature fields, and promote the efficiency of searching. Extensive experiments have been conducted on several real-world datasets. Cross features generated by CrossGO can empower a simple LR model achieving approximate or even better performances comparing with complex DNN models. | Reject |
ICLR.cc/2018/Conference | MGAN: Training Generative Adversarial Nets with Multiple Generators | We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ multiple generators, instead of using a single one as in the original GAN. The idea is simple, yet proven to be extremely effective at covering diverse data modes, easily overcoming the mode collapsing problem and delivering state-of-the-art results. A minimax formulation was able to establish among a classifier, a discriminator, and a set of generators in a similar spirit with GAN. Generators create samples that are intended to come from the same distribution as the training data, whilst the discriminator determines whether samples are true data or generated by generators, and the classifier specifies which generator a sample comes from. The distinguishing feature is that internal samples are created from multiple generators, and then one of them will be randomly selected as final output similar to the mechanism of a probabilistic mixture model. We term our method Mixture Generative Adversarial Nets (MGAN). We develop theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon divergence (JSD) between the mixture of generators’ distributions and the empirical data distribution is minimal, whilst the JSD among generators’ distributions is maximal, hence effectively avoiding the mode collapsing problem. By utilizing parameter sharing, our proposed model adds minimal computational cost to the standard GAN, and thus can also efficiently scale to large-scale datasets. We conduct extensive experiments on synthetic 2D data and natural image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior performance of our MGAN in achieving state-of-the-art Inception scores over latest baselines, generating diverse and appealing recognizable objects at different resolutions, and specializing in capturing different types of objects by the generators. | Accept (Poster) |
ICLR.cc/2022/Conference | LMSA: Low-relation Mutil-head Self-Attention Mechanism in Visual Transformer | The Transformer backbone network with the self-attention mechanism as the core has achieved great success in the field of natural language processing and computer vision. However, through the self-attention mechanism brings high performance, it also brings higher computational complexity compared to the classic visual feature extraction methods. To further reduce the complexity of self-attention mechanism and explore its lighter version in computer vision, in this paper, we design a novel lightweighted self-attention mechanism: Low-relation Mutil-head Self-Attention (LMSA), which is superior than the recent self-attention. Specifically, the proposed self-attention mechanism breaks the barrier of the dimensional consistency of the traditional self-attention mechanism, resulting in lower computational complexity and occupies less storage space. In addition, employing the new mechanism can release part of the computing consumption of the Transformer network and make the best use of it. Experimental results show that the dimensional consistency inside the traditional self-attention mechanism is unnecessary. In particular, using Swin as the backbone model for training, the accuracy in CIFAR-10 image classification task is improved by 0.43$\%$, in the meanwhile, the consumption of a single self-attention resource is reduced by 64.58$\%$, and the number of model parameters and model size are reduced by more than 15$\%$. By appropriately compressing the dimensions of the self-attention relationship variables, the Transformer network can be more efficient and even perform better. The results prompt us to rethink the reason why the self-attention mechanism works. | Reject |
ICLR.cc/2022/Conference | Learning to perceive objects by prediction | The representation of objects is the building block of higher-level concepts. Infants develop the notion of objects without supervision. The prediction error of future sensory input is likely the major teaching signal for infants. Inspired by this, we propose a new framework to extract object-centric representation from single 2D images by learning to predict future scenes in the presence of moving objects. We treat objects as latent causes whose function to an agent is to facilitate efficient prediction of the coherent motion of their parts in visual input. Distinct from previous object-centric models, our model learn to explicitly infer objects' location in 3D environment in addition to segmenting objects. Further, the network learns a latent code space where objects with the same geometric shape and texture/color frequently group together. The model requires no supervision or pre-training of any part of the network. We provide a new synthetic dataset with more complex textures on objects and background and found several previous models not based on predictive learning overly rely on clustering colors and lose specificity in object segmentation. Our work demonstrates a new approach for learning symbolic representation grounded in sensation and action. | Reject |
ICLR.cc/2019/Conference | Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data | Current generation of memory-augmented neural networks has limited scalability as they cannot efficiently process data that are too large to fit in the external memory storage. One example of this is lifelong learning scenario where the model receives unlimited length of data stream as an input which contains vast majority of uninformative entries. We tackle this problem by proposing a memory network fit for long-term lifelong learning scenario, which we refer to as Long-term Episodic Memory Networks (LEMN), that features a RNN-based retention agent that learns to replace less important memory entries based on the retention probability generated on each entry that is learned to identify data instances of generic importance relative to other memory entries, as well as its historical importance. Such learning of retention agent allows our long-term episodic memory network to retain memory entries of generic importance for a given task. We validate our model on a path-finding task as well as synthetic and real question answering tasks, on which our model achieves significant improvements over the memory augmented networks with rule-based memory scheduling as well as an RL-based baseline that does not consider relative or historical importance of the memory. | Reject |
ICLR.cc/2023/Conference | DynaMS: Dyanmic Margin Selection for Efficient Deep Learning | The great success of deep learning is largely driven by training over-parameterized models on massive datasets. To avoid excessive computation, extracting and training only on the most informative subset is drawing increasing attention. Nevertheless, it is still an open question how to select such a subset on which the model trained generalizes on par with the full data. In this paper, we propose dynamic margin selection (DynaMS). DynaMS leverages the distance from candidate samples to the classification boundary to construct the subset, and the subset is dynamically updated during model training. We show that DynaMS converges with large probability, and for the first time show both in theory and practice that dynamically updating the subset can result in better generalization over previous works. To reduce the additional computation incurred by the selection, a light parameter sharing proxy (PSP) is designed. PSP is able to faithfully evaluate instances with respect to the current model, which is necessary for dynamic selection. Extensive analysis and experiments demonstrate the superiority of the proposed approach in data selection against many state-of-the-art counterparts on benchmark datasets. | Accept: poster |
ICLR.cc/2023/Conference | A Simple Contrastive Learning Objective for Alleviating Neural Text Degeneration | The cross-entropy objective has proved to be an all-purpose training objective for autoregressive language models (LMs). However, without distinguishing problematic tokens, LMs trained using cross-entropy exhibit text degeneration problems. To address this, unlikelihood training has been proposed to reduce the probability of unlikely tokens predicted by LMs. But unlikelihood does not explicitly consider the relationship between the label tokens and unlikely token candidates, thus showing marginal improvements in degeneration. We propose a new contrastive token learning objective that inherits the advantages of cross-entropy and unlikelihood training and avoids their limitations. The key idea is to teach a LM to generate high probabilities for label tokens and low probabilities for negative candidates. Comprehensive experiments on language modeling and open-domain dialogue generation tasks show that the proposed contrastive token objective yields much less repetitive texts, with a higher generation quality than baseline approaches, achieving the new state-of-the-art performance on text degeneration. | Reject |
ICLR.cc/2019/Conference | Analyzing Federated Learning through an Adversarial Lens | Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at the server. In this work, we explore the threat of model poisoning attacks on federated learning initiated by a single, non-colluding malicious agent where the adversarial objective is to cause the model to misclassify a set of chosen inputs with high confidence. We explore a number of strategies to carry out this attack, starting with simple boosting of the malicious agent's update to overcome the effects of other agents' updates. To increase attack stealth, we propose an alternating minimization strategy, which alternately optimizes for the training loss and the adversarial objective. We follow up by using parameter estimation for the benign agents' updates to improve on attack success. Finally, we use a suite of interpretability techniques to generate visual explanations of model decisions for both benign and malicious models and show that the explanations are nearly visually indistinguishable. Our results indicate that even a highly constrained adversary can carry out model poisoning attacks while simultaneously maintaining stealth, thus highlighting the vulnerability of the federated learning setting and the need to develop effective defense strategies. | Reject |
ICLR.cc/2022/Conference | Targeted Environment Design from Offline Data | In reinforcement learning (RL) the use of simulators is ubiquitous, allowing cheaper and safer agent training than training directly in the real target environment. However, this approach relies on the simulator being a sufficiently accurate reflection of the target environment, which is difficult to achieve in practice, resulting in the need to bridge sim2real gap. Accordingly, recent methods have proposed an alternative paradigm, utilizing offline datasets from the target environment to train an agent, avoiding online access to either the target or any simulated environment but leading to poor generalization outside the support of the offline data. We propose to combine the two paradigms: offline datasets and synthetic simulators, to reduce the sim2real gap by using limited offline data to train realistic simulators. We formalize our approach as offline targeted environment design(OTED), which automatically learns a distribution over simulator parameters to match a provided offline dataset, and then uses the learned simulator to train an RL agent in standard online fashion. We derive an objective for learning the simulator parameters which corresponds to minimizing a divergence between the target offline dataset and the state-action distribution induced by the simulator. We evaluate our method on standard offlineRL benchmarks and show that it learns using as few as 5 demonstrations, and yields up to 17 times higher score compared to strong existing offline RL, behavior cloning (BC), and domain randomization baseline, thus successfully leveraging both offline datasets and simulators for better RL | Reject |
ICLR.cc/2022/Conference | Stabilized Self-training with Negative Sampling on Few-labeled Graph Data | Graph neural networks (GNNs) are designed for semi-supervised node classification on graphs where only a small subset of nodes have class labels. However, under extreme cases when very few labels are available (e.g., 1 labeled node per class), GNNs suffer from severe result quality degradation.
Specifically, we observe that existing GNNs suffer from unstable training process on few-labeled graph data, resulting to inferior performance on node classification. Therefore, we propose an effective framework, Stabilized self-training with Negative sampling (SN), which is applicable to existing GNNs to stabilize the training process and enhance the training data, and consequently, boost classification accuracy on graphs with few labeled data. In experiments, we apply our SN framework to two existing GNN base models (GCN and DAGNN) to get SNGCN and SNDAGNN, and evaluate the two methods against 13 existing solutions over 4 benchmarking datasets. Extensive experiments show that the proposed SN framework is highly effective compared with existing solutions, especially under settings with very few labeled data. In particular, on a benchmark dataset Cora with only 1 labeled node per class, while GCN only has 44.6% accuracy, SNGCN achieves 62.5% accuracy, improving GCN by 17.9%; SNDAGNN has accuracy 66.4%, improving that of the base model DAGNN (59.8%) by 6.6%. | Reject |
ICLR.cc/2023/Conference | A Higher Precision Algorithm for Computing the $1$-Wasserstein Distance | We consider the problem of computing the $1$-Wasserstein distance $\mathcal{W}(\mu,\nu)$ between two $d$-dimensional discrete distributions $\mu$ and $\nu$ whose support lie within the unit hypercube. There are several algorithms that estimate $\mathcal{W}(\mu,\nu)$ within an additive error of $\varepsilon$. However, when $\mathcal{W}(\mu,\nu)$ is small, the additive error $\varepsilon$ dominates, leading to noisy results. Consider any additive approximation algorithm with execution time $T(n,\varepsilon)$. We propose an algorithm that runs in $O(T(n,\varepsilon/d) \log n)$ time and boosts the accuracy of estimating $\mathcal{W}(\mu,\nu)$ from $\varepsilon$ to an expected additive error of $\min\{\varepsilon, (d\log_{\sqrt{d}/\varepsilon} n)\mathcal{W}(\mu,\nu)\}$. For the special case where every point in the support of $\mu$ and $\nu$ has a mass of $1/n$ (also called the Euclidean Bipartite Matching problem), we describe an algorithm to boost the accuracy of any additive approximation algorithm from $\varepsilon$ to an expected additive error of $\min\{\varepsilon, (d\log\log n)\mathcal{W}(\mu,\nu)\}$ in $O(T(n, \varepsilon/d)\log\log n)$ time. | Accept: notable-top-25% |
ICLR.cc/2019/Conference | DISTRIBUTIONAL CONCAVITY REGULARIZATION FOR GANS | We propose Distributional Concavity (DC) regularization for Generative Adversarial Networks (GANs), a functional gradient-based method that promotes the entropy of the generator distribution and works against mode collapse.
Our DC regularization is an easy-to-implement method that can be used in combination with the current state of the art methods like Spectral Normalization and Wasserstein GAN with gradient penalty to further improve the performance.
We will not only show that our DC regularization can achieve highly competitive results on ILSVRC2012 and CIFAR datasets in terms of Inception score and Fr\'echet inception distance, but also provide a mathematical guarantee that our method can always increase the entropy of the generator distribution. We will also show an intimate theoretical connection between our method and the theory of optimal transport. | Accept (Poster) |
ICLR.cc/2020/Conference | Simple and Effective Stochastic Neural Networks | Stochastic neural networks (SNNs) are currently topical, with several paradigms being actively investigated including dropout, Bayesian neural networks, variational information bottleneck (VIB) and noise regularized learning. These neural network variants impact several major considerations, including generalization, network compression, and robustness against adversarial attack and label noise. However, many existing networks are complicated and expensive to train, and/or only address one or two of these practical considerations. In this paper we propose a simple and effective stochastic neural network (SE-SNN) architecture for discriminative learning by directly modeling activation uncertainty and encouraging high activation variability. Compared to existing SNNs, our SE-SNN is simpler to implement and faster to train, and produces state of the art results on network compression by pruning, adversarial defense and learning with label noise. | Reject |
ICLR.cc/2022/Conference | Knowledge Infused Decoding | Pre-trained language models (LMs) have been shown to memorize a substantial amount of knowledge from the pre-training corpora; however, they are still limited in recalling factually correct knowledge given a certain context. Hence. they tend to suffer from counterfactual or hallucinatory generation when used in knowledge-intensive natural language generation (NLG) tasks. Recent remedies to this problem focus on modifying either the pre-training or task fine-tuning objectives to incorporate knowledge, which normally require additional costly training or architecture modification of LMs for practical applications.
We present Knowledge Infused Decoding (KID)---a novel decoding algorithm for generative LMs, which dynamically infuses external knowledge into each step of the LM decoding. Specifically, we maintain a local knowledge memory based on the current context, interacting with a dynamically created external knowledge trie, and continuously update the local memory as a knowledge-aware constraint to guide decoding via reinforcement learning. On six diverse knowledge-intensive NLG tasks, task-agnostic LMs (e.g., GPT-2 and BART) armed with KID outperform many task-optimized state-of-the-art models, and show particularly strong performance in few-shot scenarios over seven related knowledge-infusion techniques. Human evaluation confirms KID's ability to generate more relevant and factual language for the input context when compared with multiple baselines. Finally, KID also alleviates exposure bias and provides stable generation quality when generating longer sequences. | Accept (Poster) |
ICLR.cc/2022/Conference | Fast Deterministic Stackelberg Actor-Critic | Most advanced Actor-Critic (AC) approaches update the actor and critic concurrently through (stochastic) Gradient Descents (GD), which may be trapped into bad local optimality due to the instability of these simultaneous updating schemes.
Stackelberg AC learning scheme alleviates these limitations by adding a compensated indirect gradient terms to the GD. However, the indirect gradient terms are time-consuming to calculate, and the convergence rate is also relatively slow. To alleviates these challenges, we find that in the Deterministic Policy Gradient family, by removing the terms that contain Hessian matrices and adopting the block diagonal approximation technique to approximate the remaining inverse matrices, we can construct an approximated Stackelberg AC learning scheme that is easy to compute and fast to converge. Experiments reveal that ours outperform SOTAs in terms of average returns under acceptable training time. | Reject |
ICLR.cc/2018/Conference | Generating Natural Adversarial Examples | Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, these malicious perturbations are often unnatural, not semantically meaningful, and not applicable to complicated domains such as language. In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks. We present generated adversaries to demonstrate the potential of the proposed approach for black-box classifiers for a wide range of applications such as image classification, textual entailment, and machine translation. We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers. | Accept (Poster) |
ICLR.cc/2019/Conference | ON THE USE OF CONVOLUTIONAL AUTO-ENCODER FOR INCREMENTAL CLASSIFIER LEARNING IN CONTEXT AWARE ADVERTISEMENT | Context Aware Advertisement (CAA) is a type of advertisement
appearing on websites or mobile apps. The advertisement is targeted
on specific group of users and/or the content displayed on the
websites or apps. This paper focuses on classifying images displayed
on the websites by incremental learning classifier with Deep
Convolutional Neural Network (DCNN) especially for Context Aware
Advertisement (CAA) framework. Incrementally learning new knowledge
with DCNN leads to catastrophic forgetting as previously stored
information is replaced with new information. To prevent
catastrophic forgetting, part of previously learned knowledge should
be stored for the life time of incremental classifier. Storing
information for life time involves privacy and legal concerns
especially in context aware advertising framework. Here, we propose
an incremental classifier learning method which addresses privacy
and legal concerns while taking care of catastrophic forgetting
problem. We conduct experiments on different datasets including
CIFAR-100. Experimental results show that proposed system achieves
relatively high performance compared to the state-of-the-art
incremental learning methods. | Reject |
ICLR.cc/2023/Conference | Revisiting the Assumption of Latent Separability for Backdoor Defenses | Recent studies revealed that deep learning is susceptible to backdoor poisoning attacks. An adversary can embed a hidden backdoor into a model to manipulate its predictions by only modifying a few training data, without controlling the training process. Currently, a tangible signature has been widely observed across a diverse set of backdoor poisoning attacks --- models trained on a poisoned dataset tend to learn separable latent representations for poison and clean samples. This latent separation is so pervasive that a family of backdoor defenses directly take it as a default assumption (dubbed latent separability assumption), based on which to identify poison samples via cluster analysis in the latent space. An intriguing question consequently follows: is the latent separation unavoidable for backdoor poisoning attacks? This question is central to understanding whether the assumption of latent separability provides a reliable foundation for defending against backdoor poisoning attacks. In this paper, we design adaptive backdoor poisoning attacks to present counter-examples against this assumption. Our methods include two key components: (1) a set of trigger-planted samples correctly labeled to their semantic classes (other than the target class) that can regularize backdoor learning; (2) asymmetric trigger planting strategies that help to boost attack success rate (ASR) as well as to diversify latent representations of poison samples. Extensive experiments on benchmark datasets verify the effectiveness of our adaptive attacks in bypassing existing latent separation based backdoor defenses. Moreover, our attacks still maintain a high attack success rate with negligible clean accuracy drop. Our studies call for defense designers to take caution when leveraging latent separation as an assumption in their defenses. Our codes are available at https://github.com/Unispac/Circumventing-Backdoor-Defenses. | Accept: poster |
ICLR.cc/2020/Conference | Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling | We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as the \textit{test distribution}) but is still defined on the same measurable space as $P$ and dominates it. In the unrealistic case where the likelihood ratio $\Phi(z)=dP/dP'(z)$ is known, one may straightforwardly extends the Empirical Risk Minimization (ERM) approach to this specific \textit{transfer learning} setup using the same idea as that behind Importance Sampling, by minimizing a weighted version of the empirical risk functional computed from the 'biased' training data $Z'_i$ with weights $\Phi(Z'_i)$. Although the \textit{importance function} $\Phi(z)$ is generally unknown in practice, we show that, in various situations frequently encountered in practice, it takes a simple form and can be directly estimated from the $Z'_i$'s and some auxiliary information on the statistical population $P$. By means of linearization techniques, we then prove that the generalization capacity of the approach aforementioned is preserved when plugging the resulting estimates of the $\Phi(Z'_i)$'s into the weighted empirical risk. Beyond these theoretical guarantees, numerical results provide strong empirical evidence of the relevance of the approach promoted in this article. | Reject |
ICLR.cc/2019/Conference | Deep Reinforcement Learning of Universal Policies with Diverse Environment Summaries | Deep reinforcement learning has enabled robots to complete complex tasks in simulation. However, the resulting policies do not transfer to real robots due to model errors in the simulator. One solution is to randomize the simulation environment, so that the resulting, trained policy achieves high performance in expectation over a variety of configurations that could represent the real-world. However, the distribution over simulator configurations must be carefully selected to represent the relevant dynamic modes of the system, as otherwise it can be unlikely to sample challenging configurations frequently enough. Moreover, the ideal distribution to improve the policy changes as the policy (un)learns to solve tasks in certain configurations. In this paper, we propose to use an inexpensive, kernel-based summarization method method that identifies configurations that lead to diverse behaviors. Since failure modes for the given task are naturally diverse, the policy trains on a mixture of representative and challenging configurations, which leads to more robust policies. In experiments, we show that the proposed method achieves the same performance as domain randomization in simple cases, but performs better when domain randomization does not lead to diverse dynamic modes. | Reject |
ICLR.cc/2020/Conference | Antifragile and Robust Heteroscedastic Bayesian Optimisation | Bayesian Optimisation is an important decision-making tool for high-stakes applications in drug discovery and materials design. An oft-overlooked modelling consideration however is the representation of input-dependent or heteroscedastic aleatoric uncertainty. The cost of misrepresenting this uncertainty as being homoscedastic could be high in drug discovery applications where neglecting heteroscedasticity in high throughput virtual screening could lead to a failed drug discovery program. In this paper, we propose a heteroscedastic Bayesian Optimisation scheme which both represents and optimises aleatoric noise in the suggestions. We consider cases such as drug discovery where we would like to minimise or be robust to aleatoric uncertainty but also applications such as materials discovery where it may be beneficial to maximise or be antifragile to aleatoric uncertainty. Our scheme features a heteroscedastic Gaussian Process (GP) as the surrogate model in conjunction with two acquisition heuristics. First, we extend the augmented expected improvement (AEI) heuristic to the heteroscedastic setting and second, we introduce a new acquisition function, aleatoric-penalised expected improvement (ANPEI) based on a simple scalarisation of the performance and noise objective. Both methods are capable of penalising or promoting aleatoric noise in the suggestions and yield improved performance relative to a naive implementation of homoscedastic Bayesian Optimisation on toy problems as well as a real-world optimisation problem. | Reject |
ICLR.cc/2022/Conference | Localized Persistent Homologies for more Effective Deep Learning | Persistent Homologies have been successfully used to increase the performance of deep networks trained to detect curvilinear structures and to improve the topological quality of the results. However, existing methods are very global and ignore the location of topological features. In this paper, we introduce an approach that relies on a new filtration function to account for location during network training. We demonstrate experimentally on 2D images of roads and 3D image stacks of neural processes that networks trained in this manner are better at recovering the topology of the curvilinear structures they extract. | Reject |
ICLR.cc/2023/Conference | Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel | Studying the loss landscapes of neural networks is critical to identifying generalizations and avoiding overconfident predictions. Flatness, which measures the perturbation resilience of pre-trained parameters for loss values, is widely acknowledged as an essential predictor of generalization. While the concept of flatness has been formalized as a PAC-Bayes bound, it has been observed that the generalization bounds can vary arbitrarily depending on the scale of the model parameters. Despite previous attempts to address this issue, generalization bounds remain vulnerable to function-preserving scaling transformations or are limited to impractical network structures. In this paper, we introduce new PAC-Bayes prior and posterior distributions invariant to scaling transformations, achieved through the \textit{decomposition of perturbations into scale and connectivity components}. In this way, this approach expands the range of networks to which the resulting generalization bound can be applied, including those with practical transformations such as weight decay with batch normalization. Moreover, we demonstrate that scale-dependency issues of flatness can adversely affect the uncertainty calibration of Laplace approximation, and we propose a solution using our invariant posterior. Our proposed invariant posterior allows for effective measurement of flatness and calibration with low complexity while remaining invariant to practical parameter transformations, also applying it as a reliable predictor of neural network generalization. | Accept: notable-top-25% |
ICLR.cc/2022/Conference | Iterative Decoding for Compositional Generalization in Transformers | Deep learning models do well at generalizing to in-distribution data but struggle to generalize compositionally, i.e., to combine a set of learned primitives to solve more complex tasks. In particular, in sequence-to-sequence (seq2seq) learning, transformers are often unable to predict even marginally longer examples than those seen during training. This paper introduces iterative decoding, an alternative to seq2seq learning that (i) improves transformer compositional generalization and (ii) evidences that, in general, seq2seq transformers do not learn iterations that are not unrolled. Inspired by the idea of compositionality---that complex tasks can be solved by composing basic primitives---training examples are broken down into a sequence of intermediate steps that the transformer then learns iteratively. At inference time, the intermediate outputs are fed back to the transformer as intermediate inputs until an end-of-iteration token is predicted. Through numerical experiments, we show that transfomers trained via iterative decoding outperform their seq2seq counterparts on the PCFG dataset, and solve the problem of calculating Cartesian products between vectors longer than those seen during training with 100% accuracy, a task at which seq2seq models have been shown to fail. We also illustrate a limitation of iterative decoding, specifically, that it can make sorting harder to learn on the CFQ dataset. | Reject |
ICLR.cc/2023/Conference | ORCA: Interpreting Prompted Language Models via Locating Supporting Evidence in the Ocean of Pretraining Data | Prompting large pretrained language models leads to strong performance in a variety of downstream tasks. However, it is still unclear from where the model learns task-specific knowledge, especially in zero-shot setups. In this work, we propose a novel method ORCA to identify evidence of the model's task-specific competence in prompt-based learning. Through an instance attribution approach to model interpretability, by iteratively using gradient information related to the downstream task, ORCA locates a very small subset of pretraining data that directly supports the model's predictions in a given task; we call this subset supporting data evidence. We show that supporting data evidence offers new insights about the prompted language models. For example, in the tasks of sentiment analysis and textual entailment, BERT shows a substantial reliance on BookCorpus---the smaller corpus of BERT's two pretraining corpora---as well as on pretraining examples that mask out synonyms to the task labels used in prompts. | Reject |
ICLR.cc/2023/Conference | Evaluation of Active Feature Acquisition Methods under Missing Data | Machine learning (ML) methods generally assume the full set of features are available at no cost. If the acquisition of a certain feature is costly at run-time, one might want to balance the acquisition cost and the predictive value of the feature for the ML task. The task of training an AI agent to decide which features are necessary to be acquired is called active feature acquisition (AFA). Current AFA methods, however, are challenged when the AFA agent has to be trained/tested with datasets that contain missing data. We formulate, for the first time, the problem of active feature acquisition performance evaluation (AFAPE) under missing data, i.e. the problem of adjusting for the inevitable missingness distribution shift between train/test time and run-time. We first propose a new causal graph, the AFA graph, that characterizes the AFAPE problem as an intervention on the reinforcement learning environment used to train AFA agents. Here, we discuss that for handling missing data in AFAPE, the conventional approaches (off-policy reinforcement learning, blocked feature acquisitions, imputation and inverse probability weighting (IPW)) often lead to biased results or are data inefficient. We then propose active feature acquisition importance sampling (AFAIS), a novel estimator that is more data efficient than IPW. We demonstrate the detrimental conclusions to which biased estimators can lead as well as the high data efficiency of AFAIS in multiple experiments using simulated and real-world data under induced MCAR, MAR and MNAR missingness. | Reject |
ICLR.cc/2021/Conference | A Distributional Approach to Controlled Text Generation | We propose a Distributional Approach for addressing Controlled Text Generation from pre-trained Language Models (LM). This approach permits to specify, in a single formal framework, both “pointwise’” and “distributional” constraints over the target LM — to our knowledge, the first model with such generality —while minimizing KL divergence from the initial LM distribution. The optimal target distribution is then uniquely determined as an explicit EBM (Energy-BasedModel) representation. From that optimal representation, we then train a target controlled Autoregressive LM through an adaptive distributional variant of PolicyGradient. We conduct a first set of experiments over pointwise constraints showing the advantages of our approach over a set of baselines, in terms of obtaining a controlled LM balancing constraint satisfaction with divergence from the pretrained LM. We then perform experiments over distributional constraints, a unique feature of our approach, demonstrating its potential as a remedy to the problem of Bias in Language Models. Through an ablation study, we show the effectiveness of our adaptive technique for obtaining faster convergence.
Code available at https://github.com/naver/gdc | Accept (Oral) |
ICLR.cc/2018/Conference | Training Deep AutoEncoders for Recommender Systems | This paper proposes a new model for the rating prediction task in recommender systems which significantly outperforms previous state-of-the art models on a time-split Netflix data set. Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training. We empirically demonstrate that: a) deep autoencoder models generalize much better than the shallow ones, b) non-linear activation functions with negative parts are crucial for training deep models, and c) heavy use of regularization techniques such as dropout is necessary to prevent over-fitting. We also propose a new training algorithm based on iterative output re-feeding to overcome natural sparseness of collaborate filtering. The new algorithm significantly speeds up training and improves model performance. Our code is publicly available. | Reject |
ICLR.cc/2023/Conference | Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN? | Masking some input variables of a deep neural network (DNN) and computing output changes on the masked input sample represent a typical way to compute attributions of input variables in the sample. People usually mask an input variable using its baseline value. However, there is no theory to examine whether baseline value faithfully represents the absence of an input variable, i.e., removing all signals from the input variable. Fortunately, recent studies (Ren et al., 2023a; Deng et al., 2022a) show that the inference score of a DNN can be strictly disentangled into a set of causal patterns (or concepts) encoded by the DNN. Therefore, we propose to use causal patterns to examine the faithfulness of baseline values. More crucially, it is proven that causal patterns can be explained as the elementary rationale of the Shapley value. Furthermore, we propose a method to learn optimal baseline values, and experimental results have demonstrated its effectiveness. | Accept: poster |
ICLR.cc/2018/Conference | An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems. | Human-computer conversation systems have attracted much attention in Natural Language Processing. Conversation systems can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (namely a query) in a large conversational repository and return a reply that best matches the query. Generative approaches synthesize new replies. Both ways have certain advantages but suffer from their own disadvantages. We propose a novel ensemble of retrieval-based and generation-based conversation system. The retrieved candidates, in addition to the original query, are fed to a reply generator via a neural network, so that the model is aware of more information. The generated reply together with the retrieved ones then participates in a re-ranking process to find the final reply to output. Experimental results show that such an ensemble system outperforms each single module by a large margin.
| Reject |
ICLR.cc/2018/Conference | Monotonic Chunkwise Attention | Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction. To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed. We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time. When applied to online speech recognition, we obtain state-of-the-art results and match the performance of a model using an offline soft attention mechanism. In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model. | Accept (Poster) |
ICLR.cc/2020/Conference | Optimizing Data Usage via Differentiable Rewards | To acquire a new skill, humans learn better and faster if a tutor, based on their current knowledge level, informs them of how much attention they should pay to particular content or practice problems. Similarly, a machine learning model could potentially be trained better with a scorer that “adapts” to its current learning state and estimates the importance of each training data instance. Training such an adaptive scorer efficiently is a challenging problem; in order to precisely quantify the effect of a data instance at a given time during the training, it is typically necessary to first complete the entire training process. To efficiently optimize data usage, we propose a reinforcement learning approach called Differentiable Data Selection (DDS). In DDS, we formulate a scorer network as a learnable function of the training data, which can be efficiently updated along with the main model being trained. Specifically, DDS updates the scorer with an intuitive reward signal: it should up-weigh the data that has a similar gradient with a dev set upon which we would finally like to perform well. Without significant computing overhead, DDS delivers strong and consistent improvements over several strong baselines on two very different tasks of machine translation and image classification. | Reject |
ICLR.cc/2021/Conference | Adam$^+$: A Stochastic Method with Adaptive Variance Reduction | Adam is a widely used stochastic optimization method for deep learning applications. While practitioners prefer Adam because it requires less parameter tuning, its use is problematic from a theoretical point of view since it may not converge. Variants of Adam have been proposed with provable convergence guarantee, but they tend not be competitive with Adam on the practical performance. In this paper, we propose a new method named Adam$^+$ (pronounced as Adam-plus). Adam$^+$ retains some of the key components of Adam but it also has several noticeable differences: (i) it does not maintain the moving average of second moment estimate but instead computes the moving average of first moment estimate at extrapolated points; (ii) its adaptive step size is formed not by dividing the square root of second moment estimate but instead by dividing the root of the norm of first moment estimate. As a result, Adam$^+$ requires few parameter tuning, as Adam, but it enjoys a provable convergence guarantee. Our analysis further shows that Adam$^+$ enjoys adaptive variance reduction, i.e., the variance of the stochastic gradient estimator reduces as the algorithm converges, hence enjoying an adaptive convergence. We also propose a more general variant of Adam$^+$ with different adaptive step sizes and establish their fast convergence rate. Our empirical studies on various deep learning tasks, including image classification, language modeling, and automatic speech recognition, demonstrate that Adam$^+$ significantly outperforms Adam and achieves comparable performance with best-tuned SGD and momentum SGD. | Reject |
ICLR.cc/2023/Conference | Causality Compensated Attention for Contextual Biased Visual Recognition | Visual attention does not always capture the essential object representation desired for robust predictions. Attention modules tend to underline not only the target object but also the common co-occurring context that the module thinks helpful in the training. The problem is rooted in the confounding effect of the context leading to incorrect causalities between objects and predictions, which is further exacerbated by visual attention. In this paper, to learn causal object features robust for contextual bias, we propose a novel attention module named Interventional Dual Attention (IDA) for visual recognition. Specifically, IDA adopts two attention layers with multiple sampling intervention, which compensates the attention against the confounder context. Note that our method is model-agnostic and thus can be implemented on various backbones. Extensive experiments show our model obtains significant improvements in classification and detection with lower computation. In particular, we achieve the state-of-the-art results in multi-label classification on MS-COCO and PASCAL-VOC. | Accept: poster |
ICLR.cc/2019/Conference | Analyzing Inverse Problems with Invertible Neural Networks | For many applications, in particular in natural science, the task is to
determine hidden system parameters from a set of measurements. Often,
the forward process from parameter- to measurement-space is well-defined,
whereas the inverse problem is ambiguous: multiple parameter sets can
result in the same measurement. To fully characterize this ambiguity, the full
posterior parameter distribution, conditioned on an observed measurement,
has to be determined. We argue that a particular class of neural networks
is well suited for this task – so-called Invertible Neural Networks (INNs).
Unlike classical neural networks, which attempt to solve the ambiguous
inverse problem directly, INNs focus on learning the forward process, using
additional latent output variables to capture the information otherwise
lost. Due to invertibility, a model of the corresponding inverse process is
learned implicitly. Given a specific measurement and the distribution of
the latent variables, the inverse pass of the INN provides the full posterior
over parameter space. We prove theoretically and verify experimentally, on
artificial data and real-world problems from medicine and astrophysics, that
INNs are a powerful analysis tool to find multi-modalities in parameter space,
uncover parameter correlations, and identify unrecoverable parameters. | Accept (Poster) |
ICLR.cc/2019/Conference | Learning what and where to attend | Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs). Because these networks are optimized for object recognition, they learn where to attend using only a weak form of supervision derived from image class labels. Here, we demonstrate the benefit of using stronger supervisory signals by teaching DCNs to attend to image regions that humans deem important for object recognition. We first describe a large-scale online experiment (ClickMe) used to supplement ImageNet with nearly half a million human-derived "top-down" attention maps. Using human psychophysics, we confirm that the identified top-down features from ClickMe are more diagnostic than "bottom-up" saliency features for rapid image categorization. As a proof of concept, we extend a state-of-the-art attention network and demonstrate that adding ClickMe supervision significantly improves its accuracy and yields visual features that are more interpretable and more similar to those used by human observers. | Accept (Poster) |
ICLR.cc/2023/Conference | Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play | Standard empirical risk minimization (ERM) training can produce deep neural network (DNN) models that are accurate on average but under-perform in under-represented population subgroups, especially when there are imbalanced group distributions in the long-tailed training data. Therefore, approaches that improve the accuracy - group robustness trade-off frontier of a DNN model (i.e. improving worst-group accuracy without sacrificing average accuracy, or vice versa) is of crucial importance. Uncertainty-based active learning (AL) can potentially improve the frontier by preferentially sampling underrepresented subgroups to create a more balanced training dataset. However, the quality of uncertainty estimates from modern DNNs tend to degrade in the presence of spurious correlations and dataset bias, compromising the effectiveness of AL for sampling tail groups. In this work, we propose Introspective Self-play (ISP), a simple approach to improve the uncertainty estimation of a deep neural network under dataset bias, by adding an auxiliary introspection task requiring a model to predict the bias for each data point in addition to the label. We show that ISP provably improves the bias-awareness of the model representation and the resulting uncertainty estimates. On two real-world tabular and language tasks,ISP serves as a simple “plug-in” for AL model training, consistently improving both the tail-group sampling rate and the final accuracy-fairness trade-off frontier of popular AL methods. | Accept: poster |
ICLR.cc/2023/Conference | Enhancing the Inductive Biases of Graph Neural ODE for Modeling Physical Systems | Neural networks with physics-based inductive biases such as Lagrangian neural networks (LNNs), and Hamiltonian neural networks (HNNs) learn the dynamics of physical systems by encoding strong inductive biases. Alternatively, Neural ODEs with appropriate inductive biases have also been shown to give similar performances. However, these models, when applied to particle-based systems, are transductive in nature and hence, do not generalize to large system sizes. In this paper, we present a graph-based neural ODE, GNODE, to learn the time evolution of dynamical systems. Further, we carefully analyze the role of different inductive biases on the performance of GNODE. We show that similar to LNN and HNN, encoding the constraints explicitly can significantly improve the training efficiency and performance of GNODE significantly. Our experiments also assess the value of additional inductive biases, such as Newton’s third law, on the final performance of the model. We demonstrate that inducing these biases can enhance the performance of the model by orders of magnitude in terms of both energy violation and rollout error. Interestingly, we observe that the GNODE trained with the most effective inductive biases, namely MCGNODE, outperforms the graph versions of LNN and HNN, namely, Lagrangian graph networks (LGN) and Hamiltonian graph networks (HGN) in terms of energy violation error by ∼4 orders of magnitude for a pendulum system, and ∼2 orders of magnitude for spring systems. These results suggest that NODE-based systems can give competitive performances with energy-conserving neural networks by employing appropriate inductive biases. | Accept: poster |
ICLR.cc/2018/Conference | Intriguing Properties of Adversarial Examples | It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear. Here we show that distributions of logit differences have a universal functional form. This functional form is independent of architecture, dataset, and training protocol; nor does it change during training. This leads to adversarial error having a universal scaling, as a power-law, with respect to the size of the adversarial perturbation. We show that this universality holds for a broad range of datasets (MNIST, CIFAR10, ImageNet, and random data), models (including state-of-the-art deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD). Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness. Finally, we study the effect of network architectures on adversarial sensitivity. To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR10. Our resulting architecture is more robust to white \emph{and} black box attacks compared to previous attempts.
| Invite to Workshop Track |
ICLR.cc/2022/Conference | You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction | Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involved. Recent deep learning approaches for trajectory prediction show promising performance and partially attribute this to successful reasoning about agent-agent interactions. However, it remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance based on a variant of Shapley values. Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions. Instead, the past trajectory of the target is the only feature used for predicting its future. For a task with richer social interaction patterns, on the other hand, the tested models do pick up such interactions to a certain extent, as quantified by our feature attribution method. We discuss the limits of the proposed method and its links to causality. | Accept (Poster) |
ICLR.cc/2023/Conference | S-NeRF: Neural Radiance Fields for Street Views | Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this paradigm does not fit the nature of the street views that are collected by many self-driving cars from the large-scale unbounded scenes. Also, the onboard cameras perceive scenes without much overlapping. Thus, existing NeRFs often produce blurs, "floaters" and other artifacts on street-view synthesis. In this paper, we propose a new street-view NeRF (S-NeRF) that considers novel view synthesis of both the large-scale background scenes and the foreground moving vehicles jointly. Specifically, we improve the scene parameterization function and the camera poses for learning better neural representations from street views. We also use the the noisy and sparse LiDAR points to boost the training and learn a robust geometry and reprojection based confidence to address the depth outliers. Moreover, we extend our S-NeRF for reconstructing moving vehicles that is impracticable for conventional NeRFs. Thorough experiments on the large-scale driving datasets (e.g., nuScenes and Waymo) demonstrate that our method beats the state-of-the-art rivals by reducing 7～40% of the mean-squared error in the street-view synthesis and a 45% PSNR gain for the moving vehicles rendering. | Accept: poster |
ICLR.cc/2019/Conference | Learning concise representations for regression by evolving networks of trees | We propose and study a method for learning interpretable representations for the task of regression. Features are represented as networks of multi-type expression trees comprised of activation functions common in neural networks in addition to other elementary functions. Differentiable features are trained via gradient descent, and the performance of features in a linear model is used to weight the rate of change among subcomponents of each representation. The search process maintains an archive of representations with accuracy-complexity trade-offs to assist in generalization and interpretation. We compare several stochastic optimization approaches within this framework. We benchmark these variants on 100 open-source regression problems in comparison to state-of-the-art machine learning approaches. Our main finding is that this approach produces the highest average test scores across problems while producing representations that are orders of magnitude smaller than the next best performing method (gradient boosting). We also report a negative result in which attempts to directly optimize the disentanglement of the representation result in more highly correlated features. | Accept (Poster) |
ICLR.cc/2018/Conference | Activation Maximization Generative Adversarial Nets | Class labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study the properties of the current variants of GANs that make use of class label information. With class aware gradient and cross-entropy decomposition, we reveal how class labels and associated losses influence GAN's training. Based on that, we propose Activation Maximization Generative Adversarial Networks (AM-GAN) as an advanced solution. Comprehensive experiments have been conducted to validate our analysis and evaluate the effectiveness of our solution, where AM-GAN outperforms other strong baselines and achieves state-of-the-art Inception Score (8.91) on CIFAR-10. In addition, we demonstrate that, with the Inception ImageNet classifier, Inception Score mainly tracks the diversity of the generator, and there is, however, no reliable evidence that it can reflect the true sample quality. We thus propose a new metric, called AM Score, to provide more accurate estimation on the sample quality. Our proposed model also outperforms the baseline methods in the new metric. | Accept (Poster) |
ICLR.cc/2019/Conference | Few-shot Classification on Graphs with Structural Regularized GCNs | We consider the fundamental problem of semi-supervised node classification in attributed graphs with a focus on \emph{few-shot} learning. Here, we propose Structural Regularized Graph Convolutional Networks (SRGCN), novel neural network architectures extending the well-known GCN structures by stacking transposed convolutional layers for reconstruction of input features. We add a reconstruction error term in the loss function as a regularizer. Unlike standard regularization such as $L_1$ or $L_2$, which controls the model complexity by including a penalty term depends solely on parameters, our regularization function is parameterized by a trainable neural network whose structure depends on the topology of the underlying graph. The new approach effectively addresses the shortcomings of previous graph convolution-based techniques for learning classifiers in the few-shot regime and significantly improves generalization performance over original GCNs when the number of labeled samples is insufficient. Experimental studies on three challenging benchmarks demonstrate that the proposed approach has matched state-of-the-art results and can improve classification accuracies by a notable margin when there are very few examples from each class. | Reject |
ICLR.cc/2022/Conference | Explore and Control with Adversarial Surprise | Unsupervised reinforcement learning (RL) studies how to leverage environment statistics to learn useful behaviors without the cost of reward engineering. However, a central challenge in unsupervised RL is to extract behaviors that meaningfully affect the world and cover the range of possible outcomes, without getting distracted by inherently unpredictable, uncontrollable, and stochastic elements in the environment. To this end, we propose an unsupervised RL method designed for high-dimensional, stochastic environments based on an adversarial game between two policies (which we call Explore and Control) controlling a single body and competing over the amount of observation entropy the agent experiences. The Explore agent seeks out states that maximally surprise the Control agent, which in turn aims to minimize surprise, and thereby manipulate the environment to return to familiar and predictable states. The competition between these two policies drives them to seek out increasingly surprising parts of the environment while learning to gain mastery over them. We show formally that the resulting algorithm maximizes coverage of the underlying state in block MDPs with stochastic observations, providing theoretical backing to our hypothesis that this procedure avoids uncontrollable and stochastic distractions. Our experiments further demonstrate that Adversarial Surprise leads to the emergence of complex and meaningful skills, and outperforms state-of-the-art unsupervised reinforcement learning methods in terms of both exploration and zero-shot transfer to downstream tasks. | Reject |
ICLR.cc/2023/Conference | Communication-Efficient Federated Learning with Accelerated Client Gradient | Federated learning often suffers from slow and unstable convergence due to heterogeneous characteristics of participating client datasets.
Such a tendency is aggravated when the client participation ratio is low since the information collected from the clients is prone to have large variations.
To tackle this challenge, we propose a novel federated learning framework, which improves the consistency across clients and facilitates the convergence of the server model.
This is achieved by making the server broadcast a global model with a gradient acceleration.
By adopting the strategy, the proposed algorithm conveys the projective global update information to participants effectively with no extra communication cost and relieves the clients from storing the previous models.
We also regularize local updates by aligning each of the clients with the overshot global model to reduce bias and improve the stability of our algorithm.
We perform comprehensive empirical studies on real data under various settings and demonstrate remarkable performance gains of the proposed method in terms of accuracy and communication efficiency compared to the state-of-the-art methods, especially with low client participation rates.
We will release our code to facilitate and disseminate our work. | Reject |