GenAI4ELab/papercli-papers huggingface dataset:
Order Matters: Agent-by-agent Policy Optimization
Authors: Xihuai Wang; Zheng Tian; Ziyu Wan; Ying Wen; Jun Wang; Weinan Zhang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: While multi-agent trust region algorithms have achieved great success empirically in solving coordination tasks, most of them,  however, suffer from a non-stationarity problem since agents update their policies simultaneously. In contrast, a sequential scheme that updates policies agent-by-agent provides another perspective and shows strong performance. However, sample inefficiency and lack of monotonic improvement guarantees for each agent are still the two significant challenges for the sequential scheme. In this paper, we propose the \textbf{A}gent-by-\textbf{a}gent \textbf{P}olicy \textbf{O}ptimization (A2PO) algorithm to improve the sample efficiency and retain the guarantees of monotonic improvement for each agent during training. We justify the tightness of the monotonic improvement bound compared with other trust region algorithms. From the perspective of sequentially updating agents, we further consider the effect of agent updating order and extend the theory of non-stationarity into the sequential update scheme. To evaluate A2PO, we conduct a comprehensive empirical study on four benchmarks: StarCraftII, Multi-agent MuJoCo, Multi-agent Particle Environment, and Google Research Football full game scenarios. A2PO consistently outperforms strong baselines.
Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization
Authors: Sangwon Jung; Taeeon Park; Sanghyuk Chun; Taesup Moon
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Many existing group fairness-aware training methods aim to achieve the group fairness by either re-weighting underrepresented groups based on certain rules or using weakly approximated surrogates for the fairness metrics in the objective as regularization terms. Although each of the learning schemes has its own strength in terms of applicability or performance, respectively, it is difficult for any method in the either category to be considered as a gold standard since their successful performances are typically limited to specific cases. To that end, we propose a principled method, dubbed as FairDRO, which unifies the two learning schemes by incorporating a well-justified group fairness metric into the training objective using a classwise distributionally robust optimization (DRO) framework. We then develop an iterative optimization algorithm that minimizes the resulting objective by automatically producing the correct re-weights for each group. Our experiments show that FairDRO is scalable and easily adaptable to diverse applications, and consistently achieves the state-of-the-art performance on several benchmark datasets in terms of the accuracy-fairness trade-off, compared to recent strong baselines.
Sign and Basis Invariant Networks for Spectral Graph Representation Learning
Authors: Derek Lim; Joshua David Robinson; Lingxiao Zhao; Tess Smidt; Suvrit Sra; Haggai Maron; Stefanie Jegelka
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We introduce SignNet and BasisNet---new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if v is an eigenvector then so is -v; and (ii) more general basis symmetries, which occur in higher dimensional eigenspaces with infinitely many choices of basis eigenvectors. We prove that under certain conditions our networks are universal, i.e., they can approximate any continuous function of eigenvectors with the desired invariances. When used with Laplacian eigenvectors, our networks are provably more expressive than existing spectral methods on graphs; for instance, they subsume all spectral graph convolutions, certain spectral graph invariants, and previously proposed graph positional encodings as special cases. Experiments show that our networks significantly outperform existing baselines on molecular graph regression, learning expressive graph representations, and learning neural fields on triangle meshes. Our code is available at https://github.com/cptq/SignNet-BasisNet.
Inducing Meaningful Units from Character Sequences with Dynamic Capacity Slot Attention
Authors: Melika Behjati; James Henderson
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Characters do not convey meaning, but sequences of characters do.  We propose an unsupervised distributional method to learn the abstract meaning-bearing units in a sequence of characters. Rather than segmenting the sequence, our Dynamic Capacity Slot Attention model discovers continuous representations of the \textit{objects} in the sequence, extending an architecture for object discovery in images.  We train our model on different languages and evaluate the quality of the obtained representations with forward and reverse probing classifiers.  These experiments show that our model succeeds in discovering units which are similar to those proposed previously in form, content and level of abstraction, and which show promise for capturing meaningful information at a higher level of abstraction.
Neural Groundplans: Persistent Neural Scene Representations from a Single Image
Authors: Prafull Sharma; Ayush Tewari; Yilun Du; Sergey Zakharov; Rares Andrei Ambrus; Adrien Gaidon; William T. Freeman; Fredo Durand; Joshua B. Tenenbaum; Vincent Sitzmann
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene. Motivated by the bird’s-eye-view (BEV) representation commonly used in vision and robotics, we propose conditional neural groundplans, ground-aligned 2D feature grids, as persistent and memory-efficient scene representations. Our method is trained self-supervised from unlabeled multi-view observations using differentiable rendering, and learns to complete geometry and appearance of occluded regions. In addition, we show that we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time. The ability to separately reconstruct movable objects enables a variety of downstream tasks using simple heuristics, such as extraction of object-centric 3D representations, novel view synthesis, instance-level segmentation, 3D bounding box prediction, and scene editing. This highlights the value of neural groundplans as a backbone for efficient 3D scene understanding models.
Learning Proximal Operators to Discover Multiple Optima
Authors: Lingxiao Li; Noam Aigerman; Vladimir Kim; Jiajin Li; Kristjan Greenewald; Mikhail Yurochkin; Justin Solomon
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Finding multiple solutions of non-convex optimization problems is a ubiquitous yet challenging task. Most past algorithms either apply single-solution optimization methods from multiple random initial guesses or search in the vicinity of found solutions using ad hoc heuristics. We present an end-to-end method to learn the proximal operator of a family of training problems so that multiple local minima can be quickly obtained from initial guesses by iterating the learned operator, emulating the proximal-point algorithm that has fast convergence. The learned proximal operator can be further generalized to recover multiple optima for unseen problems at test time, enabling applications such as object detection. The key ingredient in our formulation is a proximal regularization term, which elevates the convexity of our training loss: by applying recent theoretical results, we show that for weakly-convex objectives with Lipschitz gradients, training of the proximal operator converges globally with a practical degree of over-parameterization. We further present an exhaustive benchmark for multi-solution optimization to demonstrate the effectiveness of our method.
Simplicity bias in $1$-hidden layer neural networks
Authors: Depen Morwani; Praneeth Netrapalli; jatin batra; Karthikeyan Shanmugam; Prateek Jain
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recent works \citep{shah2020pitfalls,chen2021intriguing} have demonstrated that neural networks exhibit extreme \emph{simplicity bias} (SB). That is,  they learn \emph{only the simplest} features  to solve a task at hand, even in the presence of other, more robust but more complex features. Due to lack of a general and rigorous definition of \emph{features}, these works showcase SB on \emph{semi-synthetic} datasets such as Color-MNIST, MNIST-CIFAR where defining features is relatively easier. 

In this work, we rigorously define as well as thoroughly establish SB for \emph{one hidden layer} neural networks. More concretely, (i) we define SB as the network essentially being a function of a low dimensional projection of the inputs (ii) theoretically, we show that when the data is linearly separable, the network primarily depends on only the linearly separable ($1$-dimensional) subspace even in the presence of an arbitrarily large number of other, more complex features which could have led to a significantly more robust classifier,  (iii) empirically, we show that models trained on \emph{real} datasets such as Imagenette and Waterbirds-Landbirds indeed depend on a low dimensional projection of the inputs, thereby demonstrating SB on these datasets, iv) finally, we present a natural ensemble approach that encourages diversity in  models by training successive models on features not used by earlier models, and demonstrate that it yields models that are significantly more robust to Gaussian noise.
Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics
Authors: Shoaib Ahmed Siddiqui; Nitarshan Rajkumar; Tegan Maharaj; David Krueger; Sara Hooker
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Modern machine learning research relies on relatively few carefully curated datasets. Even in these datasets, and typically in `untidy' or raw data, practitioners are faced with significant issues of data quality and diversity which can be prohibitively labor intensive to address. Existing methods for dealing with these challenges tend to make strong assumptions about the particular issues at play, and often require a priori knowledge or metadata such as domain labels. Our work is orthogonal to these methods: we instead focus on providing a unified and efficient framework for Metadata Archaeology -- uncovering and inferring metadata of examples in a dataset. We curate different subsets of data that might exist in a dataset (e.g. mislabeled, atypical, or out-of-distribution examples) using simple transformations, and leverage differences in learning dynamics between these probe suites to infer metadata of interest. Our method is on par with far more sophisticated mitigation methods across different tasks: identifying and correcting mislabeled examples, classifying minority-group samples, prioritizing points relevant for training and enabling scalable human auditing of relevant examples.
Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning
Authors: Alex Gu; Songtao Lu; Parikshit Ram; Tsui-Wei Weng
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We consider a generic min-max multi-objective bilevel optimization problem with applications in robust machine learning such as representation learning and hyperparameter optimization. We design MORBiT, a novel single-loop gradient descent-ascent bilevel optimization algorithm, to solve the generic problem and present a novel analysis showing that MORBiT converges to the first-order stationary point at a rate of $\widetilde{\mathcal{O}}(n^{1/2} K^{-2/5})$ for a class of weakly convex problems with $n$ objectives upon $K$ iterations of the algorithm. Our analysis utilizes novel results to handle the non-smooth min-max multi-objective setup and to obtain a sublinear dependence in the number of objectives $n$. Experimental results on robust representation learning and robust hyperparameter optimization showcase (i) the advantages of considering the min-max multi-objective setup, and (ii) convergence properties of the proposed \morbit.
Imitating Human Behaviour with Diffusion Models
Authors: Tim Pearce; Tabish Rashid; Anssi Kanervisto; Dave Bignell; Mingfei Sun; Raluca Georgescu; Sergio Valcarcel Macua; Shan Zheng Tan; Ida Momennejad; Katja Hofmann; Sam Devlin
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Diffusion models have emerged as powerful generative models in the text-to-image domain. This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments. Human behaviour is stochastic and multimodal, with structured correlations between action dimensions. Meanwhile, standard modelling choices in behaviour cloning are limited in their expressiveness and may introduce bias into the cloned policy. We begin by pointing out the limitations of these choices. We then propose that diffusion models are an excellent fit for imitating human behaviour, since they learn an expressive distribution over the joint action space. We introduce several innovations to make diffusion models suitable for sequential environments; designing suitable architectures, investigating the role of guidance, and developing reliable sampling strategies. Experimentally, diffusion models closely match human demonstrations in a simulated robotic control task and a modern 3D gaming environment.
Active Sampling for Node Attribute Completion on Graphs
Authors: Benyuan Liu; Xu Chen; Yanfeng Wang; Ya Zhang; Zhi Cao; Ivor Tsang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Node attribute is one kind of crucial information on graphs, but real-world graphs usually face attribute-missing problem where attributes of partial nodes are missing and attributes of the other nodes are available. It is meaningful to restore the missing attributes so as to benefit downstream graph learning tasks. Popular GNN is not designed for this node attribute completion issue and is not capable of solving it. Recent proposed Structure-attribute Transformer (SAT) framework decouples the input of graph structures and node attributes by a distribution matching technique, and can work on it properly. However, SAT leverages nodes with observed attributes in an equally-treated way and neglects the different contributions of different nodes in learning. In this paper, we propose a novel active sampling algorithm (ATS) to more efficiently utilize the nodes with observed attributes and better restore the missing node attributes. Specifically, ATS contains two metrics that measure the representativeness and uncertainty of each node's information by considering the graph structures, representation similarity and learning bias. Then, these two metrics are linearly combined by a Beta distribution controlled weighting scheme to finally determine which nodes are selected into the train set in the next optimization step. This ATS algorithm can be combined with SAT framework together, and is learned in an iterative manner. Through extensive experiments on 4 public benchmark datasets and two downstream tasks, we show the superiority of ATS in node attribute completion.
Image Segmentation using Transfer Learning with DeepLabv3 to Facilitate Photogrammetric Limb Scanning
Authors: Isaac A Cabrera; Yixuan Zhou; Eric K Ngo; Ramesh Rao; Albert Lin
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper, we explore the use of deep learning (DL) in conjunction with photogrammetry for scanning amputated limbs. Combining these two technologies can expand the scope of prosthetic telemedicine by facilitating low-cost limb scanning using cell phones. Previous research identified image segmentation as one of the main limitations of using photogrammetry for limb scanning. Based on those limitations, this work sought to answer two main research questions: (1) Can a neural network be trained to identify and segment an amputated limb automatically? (2) Will segmenting 2D limb images using neural networks impact the accuracy of 3D models generated via photogrammetry? To answer the first question, transfer learning was applied to a neural network with the DeepLabv3 architecture. After training, the model was able to successfully identify and segment limb images with an IoU of 79.9%. To answer the second question, the fine-tuned DL model was applied to a dataset of 22 scans comprising 6312 limb images, then 3D models were rendered utilizing Agisoft Metashape. The Mean Absolute Error (MAE) of models rendered from images segmented with DL was 0.57 mm ± 0.63 mm when compared to models rendered from ground truth images. These results are important because segmentation with DL makes photogrammetry for limb scanning feasible on a large clinical scale. Future work should focus on generalizing the segmentation model for different types of amputations and imaging conditions.
Spatio-temporal point processes with deep non-stationary kernels
Authors: Zheng Dong; Xiuyuan Cheng; Yao Xie
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Point process data are becoming ubiquitous in modern applications, such as social networks, health care, and finance. Despite the powerful expressiveness of the popular recurrent neural network (RNN) models for point process data, they may not successfully capture sophisticated non-stationary dependencies in the data due to their recurrent structures. Another popular type of deep model for point process data is based on representing the influence kernel (rather than the intensity function) by neural networks. We take the latter approach and develop a new deep non-stationary influence kernel that can model non-stationary spatio-temporal point processes. The main idea is to approximate the influence kernel with a novel and general low-rank decomposition, enabling efficient representation through deep neural networks and computational efficiency and better performance. We also take a new approach to maintain the non-negativity constraint of the conditional intensity by introducing a log-barrier penalty. We demonstrate our proposed method's good performance and computational efficiency compared with the state-of-the-art on simulated and real data.
Flow Matching for Generative Modeling
Authors: Yaron Lipman; Ricky T. Q. Chen; Heli Ben-Hamu; Maximilian Nickel; Matthew Le
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian probability paths for transforming between noise and data samples---which subsumes existing diffusion paths as specific instances. Interestingly, we find that employing FM with diffusion paths results in a more robust and stable alternative for training diffusion models. Furthermore, Flow Matching opens the door to training CNFs with other, non-diffusion probability paths. An instance of particular interest is using Optimal Transport (OT) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. Training CNFs using Flow Matching on ImageNet leads to consistently better performance than alternative diffusion-based methods in terms of both likelihood and sample quality, and allows fast and reliable sample generation using off-the-shelf numerical ODE solvers.
Leveraging the Third Dimension in Contrastive Learning
Authors: Sumukh K Aithal; Anirudh Goyal; Alex Lamb; Yoshua Bengio; Michael Curtis Mozer
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Self-Supervised Learning (SSL) methods operate on unlabeled data to learn robust representations useful for downstream tasks. Most SSL methods rely on augmentations obtained by transforming the 2D image pixel map.  These augmentations ignore the fact that  biological vision takes place in an immersive  three-dimensional, temporally contiguous environment, and that low-level biological vision relies heavily on depth cues. Using a signal provided by a pretrained state-of-the-art RGB-to-depth model (the Depth Prediction Transformer, Ranftl et al., 2021), we explore two distinct approaches to incorporating depth signals into the SSL framework. First, we evaluate contrastive learning using an RGB+depth input representation. Second, we use the depth signal to generate novel views from slightly different camera positions, thereby producing a 3D augmentation for contrastive learning. We evaluate these two approaches on three different SSL methods---BYOL, SimSiam, and SwAV---using ImageNette (10 class subset of ImageNet) and ImageNet-100. We find that both approaches to incorporating depth signals improve the robustness and generalization of the baseline SSL methods, though the first approach (with depth-channel concatenation) is superior.
Continual Transformers: Redundancy-Free Attention for Online Inference
Authors: Lukas Hedegaard; Arian Bakhtiarnia; Alexandros Iosifidis
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a time. Consequently, their use during online inference on time-series data entails considerable redundancy due to the overlap in successive token sequences. In this work, we propose novel formulations of the Scaled Dot-Product Attention, which enable Transformers to perform efficient online token-by-token inference on a continual input stream. Importantly, our modifications are purely to the order of computations, while the outputs and learned weights are identical to those of the original Transformer Encoder. We validate our Continual Transformer Encoder with experiments on the THUMOS14, TVSeries and GTZAN datasets with remarkable results: Our Continual one- and two-block architectures reduce the floating point operations per prediction by up to 63x and 2.6x, respectively, while retaining predictive performance.
CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations
Authors: Hangzhi Guo; Thanh Hong Nguyen; Amulya Yadav
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Counterfactual (or CF) explanations are a type of local explanations for Machine Learning (ML) model predictions, which offer a contrastive case as an explanation by finding the smallest changes (in feature space) to the input data point, which will lead to a different prediction by the ML model. Existing CF explanation techniques suffer from two major limitations: (i) all of them are post-hoc methods designed for use with proprietary ML models --- as a result, their procedure for generating CF explanations is uninformed by the training of the ML model, which leads to misalignment between model predictions and explanations; and (ii) most of them rely on solving separate time-intensive optimization problems to find CF explanations for each input data point (which negatively impacts their runtime). This work makes a novel departure from the prevalent post-hoc paradigm (of generating CF explanations) by presenting CounterNet, an end-to-end learning framework which integrates predictive model training and the generation of counterfactual (CF) explanations into a single pipeline. We adopt a block-wise coordinate descent procedure which helps in effectively training CounterNet's network. Our extensive experiments on multiple real-world datasets show that CounterNet generates high-quality predictions, and consistently achieves 100% CF validity and very low proximity scores (thereby achieving a well-balanced cost-invalidity trade-off) for any new input instance, and runs 3X faster than existing state-of-the-art baselines.