GenAI4ELab/papercli-papers huggingface dataset:
Approximate Conditional Coverage via Neural Model Approximations
Authors: Allen Schmaltz; Danielle Rasooly
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose a new approach for constructing prediction sets for Transformer networks via the strong signals for prediction reliability from KNN-based approximations. This enables a data-driven partitioning of the high-dimensional feature space and a new Inductive Venn Predictor for calibration, the Venn-ADMIT Predictor. Our approach more closely obtains approximate conditional coverage than recent work proposing adaptive and localized conformal score functions for deep networks. We analyze coverage on several representative natural language processing classification tasks, including class-imbalanced and distribution-shifted settings.
Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap
Authors: Weiyang Liu; Longhui Yu; Adrian Weller; Bernhard Schölkopf
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The neural collapse (NC) phenomenon describes an underlying geometric symmetry for deep neural networks, where both deeply learned features and classifiers converge to a simplex equiangular tight frame. It has been shown that both cross-entropy loss and mean square error can provably lead to NC. We remove NC's key assumption on the feature dimension and the number of classes, and then present a generalized neural collapse (GNC) hypothesis that effectively subsumes the original NC. Inspired by how NC characterizes the training target of neural networks, we decouple GNC into two objectives: minimal intra-class variability and maximal inter-class separability. We then use hyperspherical uniformity (which characterizes the degree of uniformity on the unit hypersphere) as a unified framework to quantify these two objectives. Finally, we propose a general objective -- hyperspherical uniformity gap (HUG), which is defined by the difference between inter-class and intra-class hyperspherical uniformity. HUG not only provably converges to GNC, but also decouples GNC into two separate objectives. Unlike cross-entropy loss that couples intra-class compactness and inter-class separability, HUG enjoys more flexibility and serves as a good alternative loss function. Empirical results show that HUG works well in terms of generalization and robustness.
Robustness for Free: Adversarially Robust Anomaly Detection Through Diffusion Model
Authors: Yuanpu Cao; Lu Lin; Jinghui Chen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Deep learning-based anomaly detection models have achieved remarkably high accuracy on commonly used benchmark datasets. However, the robustness of those models may not be satisfactory due to the existence of adversarial examples, which pose significant threats to the practical deployment of deep anomaly detectors. To tackle this issue, we propose an adversarially robust anomaly detector based on the diffusion model. There are two things that make diffusion models a perfect match for our task: 1) the diffusion model itself is a reconstruction-based modeling method whose reconstruction error can serve as a natural indicator of the anomaly score; 2) previous studies have shown that diffusion models can help purify the data for better adversarial robustness. In this work, we highlight that our diffusion model based method gains the adversarial robustness for free: the diffusion model will act both as an anomaly detector and an adversarial defender, thus no extra adversarial training or data purification is needed as in standard robust image classification tasks. We also extend our proposed method for certified robustness to $l_2$ norm bounded perturbations. Through extensive experiments, we show that our proposed method exhibits outstanding (certified) adversarial robustness while also maintaining equally strong anomaly detection performance on par with the state-of-the-art anomaly detectors on benchmark datasets.
Going Beyond Approximation: Encoding  Constraints for Explainable Multi-hop Inference via Differentiable Combinatorial Solvers
Authors: Mokanarangan Thayaparan; Marco Valentino; Andre Freitas
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Integer Linear Programming (ILP) provides a viable mechanism to encode explicit and controllable assumptions about explainable multi-hop inference with natural language. However, an ILP formulation is non-differentiable and cannot be integrated into broader deep learning architectures. Recently, Thayaparan et al. (2021a) proposed a novel methodology to integrate ILP with Transformers to achieve end-to-end differentiability for complex multi-hop inference. While this hybrid framework has been demonstrated to deliver better answer and explanation selection than transformer-based and existing ILP solvers, the neuro-symbolic integration still relies on a convex relaxation of the ILP formulation, which can produce sub-optimal solutions. To improve these limitations, we propose Diff-Comb Explainer, a novel neuro-symbolic architecture based on Differentiable BlackBox Combinatorial solvers (DBCS) (Pogančić et al., 2019). Unlike existing differentiable solvers, the presented model does not require the transformation and relaxation of the explicit semantic constraints, allowing for direct and more efficient integration of ILP formulations. Diff-Comb Explainer demonstrates improved accuracy and explainability over non-differentiable solvers, Transformers and existing differentiable constraint-based multi-hop inference frameworks.
Diffusion Probabilistic Fields
Authors: Peiye Zhuang; Samira Abnar; Jiatao Gu; Alex Schwing; Joshua M. Susskind; Miguel Ángel Bautista
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains. However, to adapt diffusion generative modeling to these domains the denoising network needs to be carefully designed for each domain independently, oftentimes under the assumption that data lives in a Euclidean grid. In this paper we introduce Diffusion Probabilistic Fields (DPF), a diffusion model that can learn distributions over continuous functions defined over metric spaces, commonly known as fields. We extend the formulation of diffusion probabilistic models to deal with this field parametrization in an explicit way, enabling us to define an end-to-end learning algorithm that side-steps the requirement of representing fields with latent vectors as in previous approaches (Dupont et al., 2022a; Du et al., 2021). We empirically show that, while using the same denoising network, DPF effectively deals with different modalities like 2D images and 3D geometry, in addition to modeling distributions over fields defined on non-Euclidean metric spaces.
On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition
Authors: Rhea Sanjay Sukthanker; Samuel Dooley; John P Dickerson; Colin White; Frank Hutter; Micah Goldblum
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Face recognition systems are deployed across the world by government agencies and contractors for sensitive and impactful tasks, such as surveillance and database matching.  Despite their widespread use, these systems are known to exhibit bias across a range of sociodemographic dimensions, such as gender and race.  Nonetheless, an array of works proposing pre-processing, training, and post-processing methods have failed to close these gaps. Here, we take a very different approach to this problem, identifying that both architectures and hyperparameters of neural networks are instrumental in reducing bias. We first run a large-scale analysis of the impact of architectures and training hyperparameters on several common fairness metrics and show that the implicit convention of choosing high-accuracy architectures may be suboptimal for fairness. Motivated by our findings, we run the first neural architecture search for fairness, jointly with a search for hyperparameters. We output a suite of models which Pareto-dominate all other competitive architectures in terms of accuracy and fairness. Furthermore, we show that these models transfer well to other face recognition datasets with similar and distinct protected attributes. We release our code and raw result files so that researchers and practitioners can replace our fairness metrics with a bias measure of their choice.
Continual Unsupervised Disentangling of Self-Organizing Representations
Authors: Zhiyuan Li; Xiajun Jiang; Ryan Missel; Prashnna Kumar Gyawali; Nilesh Kumar; Linwei Wang
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Limited progress has been made in continual unsupervised learning of representations, especially in reusing, expanding, and continually disentangling learned semantic factors across data environments. We argue that this is because existing approaches treat continually-arrived data independently, without considering how they are related based on the underlying semantic factors. We address this by a new generative model describing a topologically-connected mixture of spike-and-slab distributions in the latent space, learned end-to-end in a continual fashion via principled variational inference. The learned mixture is able to automatically discover the active semantic factors underlying each data environment and to accumulate their relational structure based on that. This distilled knowledge of different data environments can further be used for generative replay and guiding continual disentangling of new semantic factors. We tested the presented method on a split version of 3DShapes to provide the first quantitative disentanglement evaluation of continually learned representations, and further demonstrated its ability to continually disentangle new representations in benchmark datasets.
Unsupervised learning of features and object boundaries from local prediction
Authors: Heiko H. Schütt; Wei Ji Ma
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A visual system has to learn both which features to extract from images and how to group locations into (proto-)objects. Those two aspects are usually dealt with separately, although predictability is discussed as a cue for both. To incorporate features and boundaries into the same model, we model a layer of feature maps with a pairwise Markov random field model in which each factor is paired with an additional binary variable, which switches the factor on or off. Using one of two contrastive learning objectives, we can learn both the features and the parameters of the Markov random field factors from images without further supervision signals. The features learned by shallow neural networks based on this loss are local averages, opponent colors, and Gabor-like stripe patterns. Furthermore, we can infer connectivity between locations by inferring the switch variables. Contours inferred from this connectivity perform quite well on the Berkeley segmentation database (BSDS500) without any training on contours. Thus, computing predictions across space aids both segmentation and feature learning, and models trained to optimize these predictions show similarities to the human visual system. We speculate that retinotopic visual cortex might implement such predictions over space through lateral connections.
Theoretical  Characterization of Neural Network Generalization with Group Imbalance
Authors: Hongkang Li; Shuai Zhang; Meng Wang; Yihua Zhang; Pin-Yu Chen; Sijia Liu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Group imbalance has been a known problem in empirical risk minimization (ERM), where the achieved high \textit{average} accuracy could be accompanied by low accuracy in a \textit{minority} group. Despite various algorithmic efforts to improve the minority group accuracy, a theoretical study of the generalization performance of ERM on individual groups remains elusive. By formulating the group imbalance problem with the Gaussian Mixture Model, this paper quantifies the impact of individual groups on the sample complexity, the convergence rate, and the average and group-level testing performance. Although our theoretical framework is centered on binary classification using a one-hidden-layer neural network, to the best of our knowledge, we provide the first theoretical analysis of the group-level generalization of ERM in addition to the commonly studied average generalization performance. Sample insights of our theoretical results include that when all group-level co-variance is in the medium regime and all mean are close to zero,  the learning performance is most desirable in the sense of a small sample complexity, a fast training rate, and a high average and group-level testing accuracy. Moreover, we show that increasing the fraction of the minority group in the training data does not necessarily improve the generalization performance of the minority group.  Our theoretical results are validated on both synthetic and empirical datasets such as CelebA and CIFAR-10 in image classification.
Learning Basic Interpretable Factors from Temporal Signals via Physics Symmetry
Authors: Xuanjie Liu; Daniel Chin; Yichen Huang; Gus Xia
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We have recently seen great progress in learning interpretable music representations, ranging from basic factors, such as pitch and timbre, to high-level concepts, such as chord, texture and melody contour. However, most methods rely heavily on music domain knowledge and it remains an open question how to learn interpretable and disentangled representations using inductive biases that are more general. In this study, we use \textit{physical symmetry} as a self-consistency constraint on the latent space. Specifically, it requires the prior model that characterises the dynamics of the latent states to be \textit{equivariant} with respect to a certain group transformation (say, translation and rotation). We show that our model can learn \textit{linear} pitch factor (that agrees with human music perception) as well as pitch-timbre disentanglement from unlabelled monophonic music audio. In addition, the same methodology can be applied to computer vision, learning the 3D Cartesian space as well as space-colour disentanglement from a simple moving object shot by a single fix camera. Furthermore, applying physical symmetry to the prior model naturally leads to \textit{representation augmentation}, a new learning technique which helps improve sample efficiency.
No Double Descent in PCA: Training and Pre-Training in High Dimensions
Authors: Daniel Gedon; Antonio H. Ribeiro; Thomas B. Schön
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: With the recent body of work on overparameterized models the gap between theory and practice in contemporary machine learning is shrinking. While many of the present state-of-the-art models have an encoder-decoder architecture, there is little theoretical work for this model structure. To improve our understanding in this direction, we consider linear encoder-decoder models, specifically PCA with linear regression on data from a low-dimensional manifold. We present an analysis for fundamental guarantees of the risk and asymptotic results for isotropic data when the model is trained in a supervised manner. The results are also verified in simulations. Furthermore, we extend our analysis to the popular setting where parts of the model are pre-trained in an unsupervised manner by pre-training the PCA encoder with subsequent supervised training of the linear regression. We show that the overall risk depends on the estimates of the eigenvectors in the encoder and present a sample complexity requirement through a concentration bound. The results highlight that using more pre-training data decreases the overall risk only if it improves the eigenvector estimates. Therefore, we stress that the eigenvalue distribution determines whether more pre-training data is useful or not.
Sparsity by Redundancy: Solving $L_1$ with a Simple Reparametrization
Authors: Liu Ziyin; Zihao Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We identify and prove a general principle: $L_1$ sparsity can be achieved using a redundant parametrization plus $L_2$ penalty. Our results lead to a simple algorithm, \textit{spred}, that seamlessly integrates $L_1$ regularization into any modern deep learning framework. Practically, we demonstrate (1) the efficiency of \textit{spred} in optimizing conventional tasks such as lasso and sparse coding, (2) benchmark our method for nonlinear feature selection of six gene selection tasks, and (3) illustrate the usage of the method for achieving structured and unstructured sparsity in deep learning in an end-to-end manner. Conceptually, our result bridges the gap in understanding the inductive bias of the redundant parametrization common in deep learning and conventional statistical learning.
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
Authors: Ziluo Ding; Wanpeng Zhang; Junpeng Yue; Xiangjun Wang; Tiejun Huang; Zongqing Lu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We investigate the use of natural language to drive the generalization of policies in multi-agent settings. Unlike single-agent settings, the generalization of policies should also consider the influence of other agents. Besides, with the increasing number of entities in multi-agent settings, more agent-entity interactions are needed for language grounding, and the enormous search space could impede the learning process. Moreover, given a simple general instruction, e.g., beating all enemies, agents are required to decompose it into multiple subgoals and figure out the right one to focus on. Inspired by previous work, we try to address these issues at the entity level and propose a novel framework for language grounding in multi-agent reinforcement learning, entity divider (EnDi). EnDi enables agents to independently learn subgoal division at the entity level and act in the environment based on the associated entities. The subgoal division is regularized by opponent modeling to avoid subgoal conflicts and promote coordinated strategies. Empirically, EnDi demonstrates the strong generalization ability to unseen games with new dynamics and expresses the superiority over existing methods.
Model Obfuscation for Securing Deployed Neural Networks
Authors: Mingyi Zhou; Xiang Gao; Jing Wu; John C. Grundy; Xiao Chen; Chunyang Chen; Li Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: More and more edge devices and mobile apps are leveraging deep learning (DL) capabilities. Deploying such models on devices -- referred to as on-device models -- rather than as remote cloud-hosted services, has gained popularity as it avoids transmitting user's data off of the device and for high response time. However, on-device models can be easily attacked, as they can be accessed by unpacking corresponding apps and the model is fully exposed to attackers. Recent studies show that adversaries can easily generate white-box-like attacks for an on-device model or even inverse its training data. To protect on-device models from white-box attacks, we propose a novel technique called model obfuscation. Specifically, model obfuscation hides and obfuscates the key information -- structure, parameters and attributes -- of models by renaming, parameter encapsulation, neural structure obfuscation, shortcut injection, and extra layer injection. We have developed a prototype tool ModelObfuscator to automatically obfuscate on-device TFLite models. Our experiments show that this proposed approach can dramatically improve model security by significantly increasing the overhead of extracting models' inner information, without increasing the latency of DL models. Our proposed on-device model obfuscation has the potential to be a fundamental technique for on-device model deployment. Our prototype tool is publicly available at https://github.com/AnonymousAuthor000/Code2536.
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Authors: Erik Nijkamp; Bo Pang; Hiroaki Hayashi; Lifu Tu; Huan Wang; Yingbo Zhou; Silvio Savarese; Caiming Xiong
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Program synthesis strives to generate a computer program as a solution to a given problem specification, expressed with input-output examples or natural language descriptions. The prevalence of large language models advances the state-of-the-art for program synthesis, though limited training resources and data impede open access to such models. To democratize this, we train and release a family of large language models up to 16.1B parameters, called CODEGEN, on natural language and programming language data, and open source the training library JAXFORMER. We show the utility of the trained model by demonstrating that it is competitive with the previous state-of-the-art on zero-shot Python code generation on HumanEval. We further investigate the multi-step paradigm for program synthesis, where a single program is factorized into multiple prompts specifying subproblems. To this end, we construct an open benchmark, Multi-Turn Programming Benchmark (MTPB), consisting of 115 diverse problem sets that are factorized into multi-turn prompts. Our analysis on MTPB shows that the same intent provided to CODEGEN in multi-turn fashion significantly improves program synthesis over that provided as a single turn. We make the training library JAXFORMER and model checkpoints available as open source contribution: https://github.com/salesforce/CodeGen.
Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise
Authors: Wenbo Gong; Joel Jennings; Cheng Zhang; Nick Pawlowski
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains. For example, in stock markets, the announcement of acquisitions from leading companies may have immediate effects on stock prices and increase the uncertainty of the future market due to this past action. To discover causal relations in such case, the model needs to consider non-linear relations between variables, instantaneous effect and the change of noise distribution due to past actions. We name the latter as history-dependent noise. However, previous works do not offer a solution addressing all these problems together. In this paper, we propose a structural equation model, called Rhino, which combines vector auto-regression, deep learning and variational inference to model non-linear relationships with instantaneous effects while allowing the noise distribution to be modulated by history observations. Theoretically, we prove the structural identifiability of Rhino. Our empirical results from extensive synthetic experiments and two real-world benchmarks demonstrate better discovery performance compared to relevant baselines, with ablation studies revealing its robustness under model misspecification.