mtybilly/Instruct2DS huggingface dataset:
Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning
Authors: Fahad Sarfraz, Elahe Arani, Bahram Zonooz
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Humans excel at lifelong learning, as the brain has evolved to be robust to distribution shifts and noise in our ever-changing environment. Deep neural networks (DNNs), however, exhibit catastrophic forgetting and the learned representations drift drastically as they encounter a new task. This alludes to a different error-based learning mechanism in the brain. Unlike DNNs, where learning scales linearly with the magnitude of the error, the sensitivity to errors in the brain decreases as a function of their magnitude. To this end, we propose "ESMER" which employs a principled mechanism to modulate error sensitivity in a dual-memory rehearsal-based system. Concretely, it maintains a memory of past errors and uses it to modify the learning dynamics so that the model learns more from small consistent errors compared to large sudden errors. We also propose "Error-Sensitive Reservoir Sampling" to maintain episodic memory, which leverages the error history to pre-select low-loss samples as candidates for the buffer, which are better suited for retaining information. Empirical results show that ESMER effectively reduces forgetting and abrupt drift in representations at the task boundary by gradually adapting to the new task while consolidating knowledge. Remarkably, it also enables the model to learn under high levels of label noise, which is ubiquitous in real-world data streams.
Adaptive Optimization in the $\infty$-Width Limit
Authors: Etai Littwin, Greg Yang
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Recent works have developed detailed understanding of large neural networks' behaviors via their infinite-width limits, e.g., the neural tangent kernel (NTK) and the feature learning ($\mu$) limits. These theories were developed for stochastic gradient descent. Yet, in practice, all large NN are trained using Adam or other adaptive gradient optimizers (AGO), which are not covered by such previous works. Here, we close this gap via the Tensor Programs framework. Specifically, for deep MLPs, we derive the NTK and $\mu$ parametrizations as well as their infinite-width limits. We find 1) The NTK limit of AGO, in contrast to that of SGD, now depends nonlinearly on the loss derivative but nevertheless still fails to learn features; 2) this is fixed by the $\mu$ limit of AGO (as in the case of SGD). To obtain these results, we extend the Tensor Programs language with a new instruction that allows one to express the gradient processing done by AGOs.
On Pre-training Language Model for Antibody
Authors: Danqing Wang, Fei YE, Hao Zhou
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Antibodies are vital proteins offering robust protection for the human body from pathogens. The development of general protein and antibody-specific pre-trained language models both facilitate antibody prediction tasks. However, there have been limited studies that comprehensively explore the representation capability of distinct pre-trained language models on different antibody tasks. To investigate the problem, we aim to answer several key questions in this paper, such as how pre-trained language models perform in antibody tasks with different specificity and how introducing specific biological mechanisms to the pre-training process can benefit the model. Additionally, we evaluate if the learned antibody pre-trained representations can be applied to real-world antibody problems, like drug discovery and immune process understanding. Previously, no benchmark available largely hindered the study to answer these questions. To aid in our investigation, we provide an AnTibody Understanding Evaluation (ATUE) benchmark. We comprehensively evaluate the performance of protein pre-trained language models by empirical study along with conclusions and new insights. Our ATUE and code are released at https://github.com/dqwang122/EATLM.
Achieving Sub-linear Regret in Infinite Horizon Average Reward Constrained MDP with Linear Function Approximation
Authors: Arnob Ghosh, Xingyu Zhou, Ness Shroff
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: We study the infinite horizon average reward constrained Markov Decision Process (CMDP). In contrast to existing works on model-based, finite state space, we consider the model-free linear CMDP setup.  We first propose a computationally inefficient algorithm and show that $\tilde{\mathcal{O}}(\sqrt{d^3T})$ regret and constraint violation can be achieved, in which $T$ is the number of interactions, and $d$ is the dimension of the feature mapping. We also propose an efficient variant based on the primal-dual adaptation of the LSVI-UCB algorithm and show that $\tilde{\mathcal{O}}((dT)^{3/4})$ regret and constraint violation can be achieved. 
This improves the known regret bound of $\tilde{\mathcal{O}}(T^{5/6})$ for the finite state-space model-free constrained RL which was obtained under a stronger assumption compared to ours.  We also develop an efficient policy-based algorithm via novel adaptation of the MDP-EXP2 algorithm to our primal-dual set up with $\tilde{\mathcal{O}}(\sqrt{T})$ regret and even zero constraint violation bound under a stronger set of assumptions.
Sampling with Mollified Interaction Energy Descent
Authors: Lingxiao Li, qiang liu, Anna Korba, Mikhail Yurochkin, Justin Solomon
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Sampling from a target measure whose density is only known up to a normalization constant is a fundamental problem in computational statistics and machine learning. In this paper, we present a new optimization-based method for sampling called mollified interaction energy descent (MIED). MIED minimizes a new class of energies on probability measures called mollified interaction energies (MIEs). These energies rely on mollifier functions---smooth approximations of the Dirac delta originated from PDE theory. We show that as the mollifier approaches the Dirac delta, the MIE converges to the chi-square divergence with respect to the target measure and the gradient flow of the MIE agrees with that of the chi-square divergence. Optimizing this energy with proper discretization yields a practical first-order particle-based algorithm for sampling in both unconstrained and constrained domains. We show experimentally that for unconstrained sampling problems our algorithm performs on par with existing particle-based algorithms like SVGD, while for constrained sampling problems our method readily incorporates constrained optimization techniques to handle more flexible constraints with strong performance compared to alternatives.
FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data
Authors: Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie J Su, James Zou
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Algorithmic fairness plays an important role in machine learning and imposing fairness constraints during learning is a common approach. However, many datasets are imbalanced in certain label classes (e.g. "healthy") and sensitive subgroups (e.g. "older patients"). Empirically, this imbalance leads to a lack of generalizability not only of classification but also of fairness properties, especially in over-parameterized models. For example, fairness-aware training may 
ensure equalized odds (EO) on the training data, but EO is far from being satisfied on new users. In this paper, we propose a theoretically-principled, yet {\bf F}lexible approach that is {\bf I}mbalance-{\bf F}airness-{\bf A}ware ({\bf FIFA}). Specifically, FIFA encourages both classification and fairness generalization and can be flexibly combined with many existing fair learning methods with logits-based losses. While our main focus is on EO, FIFA can be directly applied to achieve equalized opportunity (EqOpt); and under certain conditions, it can also be applied to other fairness notions. We demonstrate the power of FIFA by combining it with a popular fair classification algorithm, and the resulting algorithm achieves significantly better fairness generalization on several real-world datasets.
PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm
Authors: Toygun Basaklar, Suat Gumussoy, Umit Ogras
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Multi-objective reinforcement learning (MORL) approaches have emerged to tackle many real-world problems with multiple conflicting objectives by maximizing a joint objective function weighted by a preference vector. These approaches find fixed customized policies corresponding to preference vectors specified during training. However, the design constraints and objectives typically change dynamically in real-life scenarios. Furthermore, storing a policy for each potential preference is not scalable. Hence, obtaining a set of Pareto front solutions for the entire preference space in a given domain with a single training is critical. To this end, we propose a novel MORL algorithm that trains a single universal network to cover the entire preference space scalable to continuous robotic tasks. The proposed approach, Preference-Driven MORL (PD-MORL), utilizes the preferences as guidance to update the network parameters. It also employs a novel parallelization approach to increase sample efficiency. We show that PD-MORL achieves up to 25% larger hypervolume for challenging continuous control tasks and uses an order of magnitude fewer trainable parameters compared to prior approaches.
CircNet: Meshing 3D Point Clouds with Circumcenter Detection
Authors: Huan Lei, Ruitao Leng, Liang Zheng, Hongdong Li
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Reconstructing 3D point clouds into triangle meshes is a key problem in computational geometry and surface reconstruction. Point cloud triangulation solves this problem by providing edge information to the input points. Since no vertex interpolation is involved, it is beneficial to preserve sharp details on the surface. Taking advantage of learning-based techniques in triangulation, existing methods enumerate the complete combinations of candidate triangles, which is both complex and inefficient. In this paper, we leverage the duality between a triangle and its circumcenter, and introduce a deep neural network that detects the circumcenters to achieve point cloud triangulation. Specifically, we introduce multiple anchor priors to divide the neighborhood space of each point. The neural network then learns to predict the presences and locations of circumcenters under the guidance of those anchors. We extract the triangles dual to the detected circumcenters to form a primitive mesh, from which an edge-manifold mesh is produced via simple post-processing. Unlike existing learning-based triangulation methods, the proposed method bypasses an exhaustive enumeration of triangle combinations and local surface parameterization. We validate the efficiency, generalization, and robustness of our method on prominent datasets of both watertight and open surfaces. The code and trained models are provided at \url{https://github.com/Ruitao-L/CircNet}.
Semi-supervised learning with a principled likelihood from a generative model of data curation
Authors: Stoil Krasimirov Ganev, Laurence Aitchison
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: We currently do not have an understanding of semi-supervised learning (SSL) objectives such as pseudo-labelling and entropy minimization as log-likelihoods, which precludes the development of e.g. Bayesian SSL. Here, we note that benchmark image datasets such as CIFAR-10 are carefully curated, and we formulate SSL objectives as a log-likelihood in a generative model of data curation. We show that SSL objectives, from entropy minimization and pseudo-labelling, to state-of-the-art techniques similar to FixMatch can be understood as lower-bounds on our principled log-likelihood. We are thus able to introduce a Bayesian extension of SSL, which gives considerable improvements over standard SSL in the setting of 40 labelled points on CIFAR-10, with performance of $92.2\pm 0.3\%$ vs $88.6\%$ in the original FixMatch paper. Finally, our theory suggests that SSL is effective in part due to the statistical patterns induced by data curation. This provides an explanation of past results which show SSL performs better on clean datasets without any ``out of distribution'' examples. Confirming these results we find that SSL gave much larger performance improvements on curated than on uncurated data, using matched curated and uncurated datasets based on Galaxy Zoo 2.
Is Adversarial Training Really a Silver Bullet for Mitigating Data Poisoning?
Authors: Rui Wen, Zhengyu Zhao, Zhuoran Liu, Michael Backes, Tianhao Wang, Yang Zhang
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Indiscriminate data poisoning can decrease the clean test accuracy of a deep learning model by slightly perturbing its training samples.
There is a consensus that such poisons can hardly harm adversarially-trained (AT) models when the adversarial training budget is no less than the poison budget, i.e., $\epsilon_\mathrm{adv}\geq\epsilon_\mathrm{poi}$. This consensus, however, is challenged in this paper based on our new attack strategy that induces \textit{entangled features} (EntF). The existence of entangled features makes the poisoned data become less useful for training a model, no matter if AT is applied or not. We demonstrate that for attacking a CIFAR-10 AT model under a reasonable setting with $\epsilon_\mathrm{adv}=\epsilon_\mathrm{poi}=8/255$, our EntF yields an accuracy drop of $13.31\%$, which is $7\times$ better than existing methods and equal to discarding $83\%$ training data. We further show the generalizability of EntF to more challenging settings, e.g., higher AT budgets, partial poisoning, unseen model architectures, and stronger (ensemble or adaptive) defenses. We finally provide new insights into the distinct roles of non-robust vs. robust features in poisoning standard vs. AT models and demonstrate the possibility of using a hybrid attack to poison standard and AT models simultaneously. Our code is available at~\url{https://github.com/WenRuiUSTC/EntF}.
Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning
Authors: Chunwei Ma, Zhanghexuan Ji, Ziyun Huang, Yan Shen, Mingchen Gao, Jinhui Xu
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Data-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs). In this paper, we present \emph{iVoro}, a novel framework derived from computational geometry. We found Voronoi Diagram (VD), a classical model for space subdivision, is especially powerful for solving the CIL problem, because VD itself can be constructed favorably in an incremental manner -- the newly added sites (classes) will only affect the proximate classes, making the non-contiguous classes hardly forgettable. Furthermore, we bridge DNN and VD using Power Diagram Reduction, and show that the VD structure can be progressively refined along the phases using a divide-and-conquer algorithm. Moreover, our VD construction is not restricted to the deep feature space, but is also applicable to multiple intermediate feature spaces, promoting VD to be multilayer VD that efficiently captures multi-grained features from DNN. Importantly, \emph{iVoro} is also capable of handling uncertainty-aware test-time Voronoi cell assignment and has exhibited high correlations between geometric uncertainty and predictive accuracy (up to ${\sim}0.9$). Putting everything together, \emph{iVoro} achieves up to $25.26\%$, $37.09\%$, and $33.21\%$ improvements on CIFAR-100, TinyImageNet, and ImageNet-Subset, respectively, compared to the state-of-the-art non-exemplar CIL approaches. In conclusion, \emph{iVoro} enables highly accurate, privacy-preserving, and geometrically interpretable CIL that is particularly useful when cross-phase data sharing is forbidden, e.g. in medical applications.
Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks
Authors: Sravanti Addepalli, Anshul Nasery, Venkatesh Babu Radhakrishnan, Praneeth Netrapalli, Prateek Jain
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Deep Neural Networks are known to be brittle to even minor distribution shifts compared to the training distribution. While one line of work has demonstrated that \emph{Simplicity Bias} (SB) of DNNs -- bias towards learning only the simplest features -- is a key reason for this brittleness, another recent line of work has surprisingly found that diverse/ complex features are indeed learned by the backbone, and their brittleness is due to the linear classification head relying primarily on the simplest features. To bridge the gap between these two lines of work, we first hypothesize and verify that while SB may not altogether preclude learning complex features, it amplifies simpler features over complex ones. Namely, simple features are replicated several times in the learned representations while complex features might not be replicated. This phenomenon, we term \emph{Feature  Replication  Hypothesis}, coupled with the \emph{Implicit Bias} of SGD to converge to maximum margin solutions in the feature space, leads the models to rely mostly on the simple features for classification. To mitigate this bias, we propose \emph{Feature Reconstruction Regularizer (FRR)} to ensure that the learned features can be reconstructed back from the logits. The use of \emph{FRR} in linear layer training (\emph{FRR-L}) encourages the use of more diverse features for classification. We further propose to finetune the full network by freezing the weights of the linear layer trained using \emph{FRR-L}, to refine the learned features, making them more suitable for classification. Using this simple solution, we demonstrate up to 15\% gains in OOD accuracy on the recently introduced semi-synthetic datasets with extreme distribution shifts. Moreover, we demonstrate noteworthy gains over existing SOTA methods on the standard OOD benchmark DomainBed as well.
Minimax Optimal Kernel Operator Learning via Multilevel Training
Authors: Jikai Jin, Yiping Lu, Jose Blanchet, Lexing Ying
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Learning mappings between infinite-dimensional function spaces have achieved empirical success in many disciplines of machine learning, including generative modeling, functional data analysis, causal inference, and multi-agent reinforcement learning. In this paper, we study the statistical limit of learning a Hilbert-Schmidt operator between two infinite-dimensional Sobolev reproducing kernel Hilbert spaces. We establish the information-theoretic lower bound in terms of the Sobolev Hilbert-Schmidt norm and show that a regularization that learns the spectral components below the bias contour and ignores the ones above the variance contour can achieve the optimal learning rate. At the same time, the spectral components between the bias and variance contours give us flexibility in designing computationally feasible machine learning algorithms. Based on this observation, we develop a multilevel kernel operator learning algorithm that is optimal when learning linear operators between infinite-dimensional function spaces.
Denoising Masked Autoencoders Help Robust Classification
Authors: QuanLin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, Liwei Wang, Di He
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: In this paper, we propose a new self-supervised method, which is called denoising masked autoencoders (DMAE), for learning certified robust classifiers of images. In DMAE, we corrupt each image by adding Gaussian noises to each pixel value and randomly masking several patches. A Transformer-based encoder-decoder model is then trained to reconstruct the original image from the corrupted one. In this learning paradigm, the encoder will learn to capture relevant semantics for the downstream tasks, which is also robust to Gaussian additive noises. We show that the pre-trained encoder can naturally be used as the base classifier in Gaussian smoothed models, where we can analytically compute the certified radius for any data point. Although the proposed method is simple, it yields significant performance improvement in downstream classification tasks. We show that the DMAE ViT-Base model, which just uses 1/10 parameters of the model developed in recent work (Carlini et al., 2022), achieves competitive or better certified accuracy in various settings. The DMAE ViT-Large model significantly surpasses all previous results, establishing a new state-of-the-art on ImageNet dataset. We further demonstrate that the pre-trained model has good transferability to the CIFAR-10 dataset, suggesting its wide adaptability. Models and code are available at
https://github.com/quanlin-wu/dmae.
Omnigrok: Grokking Beyond Algorithmic Data
Authors: Ziming Liu, Eric J Michaud, Max Tegmark
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Grokking, the unusual phenomenon for algorithmic datasets where generalization happens long after overfitting the training data, has remained elusive. We aim to understand grokking by analyzing the loss landscapes of neural networks, identifying the mismatch between training and test losses as the cause for grokking. We refer to this as the "LU mechanism" because training and test losses (against model weight norm) typically resemble "L" and "U", respectively. This simple mechanism can nicely explain many aspects of grokking: data size dependence, weight decay dependence, the emergence of representations, etc. Guided by the intuitive picture, we are able to induce grokking on tasks involving images, language and molecules, although the grokking signals are sometimes less dramatic. We attribute the dramatic nature of grokking for algorithmic datasets to representation learning.
TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization
Authors: Xiang Li, Junchi YANG, Niao He
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Adaptive gradient methods have shown their ability to adjust the stepsizes on the fly in a parameter-agnostic manner, and empirically achieve faster convergence for solving minimization problems. When it comes to nonconvex minimax optimization, however, current convergence analyses of gradient descent ascent (GDA) combined with adaptive stepsizes require careful tuning of hyper-parameters and the knowledge of problem-dependent parameters. Such a discrepancy arises from the primal-dual nature of minimax problems and the necessity of delicate time-scale separation between the primal and dual updates in attaining convergence. In this work, we propose a single-loop adaptive GDA algorithm called TiAda for nonconvex minimax optimization that automatically adapts to the time-scale separation. Our algorithm is fully parameter-agnostic and can achieve near-optimal complexities simultaneously in deterministic and stochastic settings of nonconvex-strongly-concave minimax problems. The effectiveness of the proposed method is further justified numerically for a number of machine learning applications.
Competitive Physics Informed Networks 
Authors: Qi Zeng, Yash Kothari, Spencer H Bryngelson, Florian Tobias Schaefer
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE residual as the loss function. This strategy is called "physics-informed neural networks" (PINNs), but it currently cannot produce high-accuracy solutions, typically attaining about $0.1\%$ relative error. We present an adversarial approach that overcomes this limitation, which we call competitive PINNs (CPINNs). CPINNs train a discriminator that is rewarded for predicting mistakes the PINN makes. The discriminator and PINN participate in a zero-sum game with the exact PDE solution as an optimal strategy. This approach avoids squaring the large condition numbers of PDE discretizations, which is the likely reason for failures of previous attempts to decrease PINN errors even on benign problems. Numerical experiments on a Poisson problem show that CPINNs achieve errors four orders of magnitude smaller than the best-performing PINN. We observe relative errors on the order of single-precision accuracy, consistently decreasing with each epoch. To the authors' knowledge, this is the first time this level of accuracy and convergence behavior has been achieved. Additional experiments on the nonlinear Schr{\"o}dinger, Burgers', and Allen--Cahn equation show that the benefits of CPINNs are not limited to linear problems.
Phase2vec: dynamical systems embedding with a physics-informed convolutional network
Authors: Matt Ricci, Noa Moriel, Zoe Piran, Mor Nitzan
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Dynamical systems are found in innumerable forms across the physical and biological sciences, yet all these systems fall naturally into equivalence classes: conservative or dissipative, stable or unstable, compressible or incompressible. Predicting these classes from data remains an essential open challenge in computational physics on which existing time-series classification methods struggle. Here, we propose, phase2vec, an embedding method that learns high-quality, physically-meaningful representations of low-dimensional dynamical systems without supervision. Our embeddings are produced by a convolutional backbone that extracts geometric features from flow data and minimizes a physically-informed vector field reconstruction loss. The trained architecture can not only predict the equations of unseen data, but also produces embeddings that encode meaningful physical properties of input data (e.g. stability of fixed points, conservation of energy, and the incompressibility of flows) more faithfully than standard blackbox classifiers and state-of-the-art time series classification techniques. We additionally apply our embeddings to the analysis of meteorological data, showing we can detect climatically meaningful features. Collectively, our results demonstrate the viability of embedding approaches for the discovery of dynamical features in physical systems.
Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic
Authors: Yulhwa Kim, Jaeyong Jang, Jehun Lee, Jihoon Park, Jeonghoon Kim, Byeongwook Kim, Baeseong park, Se Jung Kwon, Dongsoo Lee, jae-joon kim
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Even though floating point (FP) numbers have been adopted as a de facto standard data format for deep learning computing, the complexity of FP arithmetic impedes a broader deployment of Deep Neural Networks (DNNs). Recent works such as quantization have attempted to replace the FP matrix multiplication (MatMul) of DNNs with simple integer MatMul by transforming the datatypes of both weights and activations into integers. Unfortunately, unlike weight values that are static, it is challenging to represent dynamic activations with integers. In this paper, to simultaneously achieve the accuracy of FP activation and the simplicity of integer arithmetic, we present a method for replacing FP arithmetic with integer one without changing FP activations in the storage format while weights are quantized. The proposed method pre-aligns the significands of FP activations just ahead of the MatMul on-the-fly so that the aligned significands (integers) can be used for the computation. Inspired by an observation that conventional FP arithmetic does not produce precise results due to rounding, we demonstrate that our proposed integer arithmetic-based scheme can produce the same level of errors as that of the FP arithmetic in case DNNs use FP activations and quantized weights. Experimental results show that the hardware based on the proposed scheme shows significant improvement over FP arithmetic-based designs in terms of energy efficiency and throughput-per-area while maintaining a similar level of accuracy.
On the Soft-Subnetwork for Few-Shot Class Incremental Learning
Authors: Haeyong Kang, Jaehong Yoon, Sultan Rizky Hikmawan Madjid, Sung Ju Hwang, Chang D. Yoo
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Inspired by Regularized Lottery Ticket Hypothesis, which states that competitive smooth (non-binary) subnetworks exist within a dense network, we propose a few-shot class-incremental learning method referred to as Soft-SubNetworks (SoftNet). Our objective is to learn a sequence of sessions incrementally, where each session only includes a few training instances per class while preserving the knowledge of the previously learned ones. SoftNet jointly learns the model weights and adaptive non-binary soft masks at a base training session in which each mask consists of the major and minor subnetwork; the former aims to minimize catastrophic forgetting during training, and the latter aims to avoid overfitting to a few samples in each new training session. We provide comprehensive empirical validations demonstrating that our SoftNet effectively tackles the few-shot incremental learning problem by surpassing the performance of state-of-the-art baselines over benchmark datasets.
Red PANDA: Disambiguating Image Anomaly Detection by Removing Nuisance Factors
Authors: Niv Cohen, Jonathan Kahana, Yedid Hoshen
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Anomaly detection methods strive to discover patterns that differ from the norm in a meaningful way. This goal is ambiguous as different human operators may find different attributes meaningful. An image differing from the norm by an attribute such as pose may be considered anomalous by some operators while others may consider the attribute irrelevant. Breaking from previous research, we present a new anomaly detection method that allows operators to exclude an attribute when detecting anomalies. Our approach aims to learn representations which do not contain information regarding such nuisance attributes. Anomaly scoring is performed using a density-based approach. Importantly, our approach does not require specifying the attributes where anomalies could appear, which is typically impossible in anomaly detection, but only attributes to ignore. An empirical investigation is presented verifying the effectiveness of our approach.
Plateau in Monotonic Linear Interpolation --- A "Biased" View of Loss Landscape for Deep Networks
Authors: Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Monotonic linear interpolation (MLI) --- on the line connecting a random initialization with the minimizer it converges to, the loss and accuracy are monotonic --- is a phenomenon that is commonly observed in the training of neural networks. Such a  phenomenon may seem to suggest that optimization of neural networks is easy. In this paper, we show that the MLI property is not necessarily related to the hardness of optimization problems, and empirical observations on MLI for deep neural networks depend heavily on the biases. In particular, we show that interpolating both weights and biases linearly leads to very different influences on the final output, and when different classes have different last-layer biases on a deep network, there will be a long plateau in both the loss and accuracy interpolation (which existing theory of MLI cannot explain). We also show how the last-layer biases for different classes can be different even on a perfectly balanced dataset using a simple model. Empirically we demonstrate that similar intuitions hold on practical networks and realistic datasets.
Learning in temporally structured environments
Authors: Matt Jones, Tyler R. Scott, Mengye Ren, Gamaleldin Fathy Elsayed, Katherine Hermann, David Mayo, Michael Curtis Mozer
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Natural environments have temporal structure at multiple timescales. This property is reflected in biological learning and memory but typically not in machine learning systems. We advance a multiscale learning method in which each weight in a neural network is decomposed as a sum of subweights with different learning and decay rates. Thus knowledge becomes distributed across different timescales, enabling rapid adaptation to task changes while avoiding catastrophic interference. First, we prove previous models that learn at multiple timescales, but with complex coupling between timescales, are equivalent to multiscale learning via a reparameterization that eliminates this coupling. The same analysis yields a new characterization of momentum learning, as a fast weight with a negative learning rate. Second, we derive a model of Bayesian inference over $1/f$ noise, a common temporal pattern in many online learning domains that involves long-range (power law) autocorrelations. The generative side of the model expresses $1/f$ noise as a sum of diffusion processes at different timescales, and the inferential side tracks these latent processes using a Kalman filter. We then derive a variational approximation to the Bayesian model and show how it is an extension of the multiscale learner. The result is an optimizer that can be used as a drop-in replacement in an arbitrary neural network architecture. Third, we evaluate the ability of these methods to handle nonstationarity by testing them in online prediction tasks characterized by $1/f$ noise in the latent parameters. We find that the Bayesian model significantly outperforms online stochastic gradient descent and two batch heuristics that rely preferentially or exclusively on more recent data. Moreover, the variational approximation performs nearly as well as the full Bayesian model, and with memory requirements that are linear in the size of the network.
AnyDA: Anytime Domain Adaptation
Authors: Omprakash Chakraborty, Aadarsh Sahoo, Rameswar Panda, Abir Das
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Unsupervised domain adaptation is an open and challenging problem in computer vision. While existing research shows encouraging results in addressing cross-domain distribution shift on common benchmarks, they are often constrained to testing under a specific target setting, limiting their impact for many real-world applications. In this paper, we introduce a simple yet effective framework for anytime domain adaptation that is executable with dynamic resource constraints to achieve accuracy-efficiency trade-offs under domain-shifts. We achieve this by training a single shared network using both labeled source and unlabeled data, with switchable depth, width and input resolutions on the fly to enable testing under a wide range of computation budgets. Starting with a teacher network trained from a label-rich source domain, we utilize bootstrapped recursive knowledge distillation as a nexus between source and target domains to jointly train the student network with switchable subnetworks. Experiments on multiple datasets well demonstrate the superiority of our approach over state-of-the-art methods.
Hybrid RL: Using both offline and online data can make RL efficient
Authors: Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online interaction. The framework mitigates the challenges that arise in both pure offline and online RL settings, allowing for the design of simple and highly effective algorithms, in both theory and practice. We demonstrate these advantages by adapting the classical Q learning/iteration algorithm to the hybrid setting, which we call Hybrid Q-Learning or Hy-Q. In our theoretical results, we prove that the algorithm is both computationally and statistically efficient whenever the offline dataset supports a high-quality policy and the environment has bounded bilinear rank. Notably, we require no assumptions on the coverage provided by the initial distribution, in contrast with guarantees for policy gradient/iteration methods. In our experimental results, we show that Hy-Q with neural network function approximation outperforms state-of-the-art online, offline, and hybrid RL baselines on challenging benchmarks, including Montezuma’s Revenge.
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
Authors: Michael Crawshaw, Yajie Bao, Mingrui Liu
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract:  Gradient clipping is an important technique for deep neural networks with exploding gradients, such as recurrent neural networks. Recent studies have shown that the loss functions of these networks do not satisfy the conventional smoothness condition, but instead satisfy a relaxed smoothness condition, i.e., the Lipschitz constant of the gradient scales linearly in terms of the gradient norm. Due to this observation, several gradient clipping algorithms have been developed for nonconvex and relaxed-smooth functions. However, the existing algorithms only apply to the single-machine or multiple-machine setting with homogeneous data across machines. It remains unclear how to design provably efficient gradient clipping algorithms in the general Federated Learning (FL) setting with heterogeneous data and limited communication rounds. In this paper, we design EPISODE, the very first algorithm to solve FL problems with heterogeneous data in the nonconvex and relaxed smoothness setting. The key ingredients of the algorithm are two new techniques called \textit{episodic gradient clipping} and \textit{periodic resampled corrections}. At the beginning of each round, EPISODE resamples stochastic gradients from each client and obtains the global averaged gradient, which is used to (1) determine whether to apply gradient clipping for the entire round and (2) construct local gradient corrections for each client. Notably, our algorithm and analysis provide a unified framework for both homogeneous and heterogeneous data under any noise level of the stochastic gradient, and it achieves state-of-the-art complexity results. In particular, we prove that EPISODE can achieve linear speedup in the number of machines, and it requires significantly fewer communication rounds. Experiments on several heterogeneous datasets, including text classification and image classification, show the superior performance of EPISODE over several strong baselines in FL. The code is available at https://github.com/MingruiLiu-ML-Lab/episode.
SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning
Authors: Hao Chen, Ran Tao, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Bhiksha Raj, Marios Savvides
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance. In this paper, we first revisit the popular pseudo-labeling methods via a unified sample weighting formulation and demonstrate the inherent quantity-quality trade-off problem of pseudo-labeling with thresholding, which may prohibit learning. To this end, we propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training, effectively exploiting the unlabeled data. We derive a truncated Gaussian function to weight samples based on their confidence, which can be viewed as a soft version of the confidence threshold. We further enhance the utilization of weakly-learned classes by proposing a uniform alignment approach. In experiments, SoftMatch shows substantial improvements across a wide variety of benchmarks, including image, text, and imbalanced classification.
Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
Authors: Jonas Landman, Slimane Thabet, Constantin Dalyac, Hela Mhiri, Elham Kashefi
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Many applications of quantum computing in the near term rely on variational quantum circuits (VQCs). They have been showcased as a promising model for reaching a quantum advantage in machine learning with current noisy intermediate scale quantum computers (NISQ). It is often believed that the power of VQCs relies on their exponentially large feature space, and extensive works have explored the expressiveness and trainability of VQCs in that regard. In our work, we propose a classical sampling method that can closely approximate most VQCs with Hamiltonian encoding, given only the description of their architecture. It uses the seminal proposal of Random Fourier Features (RFF) and the fact that VQCs can be seen as large Fourier series. We show theoretically and experimentally that models built from exponentially large quantum feature space can be classically reproduced by sampling a few frequencies to build an equivalent low dimensional kernel. Precisely, we show that the number of required samples grows favourably with the size of the quantum spectrum. This tool therefore questions the hope for quantum advantage from VQCs in many cases, but conversely helps to narrow the conditions for their potential success. We expect VQCs with various and complex encoding Hamiltonians, or with large input dimension, to become more robust to classical approximations.
Topology-aware Robust Optimization for Out-of-Distribution Generalization
Authors: Fengchun Qiao, Xi Peng
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Out-of-distribution (OOD) generalization is a challenging machine learning problem yet highly desirable in many high-stake applications. 
Existing methods suffer from overly pessimistic modeling with low generalization confidence. As generalizing to arbitrary test distributions is impossible, we hypothesize that further structure on the topology of distributions is crucial in developing strong OOD resilience. To this end, we propose topology-aware robust optimization (TRO) that seamlessly integrates distributional topology in a principled optimization framework. More specifically, TRO solves two optimization objectives: (1) Topology Learning which explores data manifold to uncover the distributional topology; (2) Learning on Topology which exploits the topology to constrain robust optimization for tightly-bounded generalization risks. We theoretically demonstrate the effectiveness of our approach, and empirically show that it significantly outperforms the state of the arts in a wide range of tasks including classification, regression, and semantic segmentation. Moreover, we empirically find the data-driven distributional topology is consistent with domain knowledge, enhancing the explainability of our approach.
Complexity-Based Prompting for Multi-step Reasoning
Authors: Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, Tushar Khot
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: We study the task of prompting large-scale language models to perform multi-step reasoning. Existing work shows that when prompted with a chain of thoughts (CoT), sequences of short sentences describing intermediate reasoning steps towards a final answer, large language models can generate new reasoning chains and predict answers for new inputs. A central question is which reasoning examples make the most effective prompts. In this work, we propose complexity-based prompting, a simple and effective example selection scheme for multi-step reasoning. We show that prompts with higher reasoning complexity, i.e., chains with more reasoning steps, achieve substantially better performance on math word reasoning tasks over strong baselines. We further extend our complexity-based criteria from prompting (selecting inputs) to decoding (selecting outputs), where we sample multiple reasoning chains from the model, then choose the majority
of generated answers from complex reasoning chains (over simple chains). When used to prompt GPT-3, our approach substantially improves multi-step reasoning accuracy, with an 8.6% absolute improvement on GSM8K, and 6.4% on MathQA. Compared with existing example selection schemes like manual tuning or retrieval-based selection, selection based on reasoning complexity is intuitive, easy to implement, and annotation-efficient. Further results demonstrate the robustness of performance gains from complex prompts under format perturbation and distribution shift.
Continuized Acceleration for Quasar Convex Functions  in Non-Convex Optimization
Authors: Jun-Kun Wang, Andre Wibisono
Published in: International Conference on Learning Representations 2023 (Main Conference Track), 2023

Abstract: Quasar convexity is a condition that allows some first-order methods to efficiently minimize a function even when the optimization landscape is non-convex. Previous works develop near-optimal accelerated algorithms for minimizing this class of functions, however, they require a subroutine of binary search which results in multiple calls to gradient evaluations in each iteration, and consequently the total number of gradient evaluations does not match a known lower bound. In this work, we show that a recently proposed continuized Nesterov acceleration can be applied to minimizing quasar convex functions and achieves the optimal bound with a high probability. Furthermore, we find that the objective functions of training generalized linear models (GLMs) satisfy quasar convexity, which broadens the applicability of the relevant algorithms, while known practical examples of quasar convexity in non-convex learning are sparse in the literature. We also show that if a smooth and one-point strongly convex, Polyak-Lojasiewicz, or quadratic-growth function satisfies quasar convexity, then attaining an accelerated linear rate for minimizing the function is possible under certain conditions, while acceleration is not known in general for these classes of functions.