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0images | Bayesian Estimation of Differential Privacy | ||
0images | Adaptive Estimation of Graphical Models under Total Positivity | ||
0images | GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models | This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning (RL) with large sequence models (such as transformers). With such models, existing systems such as Reverb face considerable bottlenecks in memory, computation, and communication. GEAR, ho... | |
0images | Disentangled Multi-Fidelity Deep Bayesian Active Learning | To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a direct mapping from input parameters to simulation outputs at the highest fidelity by actively acquiring data from multiple fidelity levels. H... | |
0images | Understand and Modularize Generator Optimization in ELECTRA-style Pretraining | Despite the effectiveness of ELECTRA-style pre-training, their performance is dependent on the careful selection of the model size for the auxiliary generator, leading to high trial-and-error costs. In this paper, we present the first systematic study of this problem. Our theoretical investigation highlights the import... | |
0images | Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP | ||
0images | Taming graph kernels with random features | We introduce in this paper the mechanism of graph random features (GRFs). GRFs can be used to construct unbiased randomized estimators of several important kernels defined on graphs' nodes, in particular the regularized Laplacian kernel. As regular RFs for non-graph kernels, they provide means to scale up kernel method... | |
0images | Autoregressive Diffusion Model for Graph Generation | Diffusion-based graph generative models have recently obtained promising results for graph generation. However, existing diffusion-based graph generative models are mostly one-shot generative models that apply Gaussian diffusion in the dequantized adjacency matrix space. Such a strategy can suffer from difficulty in mo... | |
0images | Differentiable Tree Operations Promote Compositional Generalization | In the context of structure-to-structure transformation tasks, learning sequences of discrete symbolic operations poses significant challenges due to their non-differentiability. To facilitate the learning of these symbolic sequences, we introduce a differentiable tree interpreter that compiles high-level symbolic tree... | |
0images | Can Neural Network Memorization Be Localized? | Recent efforts at explaining the interplay of memorization and generalization in deep overparametrized networks have posited that neural networks <em>memorize</em> ``hard'' examples in the final few layers of the model. Memorization refers to the ability to correctly predict on <em>atypical</em> examples of the trainin... | |
0images | Bayesian Design Principles for Frequentist Sequential Learning | We develop a general theory to optimize the frequentist regret for sequential learning problems, where efficient bandit and reinforcement learning algorithms can be derived from unified Bayesian principles. We propose a novel optimization approach to create "algorithmic beliefs" at each round, and use Bayesian posterio... | |
0images | Domain Adaptation for Time Series Under Feature and Label Shifts | Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents challenges due to the dynamic temporal structure variations across domains. This leads to feature shifts in the time and frequency represen... | |
0images | On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network | ||
0images | Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding | Visually-situated language is ubiquitous---sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to this diversity, previous work has typically relied on domain-specific recipes with limited sharing of the underlying data, model architectures,... | |
0images | JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift | ||
0images | Contextual Reliability: When Different Features Matter in Different Contexts | Deep neural networks often fail catastrophically by relying on spurious correlations. Most prior work assumes a clear dichotomy into spurious and reliable features; however, this is often unrealistic. For example, most of the time we do not want an autonomous car to simply copy the speed of surrounding cars---we don't ... | |
0images | Provably Learning Object-Centric Representations | Learning structured representations of the visual world in terms of objects promises to significantly improve the generalization abilities of current machine learning models. While recent efforts to this end have shown promising empirical progress, a theoretical account of when unsupervised object-centric representatio... | |
0images | Fast Sampling of Diffusion Models via Operator Learning | Diffusion models have found widespread adoption in various areas. However, their sampling process is slow because it requires hundreds to thousands of network evaluations to emulate a continuous process defined by differential equations. In this work, we use neural operators, an efficient method to solve the probabilit... | |
0images | Unsupervised Out-of-Distribution Detection with Diffusion Inpainting | Unsupervised out-of-distribution detection (OOD) seeks to identify out-of-domain data by learning only from unlabeled in-domain data. We present a novel approach for this task -- Lift, Map, Detect (LMD) -- that leverages recent advancement in diffusion models. Diffusion models are one type of generative models. At thei... | |
0images | Sequence Modeling with Multiresolution Convolutional Memory | ||
0images | The Hessian perspective into the Nature of Convolutional Neural Networks | While Convolutional Neural Networks (CNNs) have long been investigated and applied, as well as theorized, we aim to provide a slightly different perspective into their nature --- through the perspective of their Hessian maps. The reason is that the loss Hessian captures the pairwise interaction of parameters and theref... | |
0images | Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks | Some extremely low-dimensional yet crucial geometric eigen-lengths often determine the success of some geometric tasks. For example, the <em>height</em> of an object is important to measure to check if it can fit between the shelves of a cabinet, while the <em>width</em> of a couch is crucial when trying to move it thr... | |
0images | Improved Learning-Augmented Algorithms for the Multi-Option Ski Rental Problem via Best-Possible Competitive Analysis | In this paper, we present improved learning-augmented algorithms for the multi-option ski rental problem. Learning-augmented algorithms take ML predictions as an added part of the input and incorporates these predictions in solving the given problem. Due to their unique strength that combines the power of ML prediction... | |
0images | On Regularization and Inference with Label Constraints | Prior knowledge and symbolic rules in machine learning are often expressed in the form of label constraints, especially in structured prediction problems. In this work, we compare two common strategies for encoding label constraints in a machine learning pipeline, <em>regularization with constraints</em> and <em>constr... | |
0images | Simple Disentanglement of Style and Content in Visual Representations | Learning visual representations with interpretable features, i.e., disentangled representations, remains a challenging problem. Existing methods demonstrate some success but are hard to apply to large-scale vision datasets like ImageNet. In this work, we propose a simple post-processing framework to disentangle content... | |
0images | Beyond the Edge of Stability via Two-step Gradient Updates | Gradient Descent (GD) is a powerful workhorse of modern machine learning thanks to its scalability and efficiency in high-dimensional spaces. Its ability to find local minimisers is only guaranteed for losses with Lipschitz gradients, where it can be seen as a 'bona-fide' discretisation of an underlying gradient flow. ... | |
0images | On the Role of Attention in Prompt-tuning | Prompt-tuning is an emerging strategy to adapt large language models (LLM) to downstream tasks by learning a (soft-)prompt parameter from data. Despite its success in LLMs, there is limited theoretical understanding of the power of prompt-tuning and the role of the attention mechanism in prompting. In this work, we exp... | |
0images | Hyperbolic Representation Learning: Revisiting and Advancing | The non-Euclidean geometry of hyperbolic spaces has recently garnered considerable attention in the realm of representation learning. Current endeavors in hyperbolic representation largely presuppose that the underlying hierarchies can be automatically inferred and preserved through the adaptive optimization process. T... | |
0images | MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior | We introduce MABe22, a large-scale, multi-agent video and trajectory benchmark to assess the quality of learned behavior representations. This dataset is collected from a variety of biology experiments, and includes triplets of interacting mice (4.7 million frames video+pose tracking data, 10 million frames pose only),... | |
0images | Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory | ||
0images | MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks | Physics-informed Neural Networks (PINNs) have recently achieved remarkable progress in solving Partial Differential Equations (PDEs) in various fields by minimizing a weighted sum of PDE loss and boundary loss. However, there are several critical challenges in the training of PINNs, including the lack of theoretical fr... | |
0images | Internally Rewarded Reinforcement Learning | ||
0images | Slot-VAE: Object-Centric Scene Generation with Slot Attention | Slot attention has shown remarkable object-centric representation learning performance in computer vision tasks without requiring any supervision. Despite its object-centric binding ability brought by compositional modelling, as a deterministic module, slot attention lacks the ability to generate novel scenes. In this ... | |
0images | Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators | In model-based reinforcement learning (MBRL), policy gradients can be estimated either by derivative-free RL methods, such as likelihood ratio gradients (LR), or by backpropagating through a differentiable model via reparameterization gradients (RP). Instead of using one or the other, the Total Propagation (TP) algorit... | |
0images | H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features | Deep Neural Networks (DNNs) are one of the most powerful tools for prediction, but many of them implicitly assume that the data are statistically independent. However, in the real world, it is common for large-scale data to be clustered with temporal-spatial correlation structures. Variational approaches and integrated... | |
0images | Predicting Rare Events by Shrinking Towards Proportional Odds | Training classifiers is difficult with severe class imbalance, but many rare events are the culmination of a sequence with much more common intermediate outcomes. For example, in online marketing a user first sees an ad, then may click on it, and finally may make a purchase; estimating the probability of purchases is d... | |
0images | Internet Explorer: Targeted Representation Learning on the Open Web | Vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets. These general-purpose models only capture the knowledge within their pre-training datasets, which are tiny, out-of-date snapshots of the Internet---where billions of images are uploaded each day. We suggest an alte... | |
0images | Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies | We introduce a new type of test, called a Turing Experiment (TE), for evaluating to what extent a given language model, such as GPT models, can simulate different aspects of human behavior. A TE can also reveal consistent distortions in a language model’s simulation of a specific human behavior. Unlike the Turing Test,... | |
0images | Robust and private stochastic linear bandits | In this paper, we study the stochastic linear bandit problem under the additional requirements of <em>differential privacy</em>, <em>robustness</em> and <em>batched observations</em>. In particular, we assume an adversary randomly chooses a constant fraction of the observed rewards in each batch, replacing them with ar... | |
0images | High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors | ||
0images | Learning Temporally AbstractWorld Models without Online Experimentation | Agents that can build temporally abstract representations of their environment are better able to understand their world and make plans on extended time scales, with limited computational power and modeling capacity. However, existing methods for automatically learning temporally abstract world models usually require m... | |
0images | IRNeXt: Rethinking Convolutional Network Design for Image Restoration | We present IRNeXt, a simple yet effective convolutional network architecture for image restoration. Recently, Transformer models have dominated the field of image restoration due to the powerful ability of modeling long-range pixels interactions. In this paper, we excavate the potential of the convolutional neural netw... | |
0images | TabLeak: Tabular Data Leakage in Federated Learning | While federated learning (FL) promises to preserve privacy, recent works in the image and text domains have shown that training updates leak private client data. However, most high-stakes applications of FL (e.g., in healthcare and finance) use tabular data, where the risk of data leakage has not yet been explored. A s... | |
0images | Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning | Learning to predict reliable characteristic orientations of 3D point clouds is an important yet challenging problem, as different point clouds of the same class may have largely varying appearances. In this work, we introduce a novel method to decouple the shape geometry and semantics of the input point cloud to achiev... | |
0images | Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search | Symbolic regression (SR) is the problem of learning a symbolic expression from numerical data. Recently, deep neural models trained on procedurally-generated synthetic datasets showed competitive performance compared to more classical Genetic Programming (GP) ones. Unlike their GP counterparts, these neural approaches ... | |
0images | Mixing Predictions for Online Metric Algorithms | ||
0images | Approximate Causal Effect Identification under Weak Confounding | Causal effect estimation has been studied by many researchers when only observational data is available. Sound and complete algorithms have been developed for pointwise estimation of identifiable causal queries. For non-identifiable causal queries, researchers developed polynomial programs to estimate tight bounds on c... | |
0images | Bootstrap in High Dimension with Low Computation | The bootstrap is a popular data-driven method to quantify statistical uncertainty, but for modern high-dimensional problems, it could suffer from huge computational costs due to the need to repeatedly generate resamples and refit models. We study the use of bootstraps in high-dimensional environments with a small numbe... | |
0images | Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression | Perturbative availability poisoning (PAP) adds small changes to images to prevent their use for model training. Current research adopts the belief that practical and effective approaches to countering such poisons do not exist. In this paper, we argue that it is time to abandon this belief. We present extensive experim... | |
0images | Proper Losses for Discrete Generative Models | We initiate the study of proper losses for evaluating generative models in the discrete setting. Unlike traditional proper losses, we treat both the generative model and the target distribution as black-boxes, only assuming ability to draw i.i.d. samples. We define a loss to be black-box proper if the generative distri... | |
0images | SpotEM: Efficient Video Search for Episodic Memory | The goal in episodic memory (EM) is to search a long egocentric video to answer a natural language query (e.g., “where did I leave my purse?”). Existing EM methods exhaustively extract expensive fixed-length clip features to look everywhere in the video for the answer, which is infeasible for long wearable-camera video... | |
0images | DRCFS: Doubly Robust Causal Feature Selection | Knowing the features of a complex system that are highly relevant to a particular target variable is of fundamental interest in many areas of science. Existing approaches are often limited to linear settings, sometimes lack guarantees, and in most cases, do not scale to the problem at hand, in particular to images. We ... | |
0images | Tractable Control for Autoregressive Language Generation | ||
0images | Open-Vocabulary Universal Image Segmentation with MaskCLIP | In this paper, we tackle an emerging computer vision task, open-vocabulary universal image segmentation, that aims to perform semantic/instance/panoptic segmentation (background semantic labeling + foreground instance segmentation) for arbitrary categories of text-based descriptions in inference time. We first build a ... | |
0images | Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data | In this work we perform symbolic discovery of differential operators in a situation where there is sparse experimental data. This small data regime in machine learning can be made tractable by providing our algorithms with prior information about the underlying dynamics. Physics Informed Neural Networks (PINNs) have be... | |
0images | Partial Optimality in Cubic Correlation Clustering | The higher-order correlation clustering problem is an expressive model, and recently, local search heuristics have been proposed for several applications. Certifying optimality, however, is NP-hard and practically hampered already by the complexity of the problem statement. Here, we focus on establishing partial optima... | |
0images | InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models | While diffusion models excel at generating high-quality samples, their latent variables typically lack semantic meaning and are not suitable for representation learning. Here, we propose InfoDiffusion, an algorithm that augments diffusion models with low-dimensional latent variables that capture high-level factors of v... | |
0images | A Large-Scale Study of Probabilistic Calibration in Neural Network Regression | Accurate probabilistic predictions are essential for optimal decision making. While neural network miscalibration has been studied primarily in classification, we investigate this in the less-explored domain of regression. We conduct the largest empirical study to date to assess the probabilistic calibration of neural ... | |
0images | Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows | Training normalizing flow generative models can be challenging due to the need to calculate computationally expensive determinants of Jacobians. This paper studies the likelihood-free training of flows and proposes the energy objective, an alternative sample-based loss based on proper scoring rules. The energy objectiv... | |
0images | When do Minimax-fair Learning and Empirical Risk Minimization Coincide? | Minimax-fair machine learning minimizes the error for the worst-off group. However, empirical evidence suggests that when sophisticated models are trained with standard empirical risk minimization (ERM), they often have the same performance on the worst-off group as a minimax-trained model. Our work makes this counter-... | |
0images | Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables | In recent years, the community of 'explainable artificial intelligence' (XAI) has created a vast body of methods to bridge a perceived gap between model 'complexity' and 'interpretability'. However, a concrete problem to be solved by XAI methods has not yet been formally stated. As a result, XAI methods are lacking the... | |
0images | Probabilistic Imputation for Time-series Classification with Missing Data | Multivariate time series data for real-world applications typically contain a significant amount of missing values. The dominant approach for classification with such missing values is to impute them heuristically with specific values (zero, mean, values of adjacent time-steps) or learnable parameters. However, these s... | |
0images | MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations | We study a new paradigm for sequential decision making, called offline policy learning from observations (PLfO). Offline PLfO aims to learn policies using datasets with substandard qualities: 1) only a subset of trajectories is labeled with rewards, 2) labeled trajectories may not contain actions, 3) labeled trajectori... | |
0images | Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees | ||
0images | Multi-Agent Learning from Learners | A large body of the "Inverse Reinforcement Learning" (IRL) literature focuses on recovering the reward function from a set of demonstrations of an expert agent who acts optimally or noisily optimally. Nevertheless, some recent works move away from the optimality assumption to study the "Learning from a Learner (LfL)" p... | |
0images | Cut your Losses with Squentropy | Nearly all practical neural models for classification are trained using the cross-entropy loss. Yet this ubiquitous choice is supported by little theoretical or empirical evidence. Recent work (Hui & Belkin, 2020) suggests that training using the (rescaled) square loss is often superior in terms of the classificati... | |
0images | Sparse Learning of Dynamical Systems in RKHS: An Operator-Theoretic Approach | ||
0images | Conformal Prediction with Missing Values | Conformal prediction is a theoretically grounded framework for constructing predictive intervals. We study conformal prediction with missing values in the covariates -- a setting that brings new challenges to uncertainty quantification. We first show that the marginal coverage guarantee of conformal prediction holds on... | |
0images | Weakly Supervised Regression with Interval Targets | This paper investigates an interesting weakly supervised regression setting called regression with interval targets (RIT). Although some of the previous methods on relevant regression settings can be adapted to RIT, they are not statistically consistent, and thus their empirical performance is not guaranteed. In this p... | |
0images | DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm | ||
0images | Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains | ||
0images | A Statistical Perspective on Retrieval-Based Models | Many modern high-performing machine learning models increasingly rely on scaling up models, e.g., transformer networks. Simultaneously, a parallel line of work aims to improve the model performance by augmenting an input instance with other (labeled) instances during inference. Examples of such augmentations include ta... | |
0images | Adaptive IMLE for Few-shot Pretraining-free Generative Modelling | Despite their success on large datasets, GANs have been difficult to apply in the few-shot setting, where only a limited number of training examples are provided. Due to mode collapse, GANs tend to ignore some training examples, causing overfitting to a subset of the training dataset, which is small in the first place.... | |
0images | Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision | A neural network consisting of piecewise affine building blocks, such as fully-connected layers and ReLU activations, is itself a piecewise affine function supported on a polyhedral complex. This complex has been previously studied to characterize theoretical properties of neural networks, but, in practice, extracting ... | |
0images | Task-Specific Skill Localization in Fine-tuned Language Models | ||
0images | Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator | Current approaches for training robust models are typically tailored to scenarios where data variations are accessible in the training set. While shown effective in achieving robustness to these foreseen variations, these approaches are ineffective in learning <em>unforeseen</em> robustness, i.e., robustness to data va... | |
0images | Fast Federated Machine Unlearning with Nonlinear Functional Theory | Federated machine unlearning (FMU) aims to remove the influence of a specified subset of training data upon request from a trained federated learning model. Despite achieving remarkable performance, existing FMU techniques suffer from inefficiency due to two sequential operations of training and retraining/unlearning o... | |
0images | Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories | ||
0images | BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning | The ability of deep neural networks to continually learn and adapt to a sequence of tasks has remained challenging due to catastrophic forgetting of previously learned tasks. Humans, on the other hand, have a remarkable ability to acquire, assimilate, and transfer knowledge across tasks throughout their lifetime withou... | |
0images | On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning | Recently, unsupervised representation learning (URL) has improved the sample efficiency of Reinforcement Learning (RL) by pretraining a model from a large unlabeled dataset. The underlying principle of these methods is to learn temporally predictive representations by predicting future states in the latent space. Howev... | |
0images | Feature learning in deep classifiers through Intermediate Neural Collapse | In this paper, we conduct an empirical study of the feature learning process in deep classifiers. Recent research has identified a training phenomenon called Neural Collapse (NC), in which the top-layer feature embeddings of samples from the same class tend to concentrate around their means, and the top layer's weights... | |
0images | Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows | ||
0images | Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data | Source separation involves the ill-posed problem of retrieving a set of source signals that have been observed through a mixing operator. Solving this problem requires prior knowledge, which is commonly incorporated by imposing regularity conditions on the source signals, or implicitly learned through supervised or uns... | |
0images | Doubly Optimal No-Regret Learning in Monotone Games | ||
0images | The Test of Tests: A Framework for Differentially Private Hypothesis Testing | We present a generic framework for creating differentially private versions of any hypothesis test in a black-box way. We analyze the resulting tests analytically and experimentally. Most crucially, we show good practical performance for small data sets, showing that at ε = 1 we only need 5-6 times as much data as in t... | |
0images | Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries | Deep ensembles (DE) have been successful in improving model performance by learning diverse members via the stochasticity of random initialization. While recent works have attempted to promote further diversity in DE via hyperparameters or regularizing loss functions, these methods primarily still rely on a stochastic ... | |
0images | Hidden Symmetries of ReLU Networks | ||
0images | Bootstrapped Representations in Reinforcement Learning | In reinforcement learning (RL), state representations are key to dealing with large or continuous state spaces. While one of the promises of deep learning algorithms is to automatically construct features well-tuned for the task they try to solve, such a representation might not emerge from end-to-end training of deep ... | |
0images | New metrics and search algorithms for weighted causal DAGs | Recovering causal relationships from data is an important problem. Using observational data, one can typically only recover causal graphs up to a Markov equivalence class and additional assumptions or interventional data are needed for complete recovery. In this work, under some standard assumptions, we study causal gr... | |
0images | Exact Inference in High-order Structured Prediction | In this paper, we study the problem of inference in high-order structured prediction tasks. In the context of Markov random fields, the goal of a high-order inference task is to maximize a score function on the space of labels, and the score function can be decomposed into sum of unary and high-order potentials. We app... | |
0images | Path Neural Networks: Expressive and Accurate Graph Neural Networks | Graph neural networks (GNNs) have recently become the standard approach for learning with graph-structured data. Prior work has shed light into their potential, but also their limitations. Unfortunately, it was shown that standard GNNs are limited in their expressive power. These models are no more powerful than the 1-... | |
0images | On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization | Mean-field theory is widely used in theoretical studies of neural networks. In this paper, we analyze the role of depth in the concentration of mean-field predictions for Gram matrices of hidden representations in deep multilayer perceptron (MLP) with batch normalization (BN) at initialization. It is postulated that th... | |
0images | Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression | Contrastive learning (CL) has emerged as a powerful technique for representation learning, with or without label supervision. However, supervised CL is prone to collapsing representations of subclasses within a class by not capturing all their features, and unsupervised CL may suppress harder class-relevant features by... | |
0images | Hyperparameters in Reinforcement Learning and How To Tune Them | In order to improve reproducibility, deep reinforcement learning (RL) has been adopting better scientific practices such as standardized evaluation metrics and reporting. However, the process of hyperparameter optimization still varies widely across papers, which makes it challenging to compare RL algorithms fairly. In... | |
0images | Towards Constituting Mathematical Structures for Learning to Optimize | Learning to Optimize (L2O), a technique that utilizes machine learning to learn an optimization algorithm automatically from data, has gained arising attention in recent years. A generic L2O approach parameterizes the iterative update rule and learns the update direction as a black-box network. While the generic approa... | |
0images | Identifiability and Generalizability in Constrained Inverse Reinforcement Learning | Two main challenges in Reinforcement Learning (RL) are designing appropriate reward functions and ensuring the safety of the learned policy. To address these challenges, we present a theoretical framework for Inverse Reinforcement Learning (IRL) in constrained Markov decision processes. From a convex-analytic perspecti... | |
0images | Adaptive Whitening in Neural Populations with Gain-modulating Interneurons | Statistical whitening transformations play a fundamental role in many computational systems, and may also play an important role in biological sensory systems. Existing neural circuit models of adaptive whitening operate by modifying synaptic interactions; however, such modifications would seem both too slow and insuff... | |
0images | Optimizing the Collaboration Structure in Cross-Silo Federated Learning | In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized. Through utilizing more training data, FL suffers from the potential negative transfer problem: the global FL model may even perform worse than the models trained with local data onl... | |
0images | Smart Initial Basis Selection for Linear Programs | The simplex method, introduced by Dantzig more than half a century ago, is still to date one of the most efficient methods for solving large-scale linear programming (LP) problems. While the simplex method is known to have the finite termination property under mild assumptions, the number of iterations until optimality... | |
0images | From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks |
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