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08/17/2023
Distributed Extra-gradient with Optimal Complexity and Communication Guarantees
We consider monotone variational inequality (VI) problems in multi-GPU s...
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06/27/2023
Semi Bandit Dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees
In this work, we introduce a new variant of online gradient descent, whi...
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06/08/2023
Federated Learning under Covariate Shifts with Generalization Guarantees
This paper addresses intra-client and inter-client covariate shifts in f...
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05/30/2023
Benign Overfitting in Deep Neural Networks under Lazy Training
This paper focuses on over-parameterized deep neural networks (DNNs) wit...
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04/25/2023
Provable benefits of general coverage conditions in efficient online RL with function approximation
In online reinforcement learning (RL), instead of employing standard str...
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02/20/2023
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
This paper introduces a new extragradient-type algorithm for a class of ...
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02/17/2023
Solving stochastic weak Minty variational inequalities without increasing batch size
This paper introduces a family of stochastic extragradient-type algorith...
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02/17/2023
Revisiting adversarial training for the worst-performing class
Despite progress in adversarial training (AT), there is a substantial ga...
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01/10/2023
Min-Max Optimization Made Simple: Approximating the Proximal Point Method via Contraction Maps
In this paper we present a first-order method that admits near-optimal c...
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11/03/2022
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization
We propose an adaptive variance-reduction method, called AdaSpider, for ...
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11/03/2022
Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods
This work proposes a universal and adaptive second-order method for mini...
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09/22/2022
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning
While Reinforcement Learning (RL) aims to train an agent from a reward f...
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09/22/2022
Proximal Point Imitation Learning
This work develops new algorithms with rigorous efficiency guarantees fo...
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09/16/2022
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study
Neural tangent kernel (NTK) is a powerful tool to analyze training dynam...
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09/15/2022
Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration
This paper provides a theoretical study of deep neural function approxim...
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09/15/2022
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)
We study the average robustness notion in deep neural networks in (selec...
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09/15/2022
Generalization Properties of NAS under Activation and Skip Connection Search
Neural Architecture Search (NAS) has fostered the automatic discovery of...
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09/15/2022
Sound and Complete Verification of Polynomial Networks
Polynomial Networks (PNs) have demonstrated promising performance on fac...
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06/14/2022
Adversarial Audio Synthesis with Complex-valued Polynomial Networks
Time-frequency (TF) representations in audio synthesis have been increas...
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06/13/2022
No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation
We examine the problem of regret minimization when the learner is involv...
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06/08/2022
Learning in games from a stochastic approximation viewpoint
We develop a unified stochastic approximation framework for analyzing th...
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04/06/2022
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize
In this paper, we propose a new, simplified high probability analysis of...
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03/08/2022
Score matching enables causal discovery of nonlinear additive noise models
This paper demonstrates how to recover causal graphs from the score of t...
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02/27/2022
The Spectral Bias of Polynomial Neural Networks
Polynomial neural networks (PNNs) have been recently shown to be particu...
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02/26/2022
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization
We propose a stochastic conditional gradient method (CGM) for minimizing...
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02/10/2022
Controlling the Complexity and Lipschitz Constant improves polynomial nets
While the class of Polynomial Nets demonstrates comparable performance t...
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01/19/2022
On the Complexity of a Practical Primal-Dual Coordinate Method
We prove complexity bounds for the primal-dual algorithm with random ext...
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11/02/2021
Subquadratic Overparameterization for Shallow Neural Networks
Overparameterization refers to the important phenomenon where the width ...
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11/01/2021
STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization
In this work we investigate stochastic non-convex optimization problems ...
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10/28/2021
A first-order primal-dual method with adaptivity to local smoothness
We consider the problem of finding a saddle point for the convex-concave...
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10/13/2021
On the Double Descent of Random Features Models Trained with SGD
We study generalization properties of random features (RF) regression in...
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09/17/2021
Self-Supervised Neural Architecture Search for Imbalanced Datasets
Neural Architecture Search (NAS) provides state-of-the-art results when ...
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10/13/2020
Regret minimization in stochastic non-convex learning via a proximal-gradient approach
Motivated by applications in machine learning and operations research, w...
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07/13/2020
Random extrapolation for primal-dual coordinate descent
We introduce a randomly extrapolated primal-dual coordinate descent meth...
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07/07/2020
Conditional gradient methods for stochastically constrained convex minimization
We propose two novel conditional gradient-based methods for solving stru...
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07/02/2020
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch
We study the inverse reinforcement learning (IRL) problem under the tran...
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07/02/2020
Double-Loop Unadjusted Langevin Algorithm
A well-known first-order method for sampling from log-concave probabilit...
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07/02/2020
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
We demonstrate two new important properties of the 1-path-norm of shallo...
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07/01/2020
Interaction-limited Inverse Reinforcement Learning
This paper proposes an inverse reinforcement learning (IRL) framework to...
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06/23/2020
Environment Shaping in Reinforcement Learning using State Abstraction
One of the central challenges faced by a reinforcement learning (RL) age...
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06/19/2020
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
This paper analyzes the trajectories of stochastic gradient descent (SGD...
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06/16/2020
The limits of min-max optimization algorithms: convergence to spurious non-critical sets
Compared to minimization problems, the min-max landscape in machine lear...
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06/11/2020
Convergence of adaptive algorithms for weakly convex constrained optimization
We analyze the adaptive first order algorithm AMSGrad, for solving a con...
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04/18/2020
Lipschitz constant estimation of Neural Networks via sparse polynomial optimization
We introduce LiPopt, a polynomial optimization framework for computing i...
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03/21/2020
A new regret analysis for Adam-type algorithms
In this paper, we focus on a theory-practice gap for Adam and its varian...
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02/17/2020
A Newton Frank-Wolfe Method for Constrained Self-Concordant Minimization
We demonstrate how to scalably solve a class of constrained self-concord...
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02/14/2020
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
We introduce a sampling perspective to tackle the challenging task of tr...
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12/01/2019
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
This article reviews recent advances in multi-agent reinforcement learni...
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10/30/2019
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization
We propose a novel adaptive, accelerated algorithm for the stochastic co...
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10/09/2019
