b'Volkan Cevher'

research

∙ 08/17/2023

Distributed Extra-gradient with Optimal Complexity and Communication Guarantees

We consider monotone variational inequality (VI) problems in multi-GPU s...

0 Ali Ramezani-Kebrya, et al. ∙

research

∙ 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...

0 Ioannis Panageas, et al. ∙

research

∙ 06/08/2023

Federated Learning under Covariate Shifts with Generalization Guarantees

This paper addresses intra-client and inter-client covariate shifts in f...

0 Ali Ramezani-Kebrya, et al. ∙

research

∙ 05/30/2023

Benign Overfitting in Deep Neural Networks under Lazy Training

This paper focuses on over-parameterized deep neural networks (DNNs) wit...

0 Zhenyu Zhu, et al. ∙

research

∙ 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...

0 Fanghui Liu, et al. ∙

research

∙ 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 ...

0 Thomas Pethick, et al. ∙

research

∙ 02/17/2023

Solving stochastic weak Minty variational inequalities without increasing batch size

This paper introduces a family of stochastic extragradient-type algorith...

0 Thomas Pethick, et al. ∙

research

∙ 02/17/2023

Revisiting adversarial training for the worst-performing class

Despite progress in adversarial training (AT), there is a substantial ga...

0 Thomas Pethick, et al. ∙

research

∙ 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...

0 Volkan Cevher, et al. ∙

research

∙ 11/03/2022

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

We propose an adaptive variance-reduction method, called AdaSpider, for ...

0 Ali Kavis, et al. ∙

research

∙ 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...

0 Kimon Antonakopoulos, et al. ∙

research

∙ 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...

0 Paul Rolland, et al. ∙

research

∙ 09/22/2022

Proximal Point Imitation Learning

This work develops new algorithms with rigorous efficiency guarantees fo...

0 Luca Viano, et al. ∙

research

∙ 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...

0 Yongtao Wu, et al. ∙

research

∙ 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...

0 Fanghui Liu, et al. ∙

research

∙ 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...

0 Zhenyu Zhu, et al. ∙

research

∙ 09/15/2022

Generalization Properties of NAS under Activation and Skip Connection Search

Neural Architecture Search (NAS) has fostered the automatic discovery of...

0 Zhenyu Zhu, et al. ∙

research

∙ 09/15/2022

Sound and Complete Verification of Polynomial Networks

Polynomial Networks (PNs) have demonstrated promising performance on fac...

0 Elías Abad-Rocamora, et al. ∙

research

∙ 06/14/2022

Adversarial Audio Synthesis with Complex-valued Polynomial Networks

Time-frequency (TF) representations in audio synthesis have been increas...

0 Yongtao Wu, et al. ∙

research

∙ 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...

0 Yu-Guan Hsieh, et al. ∙

research

∙ 06/08/2022

Learning in games from a stochastic approximation viewpoint

We develop a unified stochastic approximation framework for analyzing th...

0 Panayotis Mertikopoulos, et al. ∙

research

∙ 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...

0 Ali Kavis, et al. ∙

research

∙ 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...

2 Paul Rolland, et al. ∙

research

∙ 02/27/2022

The Spectral Bias of Polynomial Neural Networks

Polynomial neural networks (PNNs) have been recently shown to be particu...

0 Moulik Choraria, et al. ∙

research

∙ 02/26/2022

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

We propose a stochastic conditional gradient method (CGM) for minimizing...

0 Gideon Dresdner, et al. ∙

research

∙ 02/10/2022

Controlling the Complexity and Lipschitz Constant improves polynomial nets

While the class of Polynomial Nets demonstrates comparable performance t...

0 Zhenyu Zhu, et al. ∙

research

∙ 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...

0 Ahmet Alacaoglu, et al. ∙

research

∙ 11/02/2021

Subquadratic Overparameterization for Shallow Neural Networks

Overparameterization refers to the important phenomenon where the width ...

0 ChaeHwan Song, et al. ∙

research

∙ 11/01/2021

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

In this work we investigate stochastic non-convex optimization problems ...

0 Kfir Y. Levy, et al. ∙

research

∙ 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...

0 Maria-Luiza Vladarean, et al. ∙

research

∙ 10/13/2021

On the Double Descent of Random Features Models Trained with SGD

We study generalization properties of random features (RF) regression in...

0 Fanghui Liu, et al. ∙

research

∙ 09/17/2021

Self-Supervised Neural Architecture Search for Imbalanced Datasets

Neural Architecture Search (NAS) provides state-of-the-art results when ...

0 Aleksandr Timofeev, et al. ∙

research

∙ 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...

0 Nadav Hallak, et al. ∙

research

∙ 07/13/2020

Random extrapolation for primal-dual coordinate descent

We introduce a randomly extrapolated primal-dual coordinate descent meth...

0 Ahmet Alacaoglu, et al. ∙

research

∙ 07/07/2020

Conditional gradient methods for stochastically constrained convex minimization

We propose two novel conditional gradient-based methods for solving stru...

0 Maria-Luiza Vladarean, et al. ∙

research

∙ 07/02/2020

Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch

We study the inverse reinforcement learning (IRL) problem under the tran...

0 Luca Viano, et al. ∙

research

∙ 07/02/2020

Double-Loop Unadjusted Langevin Algorithm

A well-known first-order method for sampling from log-concave probabilit...

0 Paul Rolland, et al. ∙

research

∙ 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...

0 Fabian Latorre, et al. ∙

research

∙ 07/01/2020

Interaction-limited Inverse Reinforcement Learning

This paper proposes an inverse reinforcement learning (IRL) framework to...

0 martin-troussard, et al. ∙

research

∙ 06/23/2020

Environment Shaping in Reinforcement Learning using State Abstraction

One of the central challenges faced by a reinforcement learning (RL) age...

0 Parameswaran Kamalaruban, et al. ∙

research

∙ 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...

0 Panayotis Mertikopoulos, et al. ∙

research

∙ 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...

0 Ya-Ping Hsieh, et al. ∙

research

∙ 06/11/2020

Convergence of adaptive algorithms for weakly convex constrained optimization

We analyze the adaptive first order algorithm AMSGrad, for solving a con...

0 Ahmet Alacaoglu, et al. ∙

research

∙ 04/18/2020

Lipschitz constant estimation of Neural Networks via sparse polynomial optimization

We introduce LiPopt, a polynomial optimization framework for computing i...

0 Fabian Latorre, et al. ∙

research

∙ 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...

0 Ahmet Alacaoglu, et al. ∙

research

∙ 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...

0 Deyi Liu, et al. ∙

research

∙ 02/14/2020

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

We introduce a sampling perspective to tackle the challenging task of tr...

0 Parameswaran Kamalaruban, et al. ∙

research

∙ 12/01/2019

Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents

This article reviews recent advances in multi-agent reinforcement learni...

0 Donghwan Lee, et al. ∙

research

∙ 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...

0 Ali Kavis, et al. ∙

research

∙ 10/09/2019

Nearly Minimal Over-Parametrization of Shallow Neural Networks

A recent line of work has shown that an overparametrized neural network ...

0 Armin Eftekhari, et al. ∙

Volkan Cevher

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