2 citations · 4 across the 10 of their papers we have counts for
13 papers · 1 filter
The Hidden Cost of Approximation in Online Mirror Descent
Ofir Schlisselberg, Uri Sherman, Tomer Koren +1
Online mirror descent (OMD) is a fundamental algorithmic paradigm that underlies many algorithms in optimization, machine learning and sequential decision-making. The OMD iterates…
Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime
Amit Attia, Matan Schliserman, Uri Sherman +1
We study population convergence guarantees of stochastic gradient descent (SGD) for smooth convex objectives in the interpolation regime, where the noise at optimum is zero or near…
Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning
Uri Sherman, Tomer Koren, Yishay Mansour
We study reinforcement learning (RL) in the agnostic policy learning setting, where the goal is to find a policy whose performance is competitive with the best policy in a given cl…
Optimal Rates in Continual Linear Regression via Increasing Regularization
Ran Levinstein, Amit Attia, Matan Schliserman +4
We study realizable continual linear regression under random task orderings, a common setting for developing continual learning theory. In this setup, the worst-case expected loss…
From Continual Learning to SGD and Back: Better Rates for Continual Linear Models
Itay Evron, Ran Levinstein, Matan Schliserman +4
We study the common continual learning setup where an overparameterized model is sequentially fitted to a set of jointly realizable tasks. We analyze forgetting, defined as the los…
Convergence of Policy Mirror Descent Beyond Compatible Function Approximation
Uri Sherman, Tomer Koren, Yishay Mansour
Modern policy optimization methods roughly follow the policy mirror descent (PMD) algorithmic template, for which there are by now numerous theoretical convergence results. However…
The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization
Matan Schliserman, Uri Sherman, Tomer Koren
We study the generalization performance of gradient methods in the fundamental stochastic convex optimization setting, focusing on its dimension dependence. First, for full-batch g…
Rate-Optimal Policy Optimization for Linear Markov Decision Processes
Uri Sherman, Alon Cohen, Tomer Koren +1
We study regret minimization in online episodic linear Markov Decision Processes, and obtain rate-optimal regret where denotes the number of episodes.…
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation
Uri Sherman, Tomer Koren, Yishay Mansour
We study reinforcement learning with linear function approximation and adversarially changing cost functions, a setup that has mostly been considered under simplifying assumptions…
Regret Minimization and Convergence to Equilibria in General-sum Markov Games
Liad Erez, Tal Lancewicki, Uri Sherman +2
An abundance of recent impossibility results establish that regret minimization in Markov games with adversarial opponents is both statistically and computationally intractable. Ne…
Benign Underfitting of Stochastic Gradient Descent
Tomer Koren, Roi Livni, Yishay Mansour +1
We study to what extent may stochastic gradient descent (SGD) be understood as a "conventional" learning rule that achieves generalization performance by obtaining a good fit to tr…
Optimal Rates for Random Order Online Optimization
Uri Sherman, Tomer Koren, Yishay Mansour
We study online convex optimization in the random order model, recently proposed by \citet{garber2020online}, where the loss functions may be chosen by an adversary, but are then p…
Lazy OCO: Online Convex Optimization on a Switching Budget
Uri Sherman, Tomer Koren
We study a variant of online convex optimization where the player is permitted to switch decisions at most times in expectation throughout rounds. Similar problems have bee…