Lazy OCO: Online Convex Optimization on a Switching Budget. - Uri Sherman; Tomer Koren (COLT, 2021)

Optimal Rates for Random Order Online Optimization. - Uri Sherman; Tomer Koren; Yishay Mansour (NeurIPS, 2021)

Benign Underfitting of Stochastic Gradient Descent. - Tomer Koren; Roi Livni; Yishay Mansour; Uri Sherman (NeurIPS, 2022)

Regret Minimization and Convergence to Equilibria in General-sum Markov Games. - Liad Erez; Tal Lancewicki; Uri Sherman; Tomer Koren; Yishay Mansour (ICML, 2023)

Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation. - Uri Sherman; Tomer Koren; Yishay Mansour (ICML, 2023)

Rate-Optimal Policy Optimization for Linear Markov Decision Processes. - Uri Sherman; Alon Cohen; Tomer Koren; Yishay Mansour (ICML, 2024)

The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization. - Matan Schliserman; Uri Sherman; Tomer Koren (ALT, 2025)

Convergence of Policy Mirror Descent Beyond Compatible Function Approximation. - Uri Sherman; Tomer Koren; Yishay Mansour (CoRR, 2025)

Better Rates for Random Task Orderings in Continual Linear Models. - Itay Evron; Ran Levinstein; Matan Schliserman; Uri Sherman; Tomer Koren; Daniel Soudry; Nathan Srebro (CoRR, 2025)

Optimal Rates in Continual Linear Regression via Increasing Regularization. - Ran Levinstein; Amit Attia; Matan Schliserman; Uri Sherman; Tomer Koren; Daniel Soudry; Itay Evron (CoRR, 2025)

Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning. - Uri Sherman; Tomer Koren; Yishay Mansour (CoRR, 2025)

Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime. - Amit Attia; Matan Schliserman; Uri Sherman; Tomer Koren (CoRR, 2025)