Levy, Kfir Yehuda

25 publications

ICLR 2026 Probing in the Dark: State Entropy Maximization for POMDPs Yonatan Ashlag, Mirco Mutti, Aviv Tamar, Kfir Yehuda Levy

ICML 2025 Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning Ze’Ev Zukerman, Bassel Hamoud, Kfir Yehuda Levy

NeurIPS 2025 Convergence of Clipped SGD on Convex $(L_0,L_1)$-Smooth Functions Ofir Gaash, Kfir Yehuda Levy, Yair Carmon

ICLR 2025 Do Stochastic, Feel Noiseless: Stable Stochastic Optimization via a Double Momentum Mechanism Tehila Dahan, Kfir Yehuda Levy

ICML 2025 Enhancing Parallelism in Decentralized Stochastic Convex Optimization Ofri Eisen, Ron Dorfman, Kfir Yehuda Levy

ICLR 2025 Global Convergence of Policy Gradient in Average Reward MDPs Navdeep Kumar, Yashaswini Murthy, Itai Shufaro, Kfir Yehuda Levy, R. Srikant, Shie Mannor

NeurIPS 2025 Gradient-Variation Online Adaptivity for Accelerated Optimization with Hölder Smoothness Yuheng Zhao, Yu-Hu Yan, Kfir Yehuda Levy, Peng Zhao

NeurIPS 2025 Non-Rectangular Robust MDPs with Normed Uncertainty Sets Navdeep Kumar, Adarsh Gupta, Maxence Mohamed Elfatihi, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor

NeurIPS 2025 On the Convergence of Single-Timescale Actor-Critic Navdeep Kumar, Priyank Agrawal, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor

NeurIPS 2025 Prediction-Powered Semi-Supervised Learning with Online Power Tuning Noa Shoham, Ron Dorfman, Shalev Shaer, Kfir Yehuda Levy, Yaniv Romano

ICML 2025 Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation Roie Reshef, Kfir Yehuda Levy

AISTATS 2025 Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing Constraints Bassel Hamoud, Ilnura Usmanova, Kfir Yehuda Levy

ICML 2024 A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle Nadav Hallak, Kfir Yehuda Levy

ICML 2024 Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating the Worst Kernel Uri Gadot, Kaixin Wang, Navdeep Kumar, Kfir Yehuda Levy, Shie Mannor

ICML 2024 Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers Ron Dorfman, Naseem Amin Yehya, Kfir Yehuda Levy

NeurIPSW 2024 EXAQ: Exponent Aware Quantization for LLMs Acceleration Moran Shkolnik, Maxim Fishman, Brian Chmiel, Hilla Ben-Yaacov, Ron Banner, Kfir Yehuda Levy

ICML 2024 Efficient Value Iteration for S-Rectangular Robust Markov Decision Processes Navdeep Kumar, Kaixin Wang, Kfir Yehuda Levy, Shie Mannor

ICML 2024 Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training Tehila Dahan, Kfir Yehuda Levy

ICML 2024 Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems Roie Reshef, Kfir Yehuda Levy

ICML 2023 DoCoFL: Downlink Compression for Cross-Device Federated Learning Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Yehuda Levy

ICML 2022 Adapting to Mixing Time in Stochastic Optimization with Markovian Data Ron Dorfman, Kfir Yehuda Levy

ICLR 2022 High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize Ali Kavis, Kfir Yehuda Levy, Volkan Cevher

ICML 2021 Asynchronous Distributed Learning : Adapting to Gradient Delays Without Prior Knowledge Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Yehuda Levy

ICML 2019 Online Variance Reduction with Mixtures Zalán Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause

ICML 2016 On Graduated Optimization for Stochastic Non-Convex Problems Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz