Kfir Yehuda Levy

34 papers A* 18A 1Journal 10Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum. CoRR Navdeep Kumar, Tehila Dahan, Lior Cohen, Ananyabrata Barua, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor
2025	A*	`conf`	Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning. ICML Ze'ev Zukerman, Bassel Hamoud, Kfir Yehuda Levy
2025	A*	`conf`	Do Stochastic, Feel Noiseless: Stable Stochastic Optimization via a Double Momentum Mechanism. ICLR Tehila Dahan, Kfir Yehuda Levy
2025	J	`jnl`	Dual Formulation for Non-Rectangular Lp Robust Markov Decision Processes. CoRR Navdeep Kumar, Adarsh Gupta, Maxence Mohamed Elfatihi, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor
2025	A*	`conf`	Enhancing Parallelism in Decentralized Stochastic Convex Optimization. ICML Ofri Eisen, Ron Dorfman, Kfir Yehuda Levy
2025	A*	`conf`	Global Convergence of Policy Gradient in Average Reward MDPs. ICLR Navdeep Kumar, Yashaswini Murthy, Itai Shufaro, Kfir Yehuda Levy, R. Srikant, Shie Mannor
2025	J	`jnl`	Gradient-Variation Online Adaptivity for Accelerated Optimization with Hölder Smoothness. CoRR Yuheng Zhao, Yu-Hu Yan, Kfir Yehuda Levy, Peng Zhao
2025	J	`jnl`	Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead. CoRR Uri Koren, Navdeep Kumar, Uri Gadot, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor
2025	A*	`conf`	Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation. ICML Roie Reshef, Kfir Yehuda Levy
2025	J	`jnl`	Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation. CoRR Roie Reshef, Kfir Yehuda Levy
2025	A	`conf`	Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing Constraints. AISTATS Bassel Hamoud, Ilnura Usmanova, Kfir Yehuda Levy
2024	A*	`conf`	A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle. ICML Nadav Hallak, Kfir Yehuda Levy
2024	A*	`conf`	Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel. ICML Uri Gadot, Kaixin Wang, Navdeep Kumar, Kfir Yehuda Levy, Shie Mannor
2024	A*	`conf`	Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers. ICML Ron Dorfman, Naseem Yehya, Kfir Yehuda Levy
2024	J	`jnl`	EXAQ: Exponent Aware Quantization For LLMs Acceleration. CoRR Moran Shkolnik, Maxim Fishman, Brian Chmiel, Hilla Ben-Yaacov, Ron Banner, Kfir Yehuda Levy
2024	A*	`conf`	Efficient Value Iteration for s-rectangular Robust Markov Decision Processes. ICML Navdeep Kumar, Kaixin Wang, Kfir Yehuda Levy, Shie Mannor
2024	A*	`conf`	Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training. ICML Tehila Dahan, Kfir Yehuda Levy
2024	J	`jnl`	Improved Sample Complexity for Global Convergence of Actor-Critic Algorithms. CoRR Navdeep Kumar, Priyank Agrawal, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor
2024	—	`conf`	Learning the Uncertainty Set in Robust Markov Decision Process. Tiny Papers @ ICLR Navdeep Kumar, Kaixin Wang, Uri Gadot, Kfir Yehuda Levy, Shie Mannor
2024	—	`conf`	Policy Gradient for Reinforcement Learning with General Utilities. Tiny Papers @ ICLR Navdeep Kumar, Kaixin Wang, Utkarsh Pratiush, Kfir Yehuda Levy, Shie Mannor
2024	—	`conf`	Policy Gradient with Tree Search (PGTS) in Reinforcement Learning Evades Local Maxima. Tiny Papers @ ICLR Navdeep Kumar, Priyank Agrawal, Kfir Yehuda Levy, Shie Mannor
2024	A*	`conf`	Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems. ICML Roie Reshef, Kfir Yehuda Levy
2024	—	`conf`	Towards Faster Global Convergence of Robust Policy Gradient Methods. Tiny Papers @ ICLR Navdeep Kumar, Ilnura Usmanova, Kfir Yehuda Levy, Shie Mannor
2023	A*	`conf`	DoCoFL: Downlink Compression for Cross-Device Federated Learning. ICML Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Yehuda Levy
2023	J	`jnl`	DropCompute: simple and more robust distributed synchronous training via compute variance reduction. CoRR Niv Giladi, Shahar Gottlieb, Moran Shkolnik, Asaf Karnieli, Ron Banner, Elad Hoffer, Kfir Yehuda Levy, Daniel Soudry
2022	A*	`conf`	Adapting to Mixing Time in Stochastic Optimization with Markovian Data. ICML Ron Dorfman, Kfir Yehuda Levy
2022	A*	`conf`	High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize. ICLR Ali Kavis, Kfir Yehuda Levy, Volkan Cevher
2022	J	`jnl`	High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize. CoRR Ali Kavis, Kfir Yehuda Levy, Volkan Cevher
2022	J	`jnl`	Measuring Explainability and Trustworthiness of Power Quality Disturbances Classifiers Using XAI - Explainable Artificial Intelligence. IEEE Trans. Ind. Informatics Ram Machlev, Michael Perl, Juri Belikov, Kfir Yehuda Levy, Yoash Levron
2021	A*	`conf`	Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge. ICML Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Yehuda Levy
2019	A*	`conf`	Online Variance Reduction with Mixtures. ICML Zalán Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause
2018	A*	`conf`	Online Adaptive Methods, Universality and Acceleration. NeurIPS Kfir Yehuda Levy, Alp Yurtsever, Volkan Cevher
2017	—	`conf`	Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. NIPS An Bian, Kfir Yehuda Levy, Andreas Krause, Joachim M. Buhmann
2016	A*	`conf`	On Graduated Optimization for Stochastic Non-Convex Problems. ICML Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz