| 2025 | AISTATS | Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing Constraints. | Bassel Hamoud, Ilnura Usmanova, Kfir Yehuda Levy |
| 2025 | ICLR | Do Stochastic, Feel Noiseless: Stable Stochastic Optimization via a Double Momentum Mechanism. | Tehila Dahan, Kfir Yehuda Levy |
| 2025 | ICLR | Global Convergence of Policy Gradient in Average Reward MDPs. | Navdeep Kumar, Yashaswini Murthy, Itai Shufaro, Kfir Yehuda Levy, R. Srikant, Shie Mannor |
| 2025 | ICML | Enhancing Parallelism in Decentralized Stochastic Convex Optimization. | Ofri Eisen, Ron Dorfman, Kfir Yehuda Levy |
| 2025 | ICML | Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation. | Roie Reshef, Kfir Yehuda Levy |
| 2025 | ICML | Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning. | Ze'ev Zukerman, Bassel Hamoud, Kfir Yehuda Levy |
| 2024 | ICLR | Policy Gradient with Tree Search (PGTS) in Reinforcement Learning Evades Local Maxima. | Navdeep Kumar, Priyank Agrawal, Kfir Yehuda Levy, Shie Mannor |
| 2024 | ICLR | Towards Faster Global Convergence of Robust Policy Gradient Methods. | Navdeep Kumar, Ilnura Usmanova, Kfir Yehuda Levy, Shie Mannor |
| 2024 | ICLR | Learning the Uncertainty Set in Robust Markov Decision Process. | Navdeep Kumar, Kaixin Wang, Uri Gadot, Kfir Yehuda Levy, Shie Mannor |
| 2024 | ICLR | Policy Gradient for Reinforcement Learning with General Utilities. | Navdeep Kumar, Kaixin Wang, Utkarsh Pratiush, Kfir Yehuda Levy, Shie Mannor |
| 2024 | ICML | Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training. | Tehila Dahan, Kfir Yehuda Levy |
| 2024 | ICML | Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers. | Ron Dorfman, Naseem Yehya, Kfir Yehuda Levy |
| 2024 | ICML | 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 |
| 2024 | ICML | A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle. | Nadav Hallak, Kfir Yehuda Levy |
| 2024 | ICML | Efficient Value Iteration for s-rectangular Robust Markov Decision Processes. | Navdeep Kumar, Kaixin Wang, Kfir Yehuda Levy, Shie Mannor |
| 2024 | ICML | Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems. | Roie Reshef, Kfir Yehuda Levy |
| 2023 | ICML | DoCoFL: Downlink Compression for Cross-Device Federated Learning. | Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Yehuda Levy |
| 2022 | ICLR | High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize. | Ali Kavis, Kfir Yehuda Levy, Volkan Cevher |
| 2022 | ICML | Adapting to Mixing Time in Stochastic Optimization with Markovian Data. | Ron Dorfman, Kfir Yehuda Levy |
| 2021 | ICML | Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge. | Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Yehuda Levy |
| 2019 | ICML | Online Variance Reduction with Mixtures. | Zaln Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause |
| 2016 | ICML | On Graduated Optimization for Stochastic Non-Convex Problems. | Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz |