Unified Inter and Intra Options Learning Using Policy Gradient Methods. - Kfir Y. Levy; Nahum Shimkin (EWRL, 2011)

Logistic Regression: Tight Bounds for Stochastic and Online Optimization. - Elad Hazan; Tomer Koren; Kfir Y. Levy (COLT, 2014)

Bandit Convex Optimization: Towards Tight Bounds. - Elad Hazan; Kfir Y. Levy (NIPS, 2014)

Beyond Convexity: Stochastic Quasi-Convex Optimization. - Elad Hazan; Kfir Y. Levy; Shai Shalev-Shwartz (NIPS, 2015)

Fast Rates for Exp-concave Empirical Risk Minimization. - Tomer Koren; Kfir Y. Levy (NIPS, 2015)

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

k*-Nearest Neighbors: From Global to Local. - Oren Anava; Kfir Y. Levy (NIPS, 2016)

The Power of Normalization: Faster Evasion of Saddle Points. - Kfir Y. Levy (CoRR, 2016)

Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. - An Bian; Kfir Yehuda Levy; Andreas Krause 001; Joachim M. Buhmann (NIPS, 2017)

Online to Offline Conversions, Universality and Adaptive Minibatch Sizes. - Kfir Y. Levy (NIPS, 2017)

Faster Rates for Convex-Concave Games. - Jacob D. Abernethy; Kevin A. Lai; Kfir Y. Levy; Jun-Kun Wang (COLT, 2018)

Online Variance Reduction for Stochastic Optimization. - Zalan Borsos; Andreas Krause 001; Kfir Y. Levy (COLT, 2018)

An Online Learning Approach to Generative Adversarial Networks. - Paulina Grnarova; Kfir Y. Levy; Aurélien Lucchi; Thomas Hofmann 001; Andreas Krause 001 (ICLR (Poster), 2018)

Online Adaptive Methods, Universality and Acceleration. - Kfir Yehuda Levy; Alp Yurtsever; Volkan Cevher (NeurIPS, 2018)

Unsupervised Imitation Learning. - Sebastian Curi; Kfir Y. Levy; Andreas Krause 001 (CoRR, 2018)

Evaluating GANs via Duality. - Paulina Grnarova; Kfir Y. Levy; Aurélien Lucchi; Nathanaël Perraudin; Thomas Hofmann 001; Andreas Krause 001 (CoRR, 2018)

Projection Free Online Learning over Smooth Sets. - Kfir Y. Levy; Andreas Krause 001 (AISTATS, 2019)

Adaptive Input Estimation in Linear Dynamical Systems with Applications to Learning-from-Observations. - Sebastian Curi; Kfir Y. Levy; Andreas Krause 001 (CDC, 2019)

A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise. - Francis R. Bach; Kfir Y. Levy (COLT, 2019)

Online Variance Reduction with Mixtures. - Zalán Borsos; Sebastian Curi; Kfir Yehuda Levy; Andreas Krause 001 (ICML, 2019)

A Domain Agnostic Measure for Monitoring and Evaluating GANs. - Paulina Grnarova; Kfir Y. Levy; Aurélien Lucchi; Nathanaël Perraudin; Ian J. Goodfellow; Thomas Hofmann 001; Andreas Krause 001 (NeurIPS, 2019)

UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization. - Ali Kavis; Kfir Y. Levy; Francis R. Bach; Volkan Cevher (NeurIPS, 2019)

Online Convex Optimization in the Random Order Model. - Dan Garber; Gal Korcia; Kfir Y. Levy (ICML, 2020)

Adaptive Sampling for Stochastic Risk-Averse Learning. - Sebastian Curi; Kfir Y. Levy; Stefanie Jegelka; Andreas Krause 001 (NeurIPS, 2020)

Multi-Player Bandits: The Adversarial Case. - Pragnya Alatur; Kfir Y. Levy; Andreas Krause 001 (J. Mach. Learn. Res., 2020)

LAGA: Lagged AllReduce with Gradient Accumulation for Minimal Idle Time. - Ido Hakimi; Rotem Zamir Aviv; Kfir Y. Levy; Assaf Schuster (ICDM, 2021)

Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge. - Rotem Zamir Aviv; Ido Hakimi; Assaf Schuster; Kfir Yehuda Levy (ICML, 2021)

Fast Projection Onto Convex Smooth Constraints. - Ilnura Usmanova; Maryam Kamgarpour; Andreas Krause 001; Kfir Y. Levy (ICML, 2021)

Faster Neural Network Training with Approximate Tensor Operations. - Menachem Adelman; Kfir Y. Levy; Ido Hakimi; Mark Silberstein (NeurIPS, 2021)

STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization. - Kfir Y. Levy; Ali Kavis; Volkan Cevher (NeurIPS, 2021)

Generative Minimization Networks: Training GANs Without Competition. - Paulina Grnarova; Yannic Kilcher; Kfir Y. Levy; Aurélien Lucchi; Thomas Hofmann 001 (CoRR, 2021)

Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays. - Rotem Zamir Aviv; Ido Hakimi; Assaf Schuster; Kfir Y. Levy (CoRR, 2021)

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization. - Kfir Y. Levy; Ali Kavis; Volkan Cevher (CoRR, 2021)

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

UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees. - Kimon Antonakopoulos; Dong Quan Vu; Volkan Cevher; Kfir Y. Levy; Panayotis Mertikopoulos (ICML, 2022)

Adapting to Mixing Time in Stochastic Optimization with Markovian Data. - Ron Dorfman; Kfir Yehuda Levy (ICML, 2022)

Efficient Policy Iteration for Robust Markov Decision Processes via Regularization. - Navdeep Kumar; Kfir Levy; Kaixin Wang; Shie Mannor (CoRR, 2022)

Online Meta-Learning in Adversarial Multi-Armed Bandits. - Ilya Osadchiy; Kfir Y. Levy; Ron Meir (CoRR, 2022)

Policy Gradient for Reinforcement Learning with General Utilities. - Navdeep Kumar; Kaixin Wang; Kfir Levy; Shie Mannor (CoRR, 2022)

Measuring Explainability and Trustworthiness of Power Quality Disturbances Classifiers Using XAI - Explainable Artificial Intelligence. - Ram Machlev; Michael Perl; Juri Belikov; Kfir Yehuda Levy; Yoash Levron (IEEE Trans. Ind. Informatics, 2022)

Robust Linear Regression for General Feature Distribution. - Tom Norman; Nir Weinberger; Kfir Y. Levy (AISTATS, 2023)

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

DropCompute: simple and more robust distributed synchronous training via compute variance reduction. - Niv Giladi; Shahar Gottlieb; Moran Shkolnik; Asaf Karnieli; Ron Banner; Elad Hoffer; Kfir Y. Levy; Daniel Soudry (NeurIPS, 2023)

Meta-Learning Adversarial Bandit Algorithms. - Misha Khodak; Ilya Osadchiy; Keegan Harris; Maria-Florina Balcan; Kfir Y. Levy; Ron Meir; Zhiwei Steven Wu (NeurIPS, 2023)

Policy Gradient for Rectangular Robust Markov Decision Processes. - Navdeep Kumar; Esther Derman; Matthieu Geist; Kfir Y. Levy; Shie Mannor (NeurIPS, 2023)

Policy Gradient for s-Rectangular Robust Markov Decision Processes. - Navdeep Kumar; Esther Derman; Matthieu Geist; Kfir Levy; Shie Mannor (CoRR, 2023)

An Efficient Solution to s-Rectangular Robust Markov Decision Processes. - Navdeep Kumar; Kfir Levy; Kaixin Wang; Shie Mannor (CoRR, 2023)

SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization. - Kfir Y. Levy (CoRR, 2023)

μ2-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism. - Kfir Y. Levy (CoRR, 2023)

Robust Reinforcement Learning via Adversarial Kernel Approximation. - Kaixin Wang; Uri Gadot; Navdeep Kumar; Kfir Levy; Shie Mannor (CoRR, 2023)

Meta-Learning Adversarial Bandit Algorithms. - Mikhail Khodak; Ilya Osadchiy; Keegan Harris; Maria-Florina Balcan; Kfir Y. Levy; Ron Meir; Zhiwei Steven Wu (CoRR, 2023)

Solving Non-rectangular Reward-Robust MDPs via Frequency Regularization. - Uri Gadot; Esther Derman; Navdeep Kumar; Maxence Mohamed Elfatihi; Kfir Levy; Shie Mannor (AAAI, 2024)

Policy Gradient with Tree Search (PGTS) in Reinforcement Learning Evades Local Maxima. - Navdeep Kumar; Priyank Agrawal; Kfir Yehuda Levy; Shie Mannor (Tiny Papers @ ICLR, 2024)

Towards Faster Global Convergence of Robust Policy Gradient Methods. - Navdeep Kumar; Ilnura Usmanova; Kfir Yehuda Levy; Shie Mannor (Tiny Papers @ ICLR, 2024)

Learning the Uncertainty Set in Robust Markov Decision Process. - Navdeep Kumar; Kaixin Wang; Uri Gadot; Kfir Yehuda Levy; Shie Mannor (Tiny Papers @ ICLR, 2024)

Policy Gradient for Reinforcement Learning with General Utilities. - Navdeep Kumar; Kaixin Wang; Utkarsh Pratiush; Kfir Yehuda Levy; Shie Mannor (Tiny Papers @ ICLR, 2024)

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

Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers. - Ron Dorfman; Naseem Yehya; 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)

A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle. - Nadav Hallak; 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)

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

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML. - Tehila Dahan; Kfir Y. Levy (NeurIPS, 2024)

SLowcalSGD : Slow Query Points Improve Local-SGD for Stochastic Convex Optimization. - Tehila Dahan; Kfir Y. Levy (NeurIPS, 2024)

On the Global Convergence of Policy Gradient in Average Reward Markov Decision Processes. - Navdeep Kumar; Yashaswini Murthy; Itai Shufaro; Kfir Y. Levy; R. Srikant 001; Shie Mannor (CoRR, 2024)

EXAQ: Exponent Aware Quantization For LLMs Acceleration. - Moran Shkolnik; Maxim Fishman; Brian Chmiel; Hilla Ben-Yaacov; Ron Banner; Kfir Yehuda Levy (CoRR, 2024)

Improved Sample Complexity for Global Convergence of Actor-Critic Algorithms. - Navdeep Kumar; Priyank Agrawal; Giorgia Ramponi; Kfir Yehuda Levy; Shie Mannor (CoRR, 2024)

No-regret dynamics in the Fenchel game: a unified framework for algorithmic convex optimization. - Jun-Kun Wang; Jacob D. Abernethy; Kfir Y. Levy (Math. Program., 2024)

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

Do Stochastic, Feel Noiseless: Stable Stochastic Optimization via a Double Momentum Mechanism. - Tehila Dahan; 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 001; Shie Mannor (ICLR, 2025)

An Improved and Explainable Electricity Price Forecasting Model via SHAP-Based Error Compensation Approach. - Leena Heistrene; Juri Belikov; Dmitry Baimel; Liran Katzir 002; Ram Machlev; Kfir Levy; Shie Mannor; Yoash Levron (IEEE Trans. Artif. Intell., 2025)

DE-PADA: Personalized Augmentation and Domain Adaptation for ECG Biometrics Across Physiological States. - Amro Abu Saleh; Elliot Sprecher; Kfir Y. Levy; Daniel H. Lange (CoRR, 2025)

Dual Formulation for Non-Rectangular Lp Robust Markov Decision Processes. - Navdeep Kumar; Adarsh Gupta; Maxence Mohamed Elfatihi; Giorgia Ramponi; Kfir Yehuda Levy; Shie Mannor (CoRR, 2025)

Enhancing Parallelism in Decentralized Stochastic Convex Optimization. - Ofri Eisen; Ron Dorfman; Kfir Y. Levy (CoRR, 2025)

Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation. - Roie Reshef; Kfir Yehuda Levy (CoRR, 2025)

Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead. - Uri Koren; Navdeep Kumar; Uri Gadot; Giorgia Ramponi; Kfir Yehuda Levy; Shie Mannor (CoRR, 2025)

Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning. - Ze'ev Zukerman; Bassel Hamoud; Kfir Y. Levy (CoRR, 2025)