Kfir Y. Levy

According to our database, Kfir Y. Levy authored at least 65 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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., May, 2024

EXAQ: Exponent Aware Quantization For LLMs Acceleration.

Moran Shkolnik, Maxim Fishman, Brian Chmiel, Hilla Ben-Yaacov, Ron Banner, Kfir Yehuda Levy

CoRR, 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, Shie Mannor

CoRR, 2024

Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems.

Roie Reshef, Kfir Yehuda Levy

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Efficient Value Iteration for s-rectangular Robust Markov Decision Processes.

Navdeep Kumar, Kaixin Wang, Kfir Yehuda Levy, Shie Mannor

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle.

Nadav Hallak, Kfir Yehuda Levy

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers.

Ron Dorfman, Naseem Yehya, Kfir Yehuda Levy

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training.

Tehila Dahan, Kfir Yehuda Levy

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Policy Gradient for Reinforcement Learning with General Utilities.

Navdeep Kumar, Kaixin Wang, Utkarsh Pratiush, Kfir Yehuda Levy, Shie Mannor

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

Learning the Uncertainty Set in Robust Markov Decision Process.

Navdeep Kumar, Kaixin Wang, Uri Gadot, Kfir Yehuda Levy, Shie Mannor

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

Towards Faster Global Convergence of Robust Policy Gradient Methods.

Navdeep Kumar, Ilnura Usmanova, Kfir Yehuda Levy, Shie Mannor

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

Policy Gradient with Tree Search (PGTS) in Reinforcement Learning Evades Local Maxima.

Navdeep Kumar, Priyank Agrawal, Kfir Yehuda Levy, Shie Mannor

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

Solving Non-rectangular Reward-Robust MDPs via Frequency Regularization.

Uri Gadot, Esther Derman, Navdeep Kumar, Maxence Mohamed Elfatihi, Kfir Levy, Shie Mannor

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

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

Robust Reinforcement Learning via Adversarial Kernel Approximation.

Kaixin Wang, Uri Gadot, Navdeep Kumar, Kfir Levy, Shie Mannor

CoRR, 2023

μ²-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism.

Kfir Y. Levy

CoRR, 2023

SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization.

Kfir Y. Levy

CoRR, 2023

An Efficient Solution to s-Rectangular Robust Markov Decision Processes.

Navdeep Kumar, Kfir Levy, Kaixin Wang, Shie Mannor

CoRR, 2023

Policy Gradient for s-Rectangular Robust Markov Decision Processes.

Navdeep Kumar, Esther Derman, Matthieu Geist, Kfir Levy, Shie Mannor

CoRR, 2023

Policy Gradient for Rectangular Robust Markov Decision Processes.

Navdeep Kumar, Esther Derman, Matthieu Geist, Kfir Y. Levy, Shie Mannor

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Meta-Learning Adversarial Bandit Algorithms.

Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DoCoFL: Downlink Compression for Cross-Device Federated Learning.

Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Yehuda Levy

Proceedings of the International Conference on Machine Learning, 2023

Robust Linear Regression for General Feature Distribution.

Tom Norman, Nir Weinberger, Kfir Y. Levy

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

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

Policy Gradient for Reinforcement Learning with General Utilities.

Navdeep Kumar, Kaixin Wang, Kfir Levy, Shie Mannor

CoRR, 2022

Online Meta-Learning in Adversarial Multi-Armed Bandits.

Ilya Osadchiy, Kfir Y. Levy, Ron Meir

CoRR, 2022

Efficient Policy Iteration for Robust Markov Decision Processes via Regularization.

Navdeep Kumar, Kfir Levy, Kaixin Wang, Shie Mannor

CoRR, 2022

Adapting to Mixing Time in Stochastic Optimization with Markovian Data.

Ron Dorfman, Kfir Yehuda Levy

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2022

High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize.

Ali Kavis, Kfir Yehuda Levy, Volkan Cevher

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization.

Kfir Y. Levy, Ali Kavis, Volkan Cevher

CoRR, 2021

Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays.

Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Y. Levy

CoRR, 2021

Generative Minimization Networks: Training GANs Without Competition.

Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann

CoRR, 2021

STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization.

Kfir Y. Levy, Ali Kavis, Volkan Cevher

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Faster Neural Network Training with Approximate Tensor Operations.

Menachem Adelman, Kfir Y. Levy, Ido Hakimi, Mark Silberstein

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Fast Projection Onto Convex Smooth Constraints.

Ilnura Usmanova, Maryam Kamgarpour, Andreas Krause, Kfir Y. Levy

Proceedings of the 38th International Conference on Machine Learning, 2021

Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge.

Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Yehuda Levy

Proceedings of the 38th International Conference on Machine Learning, 2021

LAGA: Lagged AllReduce with Gradient Accumulation for Minimal Idle Time.

Ido Hakimi, Rotem Zamir Aviv, Kfir Y. Levy, Assaf Schuster

Proceedings of the IEEE International Conference on Data Mining, 2021

2020

Multi-Player Bandits: The Adversarial Case.

Pragnya Alatur, Kfir Y. Levy, Andreas Krause

J. Mach. Learn. Res., 2020

Adaptive Sampling for Stochastic Risk-Averse Learning.

Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Online Convex Optimization in the Random Order Model.

Dan Garber, Gal Korcia, Kfir Y. Levy

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization.

Ali Kavis, Kfir Y. Levy, Francis R. Bach, Volkan Cevher

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Domain Agnostic Measure for Monitoring and Evaluating GANs.

Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Online Variance Reduction with Mixtures.

Zalán Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause

Proceedings of the 36th International Conference on Machine Learning, 2019

A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise.

Francis R. Bach, Kfir Y. Levy

Proceedings of the Conference on Learning Theory, 2019

Adaptive Input Estimation in Linear Dynamical Systems with Applications to Learning-from-Observations.

Sebastian Curi, Kfir Y. Levy, Andreas Krause

Proceedings of the 58th IEEE Conference on Decision and Control, 2019

Projection Free Online Learning over Smooth Sets.

Kfir Y. Levy, Andreas Krause

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Evaluating GANs via Duality.

Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Thomas Hofmann, Andreas Krause

CoRR, 2018

Unsupervised Imitation Learning.

Sebastian Curi, Kfir Y. Levy, Andreas Krause

CoRR, 2018

Online Adaptive Methods, Universality and Acceleration.

Kfir Yehuda Levy, Alp Yurtsever, Volkan Cevher

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

An Online Learning Approach to Generative Adversarial Networks.

Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause

Proceedings of the 6th International Conference on Learning Representations, 2018

Online Variance Reduction for Stochastic Optimization.

Zalan Borsos, Andreas Krause, Kfir Y. Levy

Proceedings of the Conference On Learning Theory, 2018

Faster Rates for Convex-Concave Games.

Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang

Proceedings of the Conference On Learning Theory, 2018

2017

Online to Offline Conversions, Universality and Adaptive Minibatch Sizes.

Kfir Y. Levy

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms.

An Bian, Kfir Yehuda Levy, Andreas Krause, Joachim M. Buhmann

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

The Power of Normalization: Faster Evasion of Saddle Points.

Kfir Y. Levy

CoRR, 2016

k*-Nearest Neighbors: From Global to Local.

Oren Anava, Kfir Y. Levy

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

On Graduated Optimization for Stochastic Non-Convex Problems.

Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Fast Rates for Exp-concave Empirical Risk Minimization.

Tomer Koren, Kfir Y. Levy

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Beyond Convexity: Stochastic Quasi-Convex Optimization.

Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Bandit Convex Optimization: Towards Tight Bounds.

Elad Hazan, Kfir Y. Levy

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Logistic Regression: Tight Bounds for Stochastic and Online Optimization.

Elad Hazan, Tomer Koren, Kfir Y. Levy

Proceedings of The 27th Conference on Learning Theory, 2014

2011

Unified Inter and Intra Options Learning Using Policy Gradient Methods.

Kfir Y. Levy, Nahum Shimkin

Proceedings of the Recent Advances in Reinforcement Learning - 9th European Workshop, 2011