Kfir Y. Levy

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 53

Top Topics

Markov Decision Process

Variance Reduction

Top Venues

Publications

Jun-Kun Wang, Jacob D. Abernethy, Kfir Y. Levy
No-regret dynamics in the Fenchel game: a unified framework for algorithmic convex optimization. Math. Program. 205 (1) (2024)
Navdeep Kumar, Yashaswini Murthy, Itai Shufaro, Kfir Y. Levy, R. Srikant, Shie Mannor
On the Global Convergence of Policy Gradient in Average Reward Markov Decision Processes. CoRR (2024)
Roie Reshef, Kfir Y. Levy
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems. CoRR (2024)
Tehila Dahan, Kfir Y. Levy
Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training. CoRR (2024)
Ron Dorfman, Naseem Yehya, Kfir Y. Levy
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers. CoRR (2024)
Niv Giladi, Shahar Gottlieb, Moran Shkolnik, Asaf Karnieli, Ron Banner, Elad Hoffer, Kfir Y. Levy, Daniel Soudry
DropCompute: simple and more robust distributed synchronous training via compute variance reduction. NeurIPS (2023)
Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Steven Z. Wu
Meta-Learning Adversarial Bandit Algorithms. NeurIPS (2023)
Tom Norman, Nir Weinberger, Kfir Y. Levy
Robust Linear Regression for General Feature Distribution. AISTATS (2023)
Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu
Meta-Learning Adversarial Bandit Algorithms. CoRR (2023)
Navdeep Kumar, Esther Derman, Matthieu Geist, Kfir Y. Levy, Shie Mannor
Policy Gradient for Rectangular Robust Markov Decision Processes. NeurIPS (2023)
Kfir Y. Levy
-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism. CoRR (2023)
Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Y. Levy
DoCoFL: Downlink Compression for Cross-Device Federated Learning. CoRR (2023)
Kfir Y. Levy
SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization. CoRR (2023)
Ilya Osadchiy, Kfir Y. Levy, Ron Meir
Online Meta-Learning in Adversarial Multi-Armed Bandits. CoRR (2022)
Tom Norman, Nir Weinberger, Kfir Y. Levy
Robust Linear Regression for General Feature Distribution. CoRR (2022)
Ron Dorfman, Kfir Y. Levy
Adapting to Mixing Time in Stochastic Optimization with Markovian Data. CoRR (2022)
Kimon Antonakopoulos, Dong Quan Vu, Volkan Cevher, Kfir Y. Levy, Panayotis Mertikopoulos
UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees. ICML (2022)
Ido Hakimi, Rotem Zamir Aviv, Kfir Y. Levy, Assaf Schuster
LAGA: Lagged AllReduce with Gradient Accumulation for Minimal Idle Time. ICDM (2021)
Kfir Y. Levy, Ali Kavis, Volkan Cevher
STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization. NeurIPS (2021)
Ilnura Usmanova, Maryam Kamgarpour, Andreas Krause, Kfir Y. Levy
Fast Projection Onto Convex Smooth Constraints. ICML (2021)
Kfir Y. Levy, Ali Kavis, Volkan Cevher
STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization. CoRR (2021)
Menachem Adelman, Kfir Y. Levy, Ido Hakimi, Mark Silberstein
Faster Neural Network Training with Approximate Tensor Operations. NeurIPS (2021)
Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Y. Levy
Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays. CoRR (2021)
Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann
Generative Minimization Networks: Training GANs Without Competition. CoRR (2021)
Jun-Kun Wang, Jacob D. Abernethy, Kfir Y. Levy
No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimization. CoRR (2021)
Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause
Adaptive Sampling for Stochastic Risk-Averse Learning. NeurIPS (2020)
Dan Garber, Gal Korcia, Kfir Y. Levy
Online Convex Optimization in the Random Order Model. ICML (2020)
Pragnya Alatur, Kfir Y. Levy, Andreas Krause
Multi-Player Bandits: The Adversarial Case. J. Mach. Learn. Res. 21 (2020)
Francis R. Bach, Kfir Y. Levy
A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise. COLT (2019)
Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause
A Domain Agnostic Measure for Monitoring and Evaluating GANs. NeurIPS (2019)
Ali Kavis, Kfir Y. Levy, Francis R. Bach, Volkan Cevher
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization. CoRR (2019)
Ali Kavis, Kfir Y. Levy, Francis R. Bach, Volkan Cevher
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization. NeurIPS (2019)
Francis R. Bach, Kfir Y. Levy
A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise. CoRR (2019)
Sebastian Curi, Kfir Y. Levy, Andreas Krause
Adaptive Input Estimation in Linear Dynamical Systems with Applications to Learning-from-Observations. CDC (2019)
Pragnya Alatur, Kfir Y. Levy, Andreas Krause
Multi-Player Bandits: The Adversarial Case. CoRR (2019)
Zalán Borsos, Sebastian Curi, Kfir Y. Levy, Andreas Krause
Online Variance Reduction with Mixtures. CoRR (2019)
Kfir Y. Levy, Andreas Krause
Projection Free Online Learning over Smooth Sets. AISTATS (2019)
Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause
Adaptive Sampling for Stochastic Risk-Averse Learning. CoRR (2019)
Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Thomas Hofmann, Andreas Krause
Evaluating GANs via Duality. CoRR (2018)
Zalán Borsos, Andreas Krause, Kfir Y. Levy
Online Variance Reduction for Stochastic Optimization. CoRR (2018)
Sebastian Curi, Kfir Y. Levy, Andreas Krause
Unsupervised Imitation Learning. CoRR (2018)
Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang
Faster Rates for Convex-Concave Games. CoRR (2018)
Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause
An Online Learning Approach to Generative Adversarial Networks. ICLR (Poster) (2018)
Kfir Y. Levy, Alp Yurtsever, Volkan Cevher
Online Adaptive Methods, Universality and Acceleration. CoRR (2018)
Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang
Faster Rates for Convex-Concave Games. COLT (2018)
Zalan Borsos, Andreas Krause, Kfir Y. Levy
Online Variance Reduction for Stochastic Optimization. COLT (2018)
Oren Anava, Kfir Y. Levy
k*-Nearest Neighbors: From Global to Local. CoRR (2017)
Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause
An Online Learning Approach to Generative Adversarial Networks. CoRR (2017)
An Bian, Kfir Y. Levy, Andreas Krause, Joachim M. Buhmann
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. CoRR (2017)
Kfir Y. Levy
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes. CoRR (2017)
Kfir Y. Levy
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes. NIPS (2017)
Oren Anava, Kfir Y. Levy
k*-Nearest Neighbors: From Global to Local. NIPS (2016)
Kfir Y. Levy
The Power of Normalization: Faster Evasion of Saddle Points. CoRR (2016)
Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz
Beyond Convexity: Stochastic Quasi-Convex Optimization. NIPS (2015)
Tomer Koren, Kfir Y. Levy
Fast Rates for Exp-concave Empirical Risk Minimization. NIPS (2015)
Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz
Beyond Convexity: Stochastic Quasi-Convex Optimization. CoRR (2015)
Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz
On Graduated Optimization for Stochastic Non-Convex Problems. CoRR (2015)
Elad Hazan, Kfir Y. Levy
Bandit Convex Optimization: Towards Tight Bounds. NIPS (2014)
Elad Hazan, Tomer Koren, Kfir Y. Levy
Logistic Regression: Tight Bounds for Stochastic and Online Optimization. COLT (2014)
Elad Hazan, Tomer Koren, Kfir Y. Levy
Logistic Regression: Tight Bounds for Stochastic and Online Optimization. CoRR (2014)
Kfir Y. Levy, Nahum Shimkin
Unified Inter and Intra Options Learning Using Policy Gradient Methods. EWRL (2011)