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ORCIDKfir Y. Levy
Kfir Levy – Kfir Yehuda Levy
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- affiliation: Technion, Haifa, Faculty of Industrial Engineering and Management
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Journal Articles
- 2025
[j4]Leena Heistrene
, Juri Belikov, Dmitry Baimel, Liran Katzir, Ram Machlev, Kfir Levy, Shie Mannor, Yoash Levron:
An Improved and Explainable Electricity Price Forecasting Model via SHAP-Based Error Compensation Approach. IEEE Trans. Artif. Intell. 6(1): 159-168 (2025)- 2024
- [j3]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): 203-268 (2024) - 2022
- [j2]Ram Machlev, Michael Perl, Juri Belikov, Kfir Yehuda Levy, Yoash Levron:
Measuring Explainability and Trustworthiness of Power Quality Disturbances Classifiers Using XAI - Explainable Artificial Intelligence. IEEE Trans. Ind. Informatics 18(8): 5127-5137 (2022) - 2020
- [j1]Pragnya Alatur, Kfir Y. Levy, Andreas Krause:
Multi-Player Bandits: The Adversarial Case. J. Mach. Learn. Res. 21: 77:1-77:23 (2020)
Conference and Workshop Papers
- 2025
- [c50]Bassel Hamoud, Ilnura Usmanova, Kfir Yehuda Levy:
Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing Constraints. AISTATS 2025: 3178-3186 - [c49]Tehila Dahan, Kfir Yehuda Levy:
Do Stochastic, Feel Noiseless: Stable Stochastic Optimization via a Double Momentum Mechanism. ICLR 2025 - [c48]Navdeep Kumar, Yashaswini Murthy, Itai Shufaro, Kfir Yehuda Levy, R. Srikant, Shie Mannor:
Global Convergence of Policy Gradient in Average Reward MDPs. ICLR 2025 - 2024
- [c47]Uri Gadot, Esther Derman, Navdeep Kumar, Maxence Mohamed Elfatihi, Kfir Levy, Shie Mannor:
Solving Non-rectangular Reward-Robust MDPs via Frequency Regularization. AAAI 2024: 21090-21098 - [c46]Navdeep Kumar, Priyank Agrawal, Kfir Yehuda Levy, Shie Mannor:
Policy Gradient with Tree Search (PGTS) in Reinforcement Learning Evades Local Maxima. Tiny Papers @ ICLR 2024 - [c45]Navdeep Kumar, Ilnura Usmanova, Kfir Yehuda Levy, Shie Mannor:
Towards Faster Global Convergence of Robust Policy Gradient Methods. Tiny Papers @ ICLR 2024 - [c44]Navdeep Kumar, Kaixin Wang, Uri Gadot, Kfir Yehuda Levy, Shie Mannor:
Learning the Uncertainty Set in Robust Markov Decision Process. Tiny Papers @ ICLR 2024 - [c43]Navdeep Kumar, Kaixin Wang, Utkarsh Pratiush, Kfir Yehuda Levy, Shie Mannor:
Policy Gradient for Reinforcement Learning with General Utilities. Tiny Papers @ ICLR 2024 - [c42]Tehila Dahan, Kfir Yehuda Levy:
Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training. ICML 2024 - [c41]Ron Dorfman, Naseem Yehya, Kfir Yehuda Levy:
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers. ICML 2024 - [c40]Uri Gadot, Kaixin Wang, Navdeep Kumar, Kfir Yehuda Levy, Shie Mannor:
Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel. ICML 2024 - [c39]Nadav Hallak, Kfir Yehuda Levy:
A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle. ICML 2024 - [c38]Navdeep Kumar, Kaixin Wang, Kfir Yehuda Levy, Shie Mannor:
Efficient Value Iteration for s-rectangular Robust Markov Decision Processes. ICML 2024 - [c37]Roie Reshef, Kfir Yehuda Levy:
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems. ICML 2024 - [c36]Tehila Dahan, Kfir Y. Levy:
Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML. NeurIPS 2024 - [c35]Tehila Dahan, Kfir Y. Levy:
SLowcalSGD : Slow Query Points Improve Local-SGD for Stochastic Convex Optimization. NeurIPS 2024 - 2023
- [c34]Tom Norman, Nir Weinberger, Kfir Y. Levy:
Robust Linear Regression for General Feature Distribution. AISTATS 2023: 2405-2435 - [c33]Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Yehuda Levy:
DoCoFL: Downlink Compression for Cross-Device Federated Learning. ICML 2023: 8356-8388 - [c32]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 - [c31]Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandit Algorithms. NeurIPS 2023 - [c30]Navdeep Kumar, Esther Derman, Matthieu Geist, Kfir Y. Levy, Shie Mannor:
Policy Gradient for Rectangular Robust Markov Decision Processes. NeurIPS 2023 - 2022
- [c29]Ali Kavis, Kfir Yehuda Levy, Volkan Cevher:
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize. ICLR 2022 - [c28]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: 772-795 - [c27]Ron Dorfman, Kfir Yehuda Levy:
Adapting to Mixing Time in Stochastic Optimization with Markovian Data. ICML 2022: 5429-5446 - 2021
- [c26]Ido Hakimi, Rotem Zamir Aviv, Kfir Y. Levy, Assaf Schuster:
LAGA: Lagged AllReduce with Gradient Accumulation for Minimal Idle Time. ICDM 2021: 171-180 - [c25]Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Yehuda Levy:
Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge. ICML 2021: 436-445 - [c24]Ilnura Usmanova, Maryam Kamgarpour, Andreas Krause, Kfir Y. Levy:
Fast Projection Onto Convex Smooth Constraints. ICML 2021: 10476-10486 - [c23]Kfir Y. Levy, Ali Kavis, Volkan Cevher:
STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization. NeurIPS 2021: 20571-20582 - [c22]Menachem Adelman, Kfir Y. Levy, Ido Hakimi, Mark Silberstein:
Faster Neural Network Training with Approximate Tensor Operations. NeurIPS 2021: 27877-27889 - 2020
- [c21]Dan Garber, Gal Korcia, Kfir Y. Levy:
Online Convex Optimization in the Random Order Model. ICML 2020: 3387-3396 - [c20]Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause:
Adaptive Sampling for Stochastic Risk-Averse Learning. NeurIPS 2020 - 2019
- [c19]Kfir Y. Levy, Andreas Krause:
Projection Free Online Learning over Smooth Sets. AISTATS 2019: 1458-1466 - [c18]Sebastian Curi, Kfir Y. Levy, Andreas Krause:
Adaptive Input Estimation in Linear Dynamical Systems with Applications to Learning-from-Observations. CDC 2019: 4115-4120 - [c17]Francis R. Bach, Kfir Y. Levy:
A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise. COLT 2019: 164-194 - [c16]Zalán Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause:
Online Variance Reduction with Mixtures. ICML 2019: 705-714 - [c15]Ali Kavis, Kfir Y. Levy, Francis R. Bach, Volkan Cevher:
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization. NeurIPS 2019: 6257-6266 - [c14]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Ian J. Goodfellow, Thomas Hofmann, Andreas Krause:
A Domain Agnostic Measure for Monitoring and Evaluating GANs. NeurIPS 2019: 12069-12079 - 2018
- [c13]Zalan Borsos, Andreas Krause, Kfir Y. Levy:
Online Variance Reduction for Stochastic Optimization. COLT 2018: 324-357 - [c12]Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang:
Faster Rates for Convex-Concave Games. COLT 2018: 1595-1625 - [c11]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause:
An Online Learning Approach to Generative Adversarial Networks. ICLR (Poster) 2018 - [c10]Kfir Yehuda Levy, Alp Yurtsever, Volkan Cevher:
Online Adaptive Methods, Universality and Acceleration. NeurIPS 2018: 6501-6510 - 2017
- [c9]An Bian, Kfir Yehuda Levy, Andreas Krause, Joachim M. Buhmann:
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. NIPS 2017: 486-496 - [c8]Kfir Y. Levy:
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes. NIPS 2017: 1613-1622 - 2016
- [c7]Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz:
On Graduated Optimization for Stochastic Non-Convex Problems. ICML 2016: 1833-1841 - [c6]Oren Anava, Kfir Y. Levy:
k*-Nearest Neighbors: From Global to Local. NIPS 2016: 4916-4924 - 2015
- [c5]Tomer Koren, Kfir Y. Levy:
Fast Rates for Exp-concave Empirical Risk Minimization. NIPS 2015: 1477-1485 - [c4]Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz:
Beyond Convexity: Stochastic Quasi-Convex Optimization. NIPS 2015: 1594-1602 - 2014
- [c3]Elad Hazan, Tomer Koren, Kfir Y. Levy:
Logistic Regression: Tight Bounds for Stochastic and Online Optimization. COLT 2014: 197-209 - [c2]Elad Hazan, Kfir Y. Levy:
Bandit Convex Optimization: Towards Tight Bounds. NIPS 2014: 784-792 - 2011
- [c1]Kfir Y. Levy, Nahum Shimkin:
Unified Inter and Intra Options Learning Using Policy Gradient Methods. EWRL 2011: 153-164
Informal and Other Publications
- 2025
- [i51]Tehila Dahan, Kfir Y. Levy:
Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML. CoRR abs/2501.09621 (2025) - [i50]Amro Abu Saleh, Elliot Sprecher, Kfir Y. Levy, Daniel H. Lange:
DE-PADA: Personalized Augmentation and Domain Adaptation for ECG Biometrics Across Physiological States. CoRR abs/2502.04973 (2025) - [i49]Navdeep Kumar, Adarsh Gupta, Maxence Mohamed Elfatihi, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor:
Dual Formulation for Non-Rectangular Lp Robust Markov Decision Processes. CoRR abs/2502.09432 (2025) - [i48]Bassel Hamoud, Ilnura Usmanova, Kfir Y. Levy:
Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing Constraints. CoRR abs/2505.00398 (2025) - [i47]Ofri Eisen, Ron Dorfman, Kfir Y. Levy:
Enhancing Parallelism in Decentralized Stochastic Convex Optimization. CoRR abs/2506.00961 (2025) - [i46]Roie Reshef, Kfir Yehuda Levy:
Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation. CoRR abs/2506.02563 (2025) - [i45]Uri Koren, Navdeep Kumar, Uri Gadot, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor:
Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead. CoRR abs/2506.07054 (2025) - [i44]Ze'ev Zukerman, Bassel Hamoud, Kfir Y. Levy:
Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning. CoRR abs/2507.05508 (2025) - 2024
- [i43]Ron Dorfman, Naseem Yehya, Kfir Y. Levy:
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers. CoRR abs/2402.02951 (2024) - [i42]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 abs/2403.06806 (2024) - [i41]Tehila Dahan, Kfir Y. Levy:
Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training. CoRR abs/2405.14759 (2024) - [i40]Roie Reshef, Kfir Y. Levy:
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems. CoRR abs/2407.12396 (2024) - [i39]Moran Shkolnik, Maxim Fishman, Brian Chmiel, Hilla Ben-Yaacov, Ron Banner, Kfir Yehuda Levy:
EXAQ: Exponent Aware Quantization For LLMs Acceleration. CoRR abs/2410.03185 (2024) - [i38]Navdeep Kumar, Priyank Agrawal, Giorgia Ramponi, Kfir Yehuda Levy, Shie Mannor:
Improved Sample Complexity for Global Convergence of Actor-Critic Algorithms. CoRR abs/2410.08868 (2024) - 2023
- [i37]Navdeep Kumar, Esther Derman, Matthieu Geist, Kfir Levy, Shie Mannor:
Policy Gradient for s-Rectangular Robust Markov Decision Processes. CoRR abs/2301.13589 (2023) - [i36]Navdeep Kumar, Kfir Levy, Kaixin Wang, Shie Mannor:
An Efficient Solution to s-Rectangular Robust Markov Decision Processes. CoRR abs/2301.13642 (2023) - [i35]Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Y. Levy:
DoCoFL: Downlink Compression for Cross-Device Federated Learning. CoRR abs/2302.00543 (2023) - [i34]Kfir Y. Levy:
SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization. CoRR abs/2304.04169 (2023) - [i33]Kfir Y. Levy:
μ2-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism. CoRR abs/2304.04172 (2023) - [i32]Kaixin Wang, Uri Gadot, Navdeep Kumar, Kfir Levy, Shie Mannor:
Robust Reinforcement Learning via Adversarial Kernel Approximation. CoRR abs/2306.05859 (2023) - [i31]Niv Giladi, Shahar Gottlieb, Moran Shkolnik, Asaf Karnieli, Ron Banner, Elad Hoffer, Kfir Yehuda Levy, Daniel Soudry:
DropCompute: simple and more robust distributed synchronous training via compute variance reduction. CoRR abs/2306.10598 (2023) - [i30]Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandit Algorithms. CoRR abs/2307.02295 (2023) - [i29]Uri Gadot, Esther Derman, Navdeep Kumar, Maxence Mohamed Elfatihi, Kfir Levy, Shie Mannor:
Solving Non-Rectangular Reward-Robust MDPs via Frequency Regularization. CoRR abs/2309.01107 (2023) - 2022
- [i28]Tom Norman, Nir Weinberger, Kfir Y. Levy:
Robust Linear Regression for General Feature Distribution. CoRR abs/2202.02080 (2022) - [i27]Ron Dorfman, Kfir Y. Levy:
Adapting to Mixing Time in Stochastic Optimization with Markovian Data. CoRR abs/2202.04428 (2022) - [i26]Ali Kavis, Kfir Yehuda Levy, Volkan Cevher:
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize. CoRR abs/2204.02833 (2022) - [i25]Navdeep Kumar, Kfir Levy, Kaixin Wang, Shie Mannor:
Efficient Policy Iteration for Robust Markov Decision Processes via Regularization. CoRR abs/2205.14327 (2022) - [i24]Ilya Osadchiy, Kfir Y. Levy, Ron Meir:
Online Meta-Learning in Adversarial Multi-Armed Bandits. CoRR abs/2205.15921 (2022) - [i23]Navdeep Kumar, Kaixin Wang, Kfir Levy, Shie Mannor:
Policy Gradient for Reinforcement Learning with General Utilities. CoRR abs/2210.00991 (2022) - 2021
- [i22]Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann:
Generative Minimization Networks: Training GANs Without Competition. CoRR abs/2103.12685 (2021) - [i21]Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster, Kfir Y. Levy:
Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays. CoRR abs/2106.12261 (2021) - [i20]Kfir Y. Levy, Ali Kavis, Volkan Cevher:
STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization. CoRR abs/2111.01040 (2021) - [i19]Jun-Kun Wang, Jacob D. Abernethy, Kfir Y. Levy:
No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimization. CoRR abs/2111.11309 (2021) - 2019
- [i18]Francis R. Bach, Kfir Y. Levy:
A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise. CoRR abs/1902.01637 (2019) - [i17]Pragnya Alatur, Kfir Y. Levy, Andreas Krause:
Multi-Player Bandits: The Adversarial Case. CoRR abs/1902.08036 (2019) - [i16]Zalán Borsos, Sebastian Curi, Kfir Y. Levy, Andreas Krause:
Online Variance Reduction with Mixtures. CoRR abs/1903.12416 (2019) - [i15]Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause:
Adaptive Sampling for Stochastic Risk-Averse Learning. CoRR abs/1910.12511 (2019) - [i14]Ali Kavis, Kfir Y. Levy, Francis R. Bach, Volkan Cevher:
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization. CoRR abs/1910.13857 (2019) - 2018
- [i13]Zalán Borsos, Andreas Krause, Kfir Y. Levy:
Online Variance Reduction for Stochastic Optimization. CoRR abs/1802.04715 (2018) - [i12]Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang:
Faster Rates for Convex-Concave Games. CoRR abs/1805.06792 (2018) - [i11]Sebastian Curi, Kfir Y. Levy, Andreas Krause:
Unsupervised Imitation Learning. CoRR abs/1806.07200 (2018) - [i10]Kfir Y. Levy, Alp Yurtsever, Volkan Cevher:
Online Adaptive Methods, Universality and Acceleration. CoRR abs/1809.02864 (2018) - [i9]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Thomas Hofmann, Andreas Krause:
Evaluating GANs via Duality. CoRR abs/1811.05512 (2018) - 2017
- [i8]Oren Anava, Kfir Y. Levy:
k*-Nearest Neighbors: From Global to Local. CoRR abs/1701.07266 (2017) - [i7]Kfir Y. Levy:
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes. CoRR abs/1705.10499 (2017) - [i6]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause:
An Online Learning Approach to Generative Adversarial Networks. CoRR abs/1706.03269 (2017) - [i5]An Bian, Kfir Y. Levy, Andreas Krause, Joachim M. Buhmann:
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. CoRR abs/1711.02515 (2017) - 2016
- [i4]Kfir Y. Levy:
The Power of Normalization: Faster Evasion of Saddle Points. CoRR abs/1611.04831 (2016) - 2015
- [i3]Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz:
On Graduated Optimization for Stochastic Non-Convex Problems. CoRR abs/1503.03712 (2015) - [i2]Elad Hazan, Kfir Y. Levy, Shai Shalev-Shwartz:
Beyond Convexity: Stochastic Quasi-Convex Optimization. CoRR abs/1507.02030 (2015) - 2014
- [i1]Elad Hazan, Tomer Koren, Kfir Y. Levy:
Logistic Regression: Tight Bounds for Stochastic and Online Optimization. CoRR abs/1405.3843 (2014)
Coauthor Index
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