default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDKfir Y. Levy
Kfir Levy – Kfir Yehuda Levy
Person information
- affiliation: Technion, Haifa, Faculty of Industrial Engineering and Management
SPARQL queries 
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 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)- [c45]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 - [c44]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 - [c43]Navdeep Kumar, Ilnura Usmanova, Kfir Yehuda Levy, Shie Mannor:
Towards Faster Global Convergence of Robust Policy Gradient Methods. Tiny Papers @ ICLR 2024 - [c42]Navdeep Kumar, Kaixin Wang, Uri Gadot, Kfir Yehuda Levy, Shie Mannor:
Learning the Uncertainty Set in Robust Markov Decision Process. Tiny Papers @ ICLR 2024 - [c41]Navdeep Kumar, Kaixin Wang, Utkarsh Pratiush, Kfir Yehuda Levy, Shie Mannor:
Policy Gradient for Reinforcement Learning with General Utilities. Tiny Papers @ ICLR 2024 - [c40]Tehila Dahan, Kfir Yehuda Levy:
Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training. ICML 2024 - [c39]Ron Dorfman, Naseem Yehya, Kfir Yehuda Levy:
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers. ICML 2024 - [c38]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 - [c37]Nadav Hallak, Kfir Yehuda Levy:
A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle. ICML 2024 - [c36]Navdeep Kumar, Kaixin Wang, Kfir Yehuda Levy, Shie Mannor:
Efficient Value Iteration for s-rectangular Robust Markov Decision Processes. ICML 2024 - [c35]Roie Reshef, Kfir Yehuda Levy:
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems. ICML 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
- [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 - [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
- [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) - [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 - [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
- [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 - [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) - 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) - [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
2010 – 2019
- 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 Goodfellow, Thomas Hofmann, Andreas Krause:
A Domain Agnostic Measure for Monitoring and Evaluating GANs. NeurIPS 2019: 12069-12079 - [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
- [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 - [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
- [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 - [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
- [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 - [i4]Kfir Y. Levy:
The Power of Normalization: Faster Evasion of Saddle Points. CoRR abs/1611.04831 (2016) - 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 - [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
- [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 - [i1]Elad Hazan, Tomer Koren, Kfir Y. Levy:
Logistic Regression: Tight Bounds for Stochastic and Online Optimization. CoRR abs/1405.3843 (2014) - 2011
- [c1]Kfir Y. Levy, Nahum Shimkin:
Unified Inter and Intra Options Learning Using Policy Gradient Methods. EWRL 2011: 153-164
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-01-07 01:15 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
