top of page

I am an Assistant Professor at the ECE Department of the Technion


My research is in Machine Learning and Optimization. I am focused on the design and analysis of efficient algorithms for a wide class of Machine Learning and decision making scenarios.

Short bio:

I did my post-doc at the Institute for Machine Learning at ETHZ working with Prof. Andreas Krause.  Previously, I did my PhD at the IE&M Department of the Technion, working under the guidance of Prof. Elad HazanBefore that, I completed my master's at the EE Department of the Technion under the guidance of Prof. Nahum Shimkin.

Office: Fishbach, 459     Contact:




No-Regret Dynamics in the Fenchel Game: A Unified Framework

for Algorithmic Convex Optimization              

     Jun-Kun WangJacob Abernethy and Kfir Y. Levy

     In Mathematical Programming, 2023[arXiv]

DocoFL : Downlink Compression for cross-device

Federated Learning

    Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, and Kfir Y. Levy

     In ICML 2023. [arXiv]

Robust linear regression for general feature distribution

    Tom Norman, Nir Weinberger and Kfir Y. Levy

     In AISTATS 2023. [pdf][arXiv]

Explainable artificial intelligence (xai) techniques for energy and power systems: Review, challenges and opportunities.                    

     Ram Machlev, Leena Heistrene, Michael Perl, Kfir Y. Levy, Juri Belikov, Shie Mannor and Yoash Levron

     In Energy and AI 2022[pdf

Adapting to Mixing Time in Stochastic Optimization with Markovian Data

    Ron Dorfman, and Kfir Y. Levy

     In ICML 2022. [pdf][arXiv],  Spotlight Presentation

UNDERGRAD: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees

    Kimon Antonakopoulos, Dong Quan Vu, Volkan Cevher, Kfir Y. Levy, and Panayotis Mertikopoulos

     In ICML 2022. [pdf][arXiv],  Spotlight Presentation 

High probability bounds for a class of nonconvex algorithms with adagrad stepsize

    Kfir Y. Levy, Ali Kavis and Volkan Cevher

     In ICLR 2022[pdf][arXiv]

Faster Neural Network Training with Approximate Tensor Operations                      

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

     In NeurIPS 2021[pdf]

STORM+: Fully Adaptive SGD with Momentum for Non-convex Optimization                                                 Kfir Y. Levy, Ali Kavis and Volkan Cevher

     In NeurIPS 2021[pdf][arXiv]


Learning Measuring Explainability and Trustworthiness of Power Quality

Disturbances Classifiers Using XAI - Explainable Artificial Intelligence                        

     Ram Machlev, Michael Perl, Juri Belikov, Kfir Y. Levy and Yoash Levron

     In IEEE Transactions on Industrial Informatics 2021[pdf


LAGA: Lagged AllReduce with Gradient Accumulation for Minimal Idle Time                                                   Ido Hakimi, Rotem Aviv,  Kfir Y. Levy and Assaf Schuster 

     In ICDM 2021. [pdf]


Learning under Delayed Feedback: Implicitly Adapting to Gradient Delays                                                       Rotem Aviv, Ido Hakimi, Assaf Schuster and Kfir Y. Levy

     In ICML 2021. [pdf[arXiv


Fast Projection onto Convex Smooth Constraints

    Ilnura Usmanuva, Kfir Y. Levy, Maryam Kamgarpour and Andreas Krause.

     In ICML 2021. [pdf


Multi-Player Bandits: The Adversarial Case

    Pragnya Alatur, Kfir Y. Levy and Andreas Krause.

     In JMLR 2020. [arXiv


Adaptive Sampling for Stochastic Risk-averse Learning 

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

    In NeurIPS 2020. [pdf] 


Online Convex Optimization In the Random Order Model

    Dan Garber, Gal Korcia and Kfir Y. Levy 

    In ICML 2020.  [pdf]


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

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

    To appear In NeurIPS 2019. [arXiv


Evaluating GANs via Duality

    Paulina Grnarova, Kfir Y. Levy, Aurelien Lucchi, Nathanael Perraudin,

     Ian Goodfellow, Thomas Hofmann and Andreas Krause.

     To appear in NeurIPS 2019. [arXiv


Online Variance Reduction with Mixtures

    Zalán BorsosSebastian Curi, Kfir Y. Levy, and Andreas Krause.

    In ICML 2019. [pdf] 


A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise

    Francis Bach and Kfir Y. Levy. 

     In COLT 2019. [arXiv


Unsupervised Imitation Learning

    Sebastian Curi, Kfir Y. Levy, and Andreas Krause.

    To appear in CDC 2019. [arXiv] 


Projection Free Online Learning over Smooth Sets

    Kfir Y. Levy and Andreas Krause. 

    In AISTATS 2019. [pdf]


Online Adaptive Methods, Universality and Acceleration 

    Kfir Y. Levy, Alp Yurtsever, and Volkan Cevher. 

    In NeurIPS 2018. [pdf[arXiv


Online Variance Reduction for Stochastic Optimization

    Zalán BorsosAndreas Krause, and Kfir Y. Levy.

    In COLT 2018. [pdf] [arXiv] [Code]


Faster Rates for Convex-Concave Games

    Jacob AbernethyKevin A. LaiKfir Y. Levy, and Jun-Kun Wang.

    In COLT 2018. [pdf] [arXiv] 


An Online Learning Approach to Generative Adversarial Networks

    Paulina GrnarovaKfir Y. LevyAurelien LucchiThomas Hofmann,  and Andreas Krause.

    In ICLR 2018. [pdf][arXiv


Online to Offline Conversions, Universality and Adaptive Minibatch Sizes

    Kfir Y. Levy.

    In NIPS 2017. [pdf][arXiv


Continuous DR-submodular Maximization: Structure and Algorithms

    An BianKfir Y. LevyAndreas Krause, and Joachim M. Buhmann.

    In NIPS 2017. [pdf[arXiv


k*-Nearest Neighbors: From Global to Local

    Oren Anava and Kfir Y. Levy.

    In NIPS 2016. [pdf[arXiv] [Code]


On Graduated Optimization for Stochastic Non-Convex Problems

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

    In ICML 2016. [pdf[arXiv][Code]


Faster Evasion of Saddle Points.

     Kfir Y. Levy.

     Preprint 2016.[arXiv


Beyond Convexity: Stochastic Quasi-Convex Optimization

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

    In NIPS 2015. [pdf[arXiv]


Fast Rates for Exp-concave Empirical Risk Minimization

    Tomer Koren and Kfir Y. Levy.

    In NIPS 2015. [pdf]


Bandit Convex Optimization: Towards Tight Bounds

    Elad Hazan and Kfir Y. Levy.

    In NIPS 2014. [pdf] [Full Version]


Logistic Regression: Tight Bounds for Stochastic and Online Optimization

    Elad Hazan, Tomer Koren and Kfir Y. Levy.

    In COLT 2014. [pdf] [arXiv]


Unified Inter and Intra Options Learning Using Policy Gradient Methods

    Kfir Y. Levy and Nahum Shimkin.

    In EWRL 2011. [pdf]



bottom of page