Kfir Y. Levy
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.
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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 Hazan. Before that, I completed my master's at the EE Department of the Technion under the guidance of Prof. Nahum Shimkin.
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Office: Fishbach, 459 Contact: kfirylevy@technion.ac.il
Publications
No-Regret Dynamics in the Fenchel Game: A Unified Framework
for Algorithmic Convex Optimization
Jun-Kun Wang, Jacob Abernethy and Kfir Y. Levy
In Mathematical Programming, 2023. [arXiv]
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DocoFL : Downlink Compression for cross-device
Federated Learning
Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, and Kfir Y. Levy
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Robust linear regression for general feature distribution
Tom Norman, Nir Weinberger and Kfir Y. Levy
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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
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Adapting to Mixing Time in Stochastic Optimization with Markovian Data
Ron Dorfman, and Kfir Y. Levy
In ICML 2022. [pdf][arXiv], Spotlight Presentation
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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
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High probability bounds for a class of nonconvex algorithms with adagrad stepsize
Kfir Y. Levy, Ali Kavis and Volkan Cevher
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Faster Neural Network Training with Approximate Tensor Operations
Menachem Adelman, Kfir Y. Levy, Ido Hakimi and Mark Silberstein
In NeurIPS 2021. [pdf]
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STORM+: Fully Adaptive SGD with Momentum for Non-convex Optimization Kfir Y. Levy, Ali Kavis and Volkan Cevher
In NeurIPS 2021. [pdf][arXiv]
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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]
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LAGA: Lagged AllReduce with Gradient Accumulation for Minimal Idle Time Ido Hakimi, Rotem Aviv, Kfir Y. Levy and Assaf Schuster
In ICDM 2021. [pdf]
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Learning under Delayed Feedback: Implicitly Adapting to Gradient Delays Rotem Aviv, Ido Hakimi, Assaf Schuster and Kfir Y. Levy
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Fast Projection onto Convex Smooth Constraints
Ilnura Usmanuva, Kfir Y. Levy, Maryam Kamgarpour and Andreas Krause.
In ICML 2021. [pdf]
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Multi-Player Bandits: The Adversarial Case
Pragnya Alatur, Kfir Y. Levy and Andreas Krause.
In JMLR 2020. [arXiv]
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Adaptive Sampling for Stochastic Risk-averse Learning
Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka and Andreas Krause.
In NeurIPS 2020. [pdf]
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Online Convex Optimization In the Random Order Model
Dan Garber, Gal Korcia and Kfir Y. Levy
In ICML 2020. [pdf]
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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]
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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]
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Online Variance Reduction with Mixtures
Zalán Borsos, Sebastian Curi, Kfir Y. Levy, and Andreas Krause.
In ICML 2019. [pdf]
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A Universal Algorithm for Variational Inequalities Adaptive to Smoothness and Noise
Francis Bach and Kfir Y. Levy.
In COLT 2019. [arXiv]
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Unsupervised Imitation Learning
Sebastian Curi, Kfir Y. Levy, and Andreas Krause.
To appear in CDC 2019. [arXiv]
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Projection Free Online Learning over Smooth Sets
Kfir Y. Levy and Andreas Krause.
In AISTATS 2019. [pdf]
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Online Adaptive Methods, Universality and Acceleration
Kfir Y. Levy, Alp Yurtsever, and Volkan Cevher.
In NeurIPS 2018. [pdf] [arXiv]
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Online Variance Reduction for Stochastic Optimization
Zalán Borsos, Andreas Krause, and Kfir Y. Levy.
In COLT 2018. [pdf] [arXiv] [Code]
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Faster Rates for Convex-Concave Games
Jacob Abernethy, Kevin A. Lai, Kfir Y. Levy, and Jun-Kun Wang.
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An Online Learning Approach to Generative Adversarial Networks
Paulina Grnarova, Kfir Y. Levy, Aurelien Lucchi, Thomas Hofmann, and Andreas Krause.
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Online to Offline Conversions, Universality and Adaptive Minibatch Sizes
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Continuous DR-submodular Maximization: Structure and Algorithms
An Bian, Kfir Y. Levy, Andreas Krause, and Joachim M. Buhmann.
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k*-Nearest Neighbors: From Global to Local
Oren Anava and Kfir Y. Levy.
In NIPS 2016. [pdf] [arXiv] [Code]
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On Graduated Optimization for Stochastic Non-Convex Problems
Elad Hazan, Kfir Y. Levy, and Shai Shalev-Shwartz.
In ICML 2016. [pdf] [arXiv][Code]
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Faster Evasion of Saddle Points.
Preprint 2016.[arXiv]
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Beyond Convexity: Stochastic Quasi-Convex Optimization
Elad Hazan, Kfir Y. Levy, and Shai Shalev-Shwartz.
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Fast Rates for Exp-concave Empirical Risk Minimization
Tomer Koren and Kfir Y. Levy.
In NIPS 2015. [pdf]
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Bandit Convex Optimization: Towards Tight Bounds
Elad Hazan and Kfir Y. Levy.
In NIPS 2014. [pdf] [Full Version]
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Logistic Regression: Tight Bounds for Stochastic and Online Optimization
Elad Hazan, Tomer Koren and Kfir Y. Levy.
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Unified Inter and Intra Options Learning Using Policy Gradient Methods
Kfir Y. Levy and Nahum Shimkin.
In EWRL 2011. [pdf]
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