Yair Carmonיאיר כרמון

ycarmon@tauex.tau.ac.il

I am an assistant professor (מרצה בכיר) in Tel Aviv University’s School of Computer Science. I completed my PhD at Stanford University in 2020, advised by John Duchi and Aaron Sidford. Prior to that, I obtained my B.Sc. and M.Sc. from the Technion, Israel Institute of Technology, where I worked with Shlomo Shamai and Tsachy Weissman.

My research interests are in machine learning, optimization, information theory, signal processing and statistics. I particularly like understanding (and getting around) fundamental limits.

My name is pronounced Yah-ear (here it is with the Hebrew pronunciation of 'r' ).

Preprints

• DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule [code]

  Maor Ivgi and Oliver Hinder.

• ReSQueing Parallel and Private Stochastic Convex Optimization

  Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford and Kevin Tian.

• Malign Overfitting: Interpolation Can Provably Preclude Invariance

  Yoav Wald, Gal Yona and Uri Shalit.

  International Conference on Learning Representations (ICLR), 2023.

Publications

• Optimal and Adaptive Monteiro-Svaiter Acceleration [code]

  Danielle Hausler, Arun Jambulapati, Yujia Jin and Aaron Sidford.

  Conference on Neural Information Processing Systems (NeurIPS), 2022.

• Distributionally Robust Optimization via Ball Oracle Acceleration

  Danielle Hausler.

  Conference on Neural Information Processing Systems (NeurIPS), 2022.

• Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments

  Maor Ivgi and Jonathan Berant.

  Findings of EMNLP, 2022.

• RECAPP: Crafting a More Efficient Catalyst for Convex Optimization [code]

  Arun Jambulapati, Yujia Jin and Aaron Sidford.

  International Conference on Machine Learning (ICML), 2022.

• Model Soups: Averaging Weights of Multiple Fine-Tuned Models Improves Accuracy Without Increasing Inference Time [code]

  Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Simon Kornblith and Ludwig Schmidt.

  International Conference on Machine Learning (ICML), 2022.

• Making SGD Parameter-Free

  Oliver Hinder.

  Conference on Learning Theory (COLT), 2022.

• Stochastic Bias-Reduced Gradient Methods

  Hilal Asi, Arun Jambulapati, Yujia Jin and Aaron Sidford.

  Conference on Neural Information Processing Systems (NeurIPS), 2021.

• Never Go Full Batch (in Stochastic Convex Optimization)

  Idan Amir, Tomer Koren and Roi Livni.

  Conference on Neural Information Processing Systems (NeurIPS), 2021.

• Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

  John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang and Ludwig Schmidt.

  International Conference on Machine Learning (ICML), 2021.

• Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss

  Arun Jambulapati, Yujia Jin and Aaron Sidford.

  Conference on Learning Theory (COLT), 2021.

• Large-Scale Methods for Distributionally Robust Optimization [code]

  Daniel Levy, John Duchi and Aaron Sidford.

  Conference on Neural Information Processing Systems (NeurIPS), 2020.

• Acceleration with a Ball Optimization Oracle

  Arun Jambulapati, Qijia Jian, Yujia Jin, Yin Tat Lee, Aaron Sidford and Kevin Tian.

  Conference on Neural Information Processing Systems (NeurIPS), 2020; oral presentation.

• Coordinate Methods for Matrix Games

  Yujia Jin, Aaron Sidford and Kevin Tian.

  IEEE Symposium on Foundations of Computer Science (FOCS), 2020.

• Lower Bounds for Non-Convex Stochastic Optimization

  Yossi Arjevani, John Duchi, Dylan Foster, Nathan Srebro and Blake Woodworth.

  Mathematical Programming, 2022.

• Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations

  Yossi Arjevani, John Duchi, Dylan Foster, Ayush Sekhari, Karthik Sridharan.

  Conference on Learning Theory (COLT), 2020.

• First-Order Methods for Nonconvex Quadratic Minimization

  John Duchi.

  SIAM Review, 2020.

• Variance Reduction for Matrix Games

  Yujia Jin, Aaron Sidford and Kevin Tian.

  Conference on Neural Information Processing Systems (NeurIPS), 2019; oral presentation.

• Unlabeled Data Improves Adversarial Robustness [code]

  Aditi Raghunathan, Ludwig Schmidt, Percy Liang and John Duchi.

  Conference on Neural Information Processing Systems (NeurIPS), 2019.

• A Rank-1 Sketch for Matrix Multiplicative Weights

  John Duchi, Aaron Sidford and Kevin Tian.

  Conference on Learning Theory (COLT), 2019.

• Analysis of Krylov Subspace Solutions of Regularized Nonconvex Quadratic Problems

  John Duchi.

  Conference on Neural Information Processing Systems (NeurIPS), 2018; oral presentation.

• Lower Bounds for Finding Stationary Points II: First-Order Methods

  John Duchi, Oliver Hinder and Aaron Sidford.

  Mathematical Programming, 2019.

• Lower Bounds For Finding Stationary Points I

  John Duchi, Oliver Hinder and Aaron Sidford.

  Mathematical Programming, 2019.

• "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions

  John Duchi, Oliver Hinder and Aaron Sidford.

  International Conference on Machine Learning (ICML), 2017.

• Gradient Descent Finds the Cubic-Regularized Non-Convex Newton Step

  John Duchi.

  SIAM Journal of Optimization, 2019.

• Accelerated Methods for Non-Convex Optimization

  John Duchi, Oliver Hinder and Aaron Sidford.

  SIAM Journal of Optimization, 2018.

• Information, Estimation, and Lookahead in the Gaussian Channel

  Kartik Venkat, Shlomo Shamai and Tsachy Weissman.

  IEEE Transactions on Signal Processing, 2016.

• Comparison of the Achievable Rates in OFDM and Single Carrier Modulation with I.I.D. Inputs

  Shlomo Shamai and Tsachy Weissman.

  IEEE Transactions on Information Theory, 2015.

• Lower Bounds and Approximations for the Information Rate of the ISI Channel

  Shlomo Shamai.

  IEEE Transactions on Information Theory, 2015.

• Partial Similarity of Shapes Using a Statistical Significance Measure

  Alexander Bronstein, Michael Bronstein and Ron Kimmel.

  IPSJ Transactions on Computer Vision and Applications, 2009.

• Markov Decision Processes with Exponentially Representable Discounting

  Adam Shwartz.

  Operations Research Letters, 2009.

Technical reports

• No Bad Local Minima: Data Independent Training Error Guarantees for Multilayer Neural Networks

  Daniel Soudry.