Yair Carmon

According to our database, Yair Carmon authored at least 45 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Language models scale reliably with over-training and on downstream tasks.

Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, Suchin Gururangan, Mitchell Wortsman, Rulin Shao, Jean Mercat, Alex Fang, Jeffrey Li, Sedrick Keh, Rui Xin, Marianna Nezhurina, Igor Vasiljevic, Jenia Jitsev, Alexandros G. Dimakis, Gabriel Ilharco, Shuran Song, Thomas Kollar, Yair Carmon, Achal Dave, Reinhard Heckel, Niklas Muennighoff, Ludwig Schmidt

CoRR, 2024

The Price of Adaptivity in Stochastic Convex Optimization.

Yair Carmon, Oliver Hinder

CoRR, 2024

A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions.

Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford

Proceedings of the 2024 ACM-SIAM Symposium on Discrete Algorithms, 2024

2023

Lower bounds for non-convex stochastic optimization.

Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth

Math. Program., May, 2023

DataComp: In search of the next generation of multimodal datasets.

Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah M. Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander J. Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond.

Itai Kreisler, Mor Shpigel Nacson, Daniel Soudry, Yair Carmon

Proceedings of the International Conference on Machine Learning, 2023

DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule.

Maor Ivgi, Oliver Hinder, Yair Carmon

Proceedings of the International Conference on Machine Learning, 2023

Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds.

Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon

Proceedings of the Eleventh International Conference on Learning Representations, 2023

ReSQueing Parallel and Private Stochastic Convex Optimization.

Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian

Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

2022

Malign Overfitting: Interpolation Can Provably Preclude Invariance.

Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon

CoRR, 2022

Optimal and Adaptive Monteiro-Svaiter Acceleration.

Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Distributionally Robust Optimization via Ball Oracle Acceleration.

Yair Carmon, Danielle Hausler

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

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

Proceedings of the International Conference on Machine Learning, 2022

RECAPP: Crafting a More Efficient Catalyst for Convex Optimization.

Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford

Proceedings of the International Conference on Machine Learning, 2022

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

Maor Ivgi, Yair Carmon, Jonathan Berant

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Making SGD Parameter-Free.

Yair Carmon, Oliver Hinder

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Lower bounds for finding stationary points II: first-order methods.

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

Math. Program., 2021

Stochastic Bias-Reduced Gradient Methods.

Hilal Asi, Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Never Go Full Batch (in Stochastic Convex Optimization).

Idan Amir, Yair Carmon, Tomer Koren, Roi Livni

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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, Yair Carmon, Ludwig Schmidt

Proceedings of the 38th International Conference on Machine Learning, 2021

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

Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford

Proceedings of the Conference on Learning Theory, 2021

2020

First-Order Methods for Nonconvex Quadratic Minimization.

Yair Carmon, John C. Duchi

SIAM Rev., 2020

Lower bounds for finding stationary points I.

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

Math. Program., 2020

Large-Scale Methods for Distributionally Robust Optimization.

Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Acceleration with a Ball Optimization Oracle.

Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Coordinate Methods for Matrix Games.

Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian

Proceedings of the 61st IEEE Annual Symposium on Foundations of Computer Science, 2020

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

Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan

Proceedings of the Conference on Learning Theory, 2020

2019

Gradient Descent Finds the Cubic-Regularized Nonconvex Newton Step.

Yair Carmon, John C. Duchi

SIAM J. Optim., 2019

Unlabeled Data Improves Adversarial Robustness.

Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Variance Reduction for Matrix Games.

Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Rank-1 Sketch for Matrix Multiplicative Weights.

Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian

Proceedings of the Conference on Learning Theory, 2019

2018

Accelerated Methods for NonConvex Optimization.

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

SIAM J. Optim., 2018

Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems.

Yair Carmon, John C. Duchi

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

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

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Information, Estimation, and Lookahead in the Gaussian Channel.

Kartik Venkat, Tsachy Weissman, Yair Carmon, Shlomo Shamai

IEEE Trans. Signal Process., 2016

No bad local minima: Data independent training error guarantees for multilayer neural networks.

Daniel Soudry, Yair Carmon

CoRR, 2016

Accelerated Methods for Non-Convex Optimization.

Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford

CoRR, 2016

Gradient Descent Efficiently Finds the Cubic-Regularized Non-Convex Newton Step.

Yair Carmon, John C. Duchi

CoRR, 2016

2015

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

Yair Carmon, Shlomo Shamai, Tsachy Weissman

IEEE Trans. Inf. Theory, 2015

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

Yair Carmon, Shlomo Shamai

IEEE Trans. Inf. Theory, 2015

2013

The role of lookahead in estimation under Gaussian noise.

Kartik Venkat, Tsachy Weissman, Yair Carmon, Shlomo Shamai

Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

2012

On information, estimation and lookahead.

Kartik Venkat, Tsachy Weissman, Yair Carmon, Shlomo Shamai

Proceedings of the 50th Annual Allerton Conference on Communication, 2012

2009

Markov decision processes with exponentially representable discounting.

Yair Carmon, Adam Shwartz

Oper. Res. Lett., 2009

Partial Similarity of Shapes Using a Statistical Significance Measure.

Alexander M. Bronstein, Michael M. Bronstein, Yair Carmon, Ron Kimmel

IPSJ Trans. Comput. Vis. Appl., 2009

2008

Eventually-stationary policies for Markov decision models with non-constant discounting.

Yair Carmon, Adam Shwartz

Proceedings of the 3rd International ICST Conference on Performance Evaluation Methodologies and Tools, 2008