Eventually-stationary policies for Markov decision models with non-constant discounting. - Yair Carmon; Adam Shwartz (VALUETOOLS, 2008)

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)

Markov decision processes with exponentially representable discounting. - Yair Carmon; Adam Shwartz (Oper. Res. Lett., 2009)

On information, estimation and lookahead. - Kartik Venkat; Tsachy Weissman; Yair Carmon; Shlomo Shamai (Allerton Conference, 2012)

The role of lookahead in estimation under Gaussian noise. - Kartik Venkat; Tsachy Weissman; Yair Carmon; Shlomo Shamai (ISIT, 2013)

Lower Bounds and Approximations for the Information Rate of the ISI Channel. - Yair Carmon; Shlomo Shamai (IEEE Trans. Inf. Theory, 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)

Gradient Descent Efficiently Finds the Cubic-Regularized Non-Convex Newton Step. - Yair Carmon; John C. Duchi (CoRR, 2016)

Accelerated Methods for Non-Convex Optimization. - Yair Carmon; John C. Duchi; Oliver Hinder; Aaron Sidford (CoRR, 2016)

No bad local minima: Data independent training error guarantees for multilayer neural networks. - Daniel Soudry; Yair Carmon (CoRR, 2016)

Information, Estimation, and Lookahead in the Gaussian Channel. - Kartik Venkat; Tsachy Weissman; Yair Carmon; Shlomo Shamai (IEEE Trans. Signal Process., 2016)

"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. - Yair Carmon; John C. Duchi; Oliver Hinder; Aaron Sidford (ICML, 2017)

Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems. - Yair Carmon; John C. Duchi (NeurIPS, 2018)

Accelerated Methods for NonConvex Optimization. - Yair Carmon; John C. Duchi; Oliver Hinder; Aaron Sidford (SIAM J. Optim., 2018)

A Rank-1 Sketch for Matrix Multiplicative Weights. - Yair Carmon; John C. Duchi; Aaron Sidford; Kevin Tian (COLT, 2019)

Variance Reduction for Matrix Games. - Yair Carmon; Yujia Jin; Aaron Sidford; Kevin Tian (NeurIPS, 2019)

Unlabeled Data Improves Adversarial Robustness. - Yair Carmon; Aditi Raghunathan; Ludwig Schmidt; John C. Duchi; Percy Liang (NeurIPS, 2019)

Gradient Descent Finds the Cubic-Regularized Nonconvex Newton Step. - Yair Carmon; John C. Duchi (SIAM J. Optim., 2019)

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 (COLT, 2020)

Coordinate Methods for Matrix Games. - Yair Carmon; Yujia Jin; Aaron Sidford; Kevin Tian (FOCS, 2020)

Acceleration with a Ball Optimization Oracle. - Yair Carmon; Arun Jambulapati; Qijia Jiang; Yujia Jin; Yin Tat Lee; Aaron Sidford; Kevin Tian (NeurIPS, 2020)

Large-Scale Methods for Distributionally Robust Optimization. - Daniel Levy; Yair Carmon; John C. Duchi; Aaron Sidford (NeurIPS, 2020)

Lower bounds for finding stationary points I. - Yair Carmon; John C. Duchi; Oliver Hinder; Aaron Sidford (Math. Program., 2020)

First-Order Methods for Nonconvex Quadratic Minimization. - Yair Carmon; John C. Duchi (SIAM Rev., 2020)

Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss. - Yair Carmon; Arun Jambulapati; Yujia Jin; Aaron Sidford (COLT, 2021)

Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. - John Miller 001; Rohan Taori; Aditi Raghunathan; Shiori Sagawa; Pang Wei Koh; Vaishaal Shankar; Percy Liang; Yair Carmon; Ludwig Schmidt (ICML, 2021)

Never Go Full Batch (in Stochastic Convex Optimization). - Idan Amir; Yair Carmon; Tomer Koren; Roi Livni (NeurIPS, 2021)

Stochastic Bias-Reduced Gradient Methods. - Hilal Asi; Yair Carmon; Arun Jambulapati; Yujia Jin; Aaron Sidford (NeurIPS, 2021)

Lower bounds for finding stationary points II: first-order methods. - Yair Carmon; John C. Duchi; Oliver Hinder; Aaron Sidford (Math. Program., 2021)

Making SGD Parameter-Free. - Yair Carmon; Oliver Hinder (COLT, 2022)

Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments. - Maor Ivgi; Yair Carmon; Jonathan Berant (EMNLP (Findings), 2022)

RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. - Yair Carmon; Arun Jambulapati; Yujia Jin; Aaron Sidford (ICML, 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 (ICML, 2022)

Distributionally Robust Optimization via Ball Oracle Acceleration. - Yair Carmon; Danielle Hausler (NeurIPS, 2022)

Optimal and Adaptive Monteiro-Svaiter Acceleration. - Yair Carmon; Danielle Hausler; Arun Jambulapati; Yujia Jin; Aaron Sidford (NeurIPS, 2022)

Malign Overfitting: Interpolation Can Provably Preclude Invariance. - Yoav Wald; Gal Yona; Uri Shalit; Yair Carmon (CoRR, 2022)

ReSQueing Parallel and Private Stochastic Convex Optimization. - Yair Carmon; Arun Jambulapati; Yujia Jin; Yin Tat Lee; Daogao Liu; Aaron Sidford; Kevin Tian (FOCS, 2023)

Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds. - Yoav Wald; Gal Yona; Uri Shalit; Yair Carmon (ICLR, 2023)

DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule. - Maor Ivgi; Oliver Hinder; Yair Carmon (ICML, 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 (ICML, 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 001; Eyal Orgad; Rahim Entezari; et al. (NeurIPS, 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., 2023)

The Price of Adaptivity in Stochastic Convex Optimization. - Yair Carmon; Oliver Hinder (COLT, 2024)

Accelerated Parameter-Free Stochastic Optimization. - Itai Kreisler; Maor Ivgi; Oliver Hinder; Yair Carmon (COLT, 2024)

DataComp-LM: In search of the next generation of training sets for language models. - Jeffrey Li; Alex Fang; Georgios Smyrnis; Maor Ivgi; Matt Jordan; Samir Yitzhak Gadre; Hritik Bansal; Etash Kumar Guha; Sedrick Scott Keh; Kushal Arora; Saurabh Garg; Rui Xin; et al. (NeurIPS, 2024)

Resolving Discrepancies in Compute-Optimal Scaling of Language Models. - Tomer Porian; Mitchell Wortsman; Jenia Jitsev; Ludwig Schmidt; Yair Carmon (NeurIPS, 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 (SODA, 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; et al. (CoRR, 2024)

Extracting Dual Solutions via Primal Optimizers. - Yair Carmon; Arun Jambulapati; Liam O'Carroll; Aaron Sidford (ITCS, 2025)

An Analytical Model for Overparameterized Learning Under Class Imbalance. - Eliav Mor; Yair Carmon (Trans. Mach. Learn. Res., 2025)

Filter Like You Test: Data-Driven Data Filtering for CLIP Pretraining. - Mikey Shechter; Yair Carmon (CoRR, 2025)

The Sample Complexity of Parameter-Free Stochastic Convex Optimization. - Jared Lawrence; Ari Kalinsky; Hannah Bradfield; Yair Carmon; Oliver Hinder (CoRR, 2025)