Carmon, Yair

31 publications

TMLR 2025 An Analytical Model for Overparameterized Learning Under Class Imbalance Eliav Mor, Yair Carmon
NeurIPS 2025 Convergence of Clipped SGD on Convex $(L_0,L_1)$-Smooth Functions Ofir Gaash, Kfir Yehuda Levy, Yair Carmon
NeurIPS 2025 Filter like You Test: Data-Driven Data Filtering for CLIP Pretraining Mikey Shechter, Yair Carmon
ICLR 2025 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, Luca Soldaini, Jenia Jitsev, Alex Dimakis, Gabriel Ilharco, Pang Wei Koh, Shuran Song, Thomas Kollar, Yair Carmon, Achal Dave, Reinhard Heckel, Niklas Muennighoff, Ludwig Schmidt
COLT 2024 Accelerated Parameter-Free Stochastic Optimization Itai Kreisler, Maor Ivgi, Oliver Hinder, Yair Carmon
NeurIPS 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 Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Chandu, Thao Nguyen, Igor Vasiljevic, Sham Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar
NeurIPS 2024 Resolving Discrepancies in Compute-Optimal Scaling of Language Models Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, Yair Carmon
ICMLW 2024 Resolving Discrepancies in Compute-Optimal Scaling of Language Models Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, Yair Carmon
COLT 2024 The Price of Adaptivity in Stochastic Convex Optimization Yair Carmon, Oliver Hinder
NeurIPS 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 Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei W 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
ICML 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
ICLR 2023 Malign Overfitting: Interpolation and Invariance Are Fundamentally at Odds Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon
NeurIPS 2022 Distributionally Robust Optimization via Ball Oracle Acceleration Yair Carmon, Danielle Hausler
COLT 2022 Making SGD Parameter-Free Yair Carmon, Oliver Hinder
NeurIPSW 2022 Malign Overfitting: Interpolation and Invariance Are Fundamentally at Odds Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon
ICML 2022 Model Soups: Averaging Weights of Multiple Fine-Tuned Models Improves Accuracy Without Increasing Inference Time Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt
NeurIPS 2022 Optimal and Adaptive Monteiro-Svaiter Acceleration Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford
ICML 2022 RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
ICML 2021 Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization John P Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt
NeurIPS 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
COLT 2021 Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
NeurIPS 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
COLT 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
COLT 2019 A Rank-1 Sketch for Matrix Multiplicative Weights Yair Carmon, John C Duchi, Sidford Aaron, Tian Kevin
NeurIPS 2019 Unlabeled Data Improves Adversarial Robustness Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang
NeurIPS 2019 Variance Reduction for Matrix Games Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian
NeurIPS 2018 Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems Yair Carmon, John C. Duchi
ICML 2017 “Convex Until Proven Guilty”: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford