Yair Carmon - RankMe

84 papers A* 28A 1B 1Journal 51Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	An Analytical Model for Overparameterized Learning Under Class Imbalance. CoRR Eliav Mor, Yair Carmon
2025	J	`jnl`	An Analytical Model for Overparameterized Learning Under Class Imbalance. Trans. Mach. Learn. Res. Eliav Mor, Yair Carmon
2025	A	`conf`	Extracting Dual Solutions via Primal Optimizers. ITCS Yair Carmon, Arun Jambulapati, Liam O'Carroll, Aaron Sidford
2025	J	`jnl`	Filter Like You Test: Data-Driven Data Filtering for CLIP Pretraining. CoRR Mikey Shechter, Yair Carmon
2025	J	`jnl`	The Sample Complexity of Parameter-Free Stochastic Convex Optimization. CoRR Jared Lawrence, Ari Kalinsky, Hannah Bradfield, Yair Carmon, Oliver Hinder
2024	A*	`conf`	A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions. SODA Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
2024	A*	`conf`	Accelerated Parameter-Free Stochastic Optimization. COLT Itai Kreisler, Maor Ivgi, Oliver Hinder, Yair Carmon
2024	J	`jnl`	Accelerated Parameter-Free Stochastic Optimization. CoRR Itai Kreisler, Maor Ivgi, Oliver Hinder, Yair Carmon
2024	A*	`conf`	DataComp-LM: In search of the next generation of training sets for language models. NeurIPS 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, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee F. 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 M. Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Raghavi Chandu, Thao Nguyen, Igor Vasiljevic, Sham M. 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, Alex Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar
2024	J	`jnl`	DataComp-LM: In search of the next generation of training sets for language models. CoRR Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Kumar Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee F. 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 M. Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Raghavi Chandu, Thao Nguyen, Igor Vasiljevic, Sham M. 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
2024	J	`jnl`	Extracting Dual Solutions via Primal Optimizers. CoRR Yair Carmon, Arun Jambulapati, Liam O'Carroll, Aaron Sidford
2024	J	`jnl`	Language models scale reliably with over-training and on downstream tasks. CoRR 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
2024	A*	`conf`	Resolving Discrepancies in Compute-Optimal Scaling of Language Models. NeurIPS Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, Yair Carmon
2024	J	`jnl`	Resolving Discrepancies in Compute-Optimal Scaling of Language Models. CoRR Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, Yair Carmon
2024	A*	`conf`	The Price of Adaptivity in Stochastic Convex Optimization. COLT Yair Carmon, Oliver Hinder
2024	J	`jnl`	The Price of Adaptivity in Stochastic Convex Optimization. CoRR Yair Carmon, Oliver Hinder
2023	J	`jnl`	A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions. CoRR Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
2023	A*	`conf`	DataComp: In search of the next generation of multimodal datasets. NeurIPS 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
2023	J	`jnl`	DataComp: In search of the next generation of multimodal datasets. CoRR 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 Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt
2023	A*	`conf`	DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule. ICML Maor Ivgi, Oliver Hinder, Yair Carmon
2023	J	`jnl`	DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule. CoRR Maor Ivgi, Oliver Hinder, Yair Carmon
2023	A*	`conf`	Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond. ICML Itai Kreisler, Mor Shpigel Nacson, Daniel Soudry, Yair Carmon
2023	J	`jnl`	Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond. CoRR Itai Kreisler, Mor Shpigel Nacson, Daniel Soudry, Yair Carmon
2023	J	`jnl`	Lower bounds for non-convex stochastic optimization. Math. Program. Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
2023	A*	`conf`	Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds. ICLR Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon
2023	A*	`conf`	ReSQueing Parallel and Private Stochastic Convex Optimization. FOCS Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian
2023	J	`jnl`	ReSQueing Parallel and Private Stochastic Convex Optimization. CoRR Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian
2022	A*	`conf`	Distributionally Robust Optimization via Ball Oracle Acceleration. NeurIPS Yair Carmon, Danielle Hausler
2022	J	`jnl`	Distributionally Robust Optimization via Ball Oracle Acceleration. CoRR Yair Carmon, Danielle Hausler
2022	A*	`conf`	Making SGD Parameter-Free. COLT Yair Carmon, Oliver Hinder
2022	J	`jnl`	Making SGD Parameter-Free. CoRR Yair Carmon, Oliver Hinder
2022	J	`jnl`	Malign Overfitting: Interpolation Can Provably Preclude Invariance. CoRR Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon
2022	A*	`conf`	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. ICML 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
2022	J	`jnl`	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. CoRR 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
2022	A*	`conf`	Optimal and Adaptive Monteiro-Svaiter Acceleration. NeurIPS Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford
2022	J	`jnl`	Optimal and Adaptive Monteiro-Svaiter Acceleration. CoRR Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford
2022	A*	`conf`	RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. ICML Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
2022	J	`jnl`	RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. CoRR Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
2022	—	`conf`	Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments. EMNLP (Findings) Maor Ivgi, Yair Carmon, Jonathan Berant
2022	J	`jnl`	Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments. CoRR Maor Ivgi, Yair Carmon, Jonathan Berant
2021	J	`jnl`	Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. CoRR John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt
2021	A*	`conf`	Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. ICML John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt
2021	J	`jnl`	Lower bounds for finding stationary points II: first-order methods. Math. Program. Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
2021	A*	`conf`	Never Go Full Batch (in Stochastic Convex Optimization). NeurIPS Idan Amir, Yair Carmon, Tomer Koren, Roi Livni
2021	J	`jnl`	Never Go Full Batch (in Stochastic Convex Optimization). CoRR Idan Amir, Yair Carmon, Tomer Koren, Roi Livni
2021	A*	`conf`	Stochastic Bias-Reduced Gradient Methods. NeurIPS Hilal Asi, Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
2021	J	`jnl`	Stochastic Bias-Reduced Gradient Methods. CoRR Hilal Asi, Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
2021	A*	`conf`	Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss. COLT Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
2021	J	`jnl`	Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss. CoRR Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford
2020	A*	`conf`	Acceleration with a Ball Optimization Oracle. NeurIPS Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian
2020	J	`jnl`	Acceleration with a Ball Optimization Oracle. CoRR Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian
2020	A*	`conf`	Coordinate Methods for Matrix Games. FOCS Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian
2020	J	`jnl`	Coordinate Methods for Matrix Games. CoRR Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian
2020	J	`jnl`	First-Order Methods for Nonconvex Quadratic Minimization. SIAM Rev. Yair Carmon, John C. Duchi
2020	A*	`conf`	Large-Scale Methods for Distributionally Robust Optimization. NeurIPS Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford
2020	J	`jnl`	Large-Scale Methods for Distributionally Robust Optimization. CoRR Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford
2020	J	`jnl`	Lower bounds for finding stationary points I. Math. Program. Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
2020	A*	`conf`	Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. COLT Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
2020	J	`jnl`	Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. CoRR Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
2019	A*	`conf`	A Rank-1 Sketch for Matrix Multiplicative Weights. COLT Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian
2019	J	`jnl`	A Rank-1 Sketch for Matrix Multiplicative Weights. CoRR Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian
2019	J	`jnl`	Gradient Descent Finds the Cubic-Regularized Nonconvex Newton Step. SIAM J. Optim. Yair Carmon, John C. Duchi
2019	J	`jnl`	Lower Bounds for Non-Convex Stochastic Optimization. CoRR Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
2019	A*	`conf`	Unlabeled Data Improves Adversarial Robustness. NeurIPS Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang
2019	J	`jnl`	Unlabeled Data Improves Adversarial Robustness. CoRR Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi
2019	A*	`conf`	Variance Reduction for Matrix Games. NeurIPS Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian
2019	J	`jnl`	Variance Reduction for Matrix Games. CoRR Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian
2018	J	`jnl`	Accelerated Methods for NonConvex Optimization. SIAM J. Optim. Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
2018	A*	`conf`	Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems. NeurIPS Yair Carmon, John C. Duchi
2017	A*	`conf`	"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. ICML Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
2016	J	`jnl`	Accelerated Methods for Non-Convex Optimization. CoRR Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford
2016	J	`jnl`	Gradient Descent Efficiently Finds the Cubic-Regularized Non-Convex Newton Step. CoRR Yair Carmon, John C. Duchi
2016	J	`jnl`	Information, Estimation, and Lookahead in the Gaussian Channel. IEEE Trans. Signal Process. Kartik Venkat, Tsachy Weissman, Yair Carmon, Shlomo Shamai
2016	J	`jnl`	No bad local minima: Data independent training error guarantees for multilayer neural networks. CoRR Daniel Soudry, Yair Carmon
2015	J	`jnl`	Comparison of the Achievable Rates in OFDM and Single Carrier Modulation with I.I.D. Inputs. IEEE Trans. Inf. Theory Yair Carmon, Shlomo Shamai, Tsachy Weissman
2015	J	`jnl`	Lower Bounds and Approximations for the Information Rate of the ISI Channel. IEEE Trans. Inf. Theory Yair Carmon, Shlomo Shamai
2014	J	`jnl`	Lower Bounds and Approximations for the Information Rate of the ISI Channel. CoRR Yair Carmon, Shlomo Shamai
2013	J	`jnl`	Comparison of the Achievable Rates in OFDM and Single Carrier Modulation with I.I.D. Inputs. CoRR Yair Carmon, Shlomo Shamai, Tsachy Weissman
2013	J	`jnl`	Information, Estimation, and Lookahead in the Gaussian channel CoRR Kartik Venkat, Tsachy Weissman, Yair Carmon, Shlomo Shamai
2013	B	`conf`	The role of lookahead in estimation under Gaussian noise. ISIT Kartik Venkat, Tsachy Weissman, Yair Carmon, Shlomo Shamai
2012	—	`conf`	On information, estimation and lookahead. Allerton Conference Kartik Venkat, Tsachy Weissman, Yair Carmon, Shlomo Shamai
2009	J	`jnl`	Markov decision processes with exponentially representable discounting. Oper. Res. Lett. Yair Carmon, Adam Shwartz
2009	J	`jnl`	Partial Similarity of Shapes Using a Statistical Significance Measure. IPSJ Trans. Comput. Vis. Appl. Alexander M. Bronstein, Michael M. Bronstein, Yair Carmon, Ron Kimmel
2008	—	`conf`	Eventually-stationary policies for Markov decision models with non-constant discounting. VALUETOOLS Yair Carmon, Adam Shwartz