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ORCIDYair Carmon
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2020 – today
- 2024
[c30]Yair Carmon, Oliver Hinder:
The Price of Adaptivity in Stochastic Convex Optimization. COLT 2024: 772-774- [c29]Itai Kreisler, Maor Ivgi, Oliver Hinder, Yair Carmon:
Accelerated Parameter-Free Stochastic Optimization. COLT 2024: 3257-3324 - [c28]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions. SODA 2024: 3685-3723 - [i35]Yair Carmon, Oliver Hinder:
The Price of Adaptivity in Stochastic Convex Optimization. CoRR abs/2402.10898 (2024) - [i34]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:
Language models scale reliably with over-training and on downstream tasks. CoRR abs/2403.08540 (2024) - [i33]Itai Kreisler, Maor Ivgi, Oliver Hinder, Yair Carmon:
Accelerated Parameter-Free Stochastic Optimization. CoRR abs/2404.00666 (2024) - [i32]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 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:
DataComp-LM: In search of the next generation of training sets for language models. CoRR abs/2406.11794 (2024) - [i31]Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, Yair Carmon:
Resolving Discrepancies in Compute-Optimal Scaling of Language Models. CoRR abs/2406.19146 (2024) - 2023
- [j11]Yossi Arjevani, Yair Carmon
, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth:
Lower bounds for non-convex stochastic optimization. Math. Program. 199(1): 165-214 (2023) - [c27]Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian:
ReSQueing Parallel and Private Stochastic Convex Optimization. FOCS 2023: 2031-2058 - [c26]Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon:
Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds. ICLR 2023 - [c25]Maor Ivgi, Oliver Hinder, Yair Carmon:
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule. ICML 2023: 14465-14499 - [c24]Itai Kreisler, Mor Shpigel Nacson, Daniel Soudry, Yair Carmon:
Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond. ICML 2023: 17684-17744 - [c23]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:
DataComp: In search of the next generation of multimodal datasets. NeurIPS 2023 - [i30]Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian:
ReSQueing Parallel and Private Stochastic Convex Optimization. CoRR abs/2301.00457 (2023) - [i29]Maor Ivgi, Oliver Hinder, Yair Carmon:
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule. CoRR abs/2302.12022 (2023) - [i28]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:
DataComp: In search of the next generation of multimodal datasets. CoRR abs/2304.14108 (2023) - [i27]Itai Kreisler, Mor Shpigel Nacson, Daniel Soudry, Yair Carmon:
Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond. CoRR abs/2305.13064 (2023) - [i26]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions. CoRR abs/2311.10886 (2023) - 2022
- [c22]Yair Carmon, Oliver Hinder:
Making SGD Parameter-Free. COLT 2022: 2360-2389 - [c21]Maor Ivgi, Yair Carmon, Jonathan Berant:
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments. EMNLP (Findings) 2022: 7354-7371 - [c20]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. ICML 2022: 2658-2685 - [c19]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:
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. ICML 2022: 23965-23998 - [c18]Yair Carmon, Danielle Hausler:
Distributionally Robust Optimization via Ball Oracle Acceleration. NeurIPS 2022 - [c17]Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Optimal and Adaptive Monteiro-Svaiter Acceleration. NeurIPS 2022 - [i25]Maor Ivgi, Yair Carmon, Jonathan Berant:
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments. CoRR abs/2202.06387 (2022) - [i24]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:
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. CoRR abs/2203.05482 (2022) - [i23]Yair Carmon, Danielle Hausler:
Distributionally Robust Optimization via Ball Oracle Acceleration. CoRR abs/2203.13225 (2022) - [i22]Yair Carmon, Oliver Hinder:
Making SGD Parameter-Free. CoRR abs/2205.02160 (2022) - [i21]Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Optimal and Adaptive Monteiro-Svaiter Acceleration. CoRR abs/2205.15371 (2022) - [i20]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. CoRR abs/2206.08627 (2022) - [i19]Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon:
Malign Overfitting: Interpolation Can Provably Preclude Invariance. CoRR abs/2211.15724 (2022) - 2021
- [j10]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
Lower bounds for finding stationary points II: first-order methods. Math. Program. 185(1-2): 315-355 (2021) - [c16]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss. COLT 2021: 866-882 - [c15]John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt:
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. ICML 2021: 7721-7735 - [c14]Hilal Asi, Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Stochastic Bias-Reduced Gradient Methods. NeurIPS 2021: 10810-10822 - [c13]Idan Amir, Yair Carmon, Tomer Koren, Roi Livni:
Never Go Full Batch (in Stochastic Convex Optimization). NeurIPS 2021: 25033-25043 - [i18]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss. CoRR abs/2105.01778 (2021) - [i17]Hilal Asi, Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Stochastic Bias-Reduced Gradient Methods. CoRR abs/2106.09481 (2021) - [i16]Idan Amir, Yair Carmon, Tomer Koren, Roi Livni:
Never Go Full Batch (in Stochastic Convex Optimization). CoRR abs/2107.00469 (2021) - [i15]John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt:
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. CoRR abs/2107.04649 (2021) - 2020
- [j9]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
Lower bounds for finding stationary points I. Math. Program. 184(1): 71-120 (2020) - [j8]Yair Carmon, John C. Duchi:
First-Order Methods for Nonconvex Quadratic Minimization. SIAM Rev. 62(2): 395-436 (2020) - [c12]Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan:
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. COLT 2020: 242-299 - [c11]Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian:
Coordinate Methods for Matrix Games. FOCS 2020: 283-293 - [c10]Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian:
Acceleration with a Ball Optimization Oracle. NeurIPS 2020 - [c9]Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford:
Large-Scale Methods for Distributionally Robust Optimization. NeurIPS 2020 - [i14]Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian:
Acceleration with a Ball Optimization Oracle. CoRR abs/2003.08078 (2020) - [i13]Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Ayush Sekhari, Karthik Sridharan:
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations. CoRR abs/2006.13476 (2020) - [i12]Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian:
Coordinate Methods for Matrix Games. CoRR abs/2009.08447 (2020) - [i11]Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford:
Large-Scale Methods for Distributionally Robust Optimization. CoRR abs/2010.05893 (2020)
2010 – 2019
- 2019
- [j7]Yair Carmon, John C. Duchi:
Gradient Descent Finds the Cubic-Regularized Nonconvex Newton Step. SIAM J. Optim. 29(3): 2146-2178 (2019) - [c8]Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian:
A Rank-1 Sketch for Matrix Multiplicative Weights. COLT 2019: 589-623 - [c7]Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang:
Unlabeled Data Improves Adversarial Robustness. NeurIPS 2019: 11190-11201 - [c6]Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian:
Variance Reduction for Matrix Games. NeurIPS 2019: 11377-11388 - [i10]Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian:
A Rank-1 Sketch for Matrix Multiplicative Weights. CoRR abs/1903.02675 (2019) - [i9]Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi:
Unlabeled Data Improves Adversarial Robustness. CoRR abs/1905.13736 (2019) - [i8]Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian:
Variance Reduction for Matrix Games. CoRR abs/1907.02056 (2019) - [i7]Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth:
Lower Bounds for Non-Convex Stochastic Optimization. CoRR abs/1912.02365 (2019) - 2018
- [j6]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
Accelerated Methods for NonConvex Optimization. SIAM J. Optim. 28(2): 1751-1772 (2018) - [c5]Yair Carmon, John C. Duchi:
Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems. NeurIPS 2018: 10728-10738 - 2017
- [c4]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. ICML 2017: 654-663 - 2016
- [j5]Kartik Venkat, Tsachy Weissman, Yair Carmon, Shlomo Shamai:
Information, Estimation, and Lookahead in the Gaussian Channel. IEEE Trans. Signal Process. 64(14): 3605-3618 (2016) - [i6]Daniel Soudry, Yair Carmon:
No bad local minima: Data independent training error guarantees for multilayer neural networks. CoRR abs/1605.08361 (2016) - [i5]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
Accelerated Methods for Non-Convex Optimization. CoRR abs/1611.00756 (2016) - [i4]Yair Carmon, John C. Duchi:
Gradient Descent Efficiently Finds the Cubic-Regularized Non-Convex Newton Step. CoRR abs/1612.00547 (2016) - 2015
- [j4]Yair Carmon, Shlomo Shamai, Tsachy Weissman:
Comparison of the Achievable Rates in OFDM and Single Carrier Modulation with I.I.D. Inputs. IEEE Trans. Inf. Theory 61(4): 1795-1818 (2015) - [j3]Yair Carmon, Shlomo Shamai:
Lower Bounds and Approximations for the Information Rate of the ISI Channel. IEEE Trans. Inf. Theory 61(10): 5417-5431 (2015) - 2014
- [i3]Yair Carmon, Shlomo Shamai:
Lower Bounds and Approximations for the Information Rate of the ISI Channel. CoRR abs/1401.1480 (2014) - 2013
- [c3]Kartik Venkat, Tsachy Weissman, Yair Carmon, Shlomo Shamai:
The role of lookahead in estimation under Gaussian noise. ISIT 2013: 2850-2854 - [i2]Kartik Venkat, Tsachy Weissman, Yair Carmon, Shlomo Shamai:
Information, Estimation, and Lookahead in the Gaussian channel. CoRR abs/1302.2167 (2013) - [i1]Yair Carmon, Shlomo Shamai, Tsachy Weissman:
Comparison of the Achievable Rates in OFDM and Single Carrier Modulation with I.I.D. Inputs. CoRR abs/1306.5781 (2013) - 2012
- [c2]Kartik Venkat, Tsachy Weissman, Yair Carmon, Shlomo Shamai:
On information, estimation and lookahead. Allerton Conference 2012: 1292-1299
2000 – 2009
- 2009
- [j2]Alexander M. Bronstein, Michael M. Bronstein, Yair Carmon, Ron Kimmel:
Partial Similarity of Shapes Using a Statistical Significance Measure. IPSJ Trans. Comput. Vis. Appl. 1: 105-114 (2009) - [j1]Yair Carmon, Adam Shwartz:
Markov decision processes with exponentially representable discounting. Oper. Res. Lett. 37(1): 51-55 (2009) - 2008
- [c1]Yair Carmon, Adam Shwartz:
Eventually-stationary policies for Markov decision models with non-constant discounting. VALUETOOLS 2008: 63
Coauthor Index
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