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Unlabeled Data Improves Adversarial Robustness.

Y. Carmon, A. Raghunathan, L. Schmidt, J. Duchi, and P. Liang. NeurIPS, page 11190-11201. (2019)

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Yair Schiftan

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A Rank-1 Sketch for Matrix Multiplicative Weights.Y. Carmon, J. Duchi, A. Sidford, and K. Tian. COLT, volume 99 of Proceedings of Machine Learning Research, page 589-623. PMLR, (2019)Eventually-stationary policies for Markov decision models with non-constant discounting.Y. Carmon, and A. Shwartz. VALUETOOLS, page 63. ICST/ACM, (2008)Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss.Y. Carmon, A. Jambulapati, Y. Jin, and A. Sidford. COLT, volume 134 of Proceedings of Machine Learning Research, page 866-882. PMLR, (2021)Optimal and Adaptive Monteiro-Svaiter Acceleration.Y. Carmon, D. Hausler, A. Jambulapati, Y. Jin, and A. Sidford. NeurIPS, (2022)Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond.I. Kreisler, M. Nacson, D. Soudry, and Y. Carmon. ICML, volume 202 of Proceedings of Machine Learning Research, page 17684-17744. PMLR, (2023)First-Order Methods for Nonconvex Quadratic Minimization.Y. Carmon, and J. Duchi. SIAM Rev., 62 (2): 395-436 (2020)Comparison of the Achievable Rates in OFDM and Single Carrier Modulation with I.I.D. Inputs.Y. Carmon, S. Shamai, and T. Weissman. CoRR, (2013)A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions.Y. Carmon, A. Jambulapati, Y. Jin, and A. Sidford. CoRR, (2023)Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems.Y. Carmon, and J. Duchi. NeurIPS, page 10728-10738. (2018)Acceleration with a Ball Optimization Oracle.Y. Carmon, A. Jambulapati, Q. Jiang, Y. Jin, Y. Lee, A. Sidford, and K. Tian. NeurIPS, (2020)

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Unlabeled Data Improves Adversarial Robustness.

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Yair Schiftan

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Disambiguation of "Carmon, Yair"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Unlabeled Data Improves Adversarial Robustness.

Please choose a person to relate this publication to

Yair Schiftan

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Unlabeled Data Improves Adversarial Robustness.