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Turing-Universal Learners with Optimal Scaling Laws


Nov 09, 2021
Preetum Nakkiran


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Revisiting Model Stitching to Compare Neural Representations


Jun 14, 2021
Yamini Bansal, Preetum Nakkiran, Boaz Barak


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The Deep Bootstrap: Good Online Learners are Good Offline Generalizers


Oct 16, 2020
Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi


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Distributional Generalization: A New Kind of Generalization


Sep 17, 2020
Preetum Nakkiran, Yamini Bansal

* PN, YB co-first authors 

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Learning Rate Annealing Can Provably Help Generalization, Even for Convex Problems


May 15, 2020
Preetum Nakkiran

* 4 pages plus appendix 

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Optimal Regularization Can Mitigate Double Descent


Mar 04, 2020
Preetum Nakkiran, Prayaag Venkat, Sham Kakade, Tengyu Ma


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More Data Can Hurt for Linear Regression: Sample-wise Double Descent


Dec 16, 2019
Preetum Nakkiran


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Deep Double Descent: Where Bigger Models and More Data Hurt


Dec 04, 2019
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever

* G.K. and Y.B. contributed equally 

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SGD on Neural Networks Learns Functions of Increasing Complexity


May 28, 2019
Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L. Edelman, Fred Zhang, Boaz Barak

* Submitted to NeurIPS 2019 

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Adversarial Robustness May Be at Odds With Simplicity


Jan 02, 2019
Preetum Nakkiran

* welcome 

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The Generic Holdout: Preventing False-Discoveries in Adaptive Data Science


Sep 14, 2018
Preetum Nakkiran, Jarosław Błasiok


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Predicting Positive and Negative Links with Noisy Queries: Theory & Practice


Aug 07, 2018
Charalampos E. Tsourakakis, Michael Mitzenmacher, Kasper Green Larsen, Jarosław Błasiok, Ben Lawson, Preetum Nakkiran, Vasileios Nakos

* arXiv admin note: text overlap with arXiv:1609.00750 

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