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Covariate Shift in High-Dimensional Random Feature Regression

Nov 16, 2021
Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington

* 107 pages, 10 figures 

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Underspecification Presents Challenges for Credibility in Modern Machine Learning

Nov 06, 2020
Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley

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Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition

Nov 04, 2020
Ben Adlam, Jeffrey Pennington

* Published as a conference paper in the Proceedings of the Thirty-fourth Conference on Neural Information Processing Systems; 54 pages; 5 figures. arXiv admin note: text overlap with arXiv:2008.06786 

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Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit

Oct 14, 2020
Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek

* 23 pages, 11 figures 

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Finite Versus Infinite Neural Networks: an Empirical Study

Sep 08, 2020
Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein

* 17+11 pages; v2 references added, minor improvements 

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The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization

Aug 15, 2020
Ben Adlam, Jeffrey Pennington

* Published as a conference paper in the Proceedings of the 37th International Conference on Machine Learning; 31 pages; 4 figures 

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Cold Posteriors and Aleatoric Uncertainty

Jul 31, 2020
Ben Adlam, Jasper Snoek, Samuel L. Smith

* ICML workshop on Uncertainty and Robustness in Deep Learning (2020) 
* 5 pages, 3 figures 

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The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks

Jun 25, 2020
Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington

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A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning

Dec 02, 2019
Ben Adlam, Jake Levinson, Jeffrey Pennington

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Learning GANs and Ensembles Using Discrepancy

Nov 06, 2019
Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang

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Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks

Oct 30, 2019
Ben Adlam, Charles Weill, Amol Kapoor

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AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

Apr 30, 2019
Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes

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