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Contrasting Contrastive Self-Supervised Representation Learning Models


Mar 25, 2021
Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, Roozbeh Mottaghi


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Measuring Robustness to Natural Distribution Shifts in Image Classification


Jul 01, 2020
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt


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The Effect of Natural Distribution Shift on Question Answering Models


Apr 29, 2020
John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt


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Neural Kernels Without Tangents


Mar 05, 2020
Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Ludwig Schmidt, Jonathan Ragan-Kelley, Benjamin Recht

* code used to produce our results can be found at: https://github.com/modestyachts/neural_kernels_code 

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Unlabeled Data Improves Adversarial Robustness


Jun 10, 2019
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi


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A systematic framework for natural perturbations from videos


Jun 05, 2019
Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

* 16 pages, 5 tables, 6 figures. Paper Website: https://modestyachts.github.io/natural-perturbations-website/ 

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Model Similarity Mitigates Test Set Overuse


May 29, 2019
Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht

* 18 pages, 7 figures 

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Do ImageNet Classifiers Generalize to ImageNet?


Feb 13, 2019
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar


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Model Reconstruction from Model Explanations


Jul 13, 2018
Smitha Milli, Ludwig Schmidt, Anca D. Dragan, Moritz Hardt


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A Classification-Based Study of Covariate Shift in GAN Distributions


Jun 06, 2018
Shibani Santurkar, Ludwig Schmidt, Aleksander Mądry


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On the Limitations of First-Order Approximation in GAN Dynamics


Jun 03, 2018
Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt

* 18 pages, 4 figures, accepted to ICML 2018 

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Do CIFAR-10 Classifiers Generalize to CIFAR-10?


Jun 01, 2018
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar


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Adversarially Robust Generalization Requires More Data


May 02, 2018
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry

* Small changes for biblatex compatibility 

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Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms


Feb 23, 2018
Ilias Diakonikolas, Jerry Li, Ludwig Schmidt


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A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations


Feb 13, 2018
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry

* Preliminary version appeared in the NIPS 2017 Workshop on Machine Learning and Computer Security 

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Graph-Sparse Logistic Regression


Dec 15, 2017
Alexander LeNail, Ludwig Schmidt, Johnathan Li, Tobias Ehrenberger, Karen Sachs, Stefanie Jegelka, Ernest Fraenkel

* 7 pages, 2 figures, NIPS DISCML workshop paper 

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Towards Deep Learning Models Resistant to Adversarial Attacks


Nov 09, 2017
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu


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On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks


Apr 10, 2017
Arturs Backurs, Piotr Indyk, Ludwig Schmidt


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Fast Algorithms for Segmented Regression


Jul 14, 2016
Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

* 27 pages, appeared in ICML 2016 

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A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k


Jun 03, 2015
Jerry Li, Ludwig Schmidt


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Sample-Optimal Density Estimation in Nearly-Linear Time


Jun 01, 2015
Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt


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