Ludwig Schmidt

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	All	Since 2017
Citations	9856	9566
h-index	28	27
i10-index	43	42

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Aleksander MądryMITVerified email at mit.edu
Piotr IndykProfessor of Electrical Engineering and Computer Science, MITVerified email at mit.edu
Benjamin RechtAssociate Professor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Vaishaal ShankarAmazonVerified email at amazon.com
Dimitris TsiprasStanford UniversityVerified email at stanford.edu
Chinmay HegdeAssistant Professor, New York UniversityVerified email at nyu.edu
Rebecca RoelofsUC BerkeleyVerified email at berkeley.edu
Jerry LiMicrosoft ResearchVerified email at mit.edu
Adrian VladuCNRS, IRIF, Université Paris CitéVerified email at mit.edu
Aleksandar MakelovGraduate student, MITVerified email at mit.edu
Shibani SanturkarPostdoc, Stanford UniversityVerified email at stanford.edu
Ilias DiakonikolasUniversity of Wisconsin-MadisonVerified email at wisc.edu
Yair CarmonTel Aviv UniversityVerified email at tauex.tau.ac.il
Moritz HardtDirector, Max Planck Institute for Intelligent Systems; Associate Professor, EECS, UC BerkeleyVerified email at is.mpg.de
Achal DaveCarnegie Mellon UniversityVerified email at cs.cmu.edu
Logan EngstromMITVerified email at mit.edu
Brandon TranPh.D candidate, MITVerified email at mit.edu
Rohan TaoriCS PhD @ StanfordVerified email at stanford.edu
Kunal TalwarApple IncVerified email at apple.com
Aditi RaghunathanPhD Student, Stanford UniversityVerified email at stanford.edu

Ludwig Schmidt

Toyota Research and University of Washington

Verified email at cs.washington.edu - Homepage

Machine Learning Optimization Algorithms Statistics


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Towards deep learning models resistant to adversarial attacks A Madry, A Makelov, L Schmidt, D Tsipras, A Vladu arXiv preprint arXiv:1706.06083, 2017	5600	2017
Do ImageNet Classifiers Generalize to ImageNet? B Recht, R Roelofs, L Schmidt, V Shankar arXiv preprint arXiv:1902.10811, 2019	680*	2019
Exploring the Landscape of Spatial Robustness L Engstrom, B Tran, D Tsipras, L Schmidt, A Madry International Conference on Machine Learning, 1802-1811, 2019	523*	2019
Adversarially robust generalization requires more data L Schmidt, S Santurkar, D Tsipras, K Talwar, A Madry Advances in Neural Information Processing Systems 31, 5014-5026, 2018	500	2018
Practical and optimal LSH for angular distance A Andoni, P Indyk, T Laarhoven, I Razenshteyn, L Schmidt Advances in Neural Information Processing Systems, 1225-1233, 2015	402	2015
Unlabeled data improves adversarial robustness Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang Advances in Neural Information Processing Systems, 11192-11203, 2019	346	2019
Recent developments in the sparse Fourier transform: A compressed Fourier transform for big data AC Gilbert, P Indyk, M Iwen, L Schmidt IEEE Signal Processing Magazine 31 (5), 91-100, 2014	166	2014
Measuring robustness to natural distribution shifts in image classification R Taori, A Dave, V Shankar, N Carlini, B Recht, L Schmidt	147	2020
Approximation algorithms for model-based compressive sensing C Hegde, P Indyk, L Schmidt IEEE Transactions on Information Theory 61 (9), 5129-5147, 2015	103*	2015
A nearly-linear time framework for graph-structured sparsity C Hegde, P Indyk, L Schmidt International Conference on Machine Learning, 928-937, 2015	99	2015
Model reconstruction from model explanations S Milli, L Schmidt, AD Dragan, M Hardt Proceedings of the Conference on Fairness, Accountability, and Transparency, 1-9, 2019	84	2019
On the limitations of first order approximation in GAN dynamics J Li, A Madry, J Peebles, L Schmidt	80*	2018
Trends in circumventing web-malware detection M Rajab, L Ballard, N Jagpal, P Mavrommatis, D Nojiri, N Provos, ... Google, Google Technical Report, 2011	77	2011
Sample-optimal density estimation in nearly-linear time J Acharya, I Diakonikolas, J Li, L Schmidt Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017	76	2017
A meta-analysis of overfitting in machine learning R Roelofs, S Fridovich-Keil, J Miller, V Shankar, M Hardt, B Recht, ... Proceedings of the 33rd International Conference on Neural Information …, 2019	71	2019
A classification-based perspective on gan distributions S Santurkar, L Schmidt, A Madry	56*	2018
Large-scale speaker identification L Schmidt, M Sharifi, IL Moreno 2014 IEEE International conference on acoustics, speech and signal …, 2014	56	2014
Neural kernels without tangents V Shankar, A Fang, W Guo, S Fridovich-Keil, J Ragan-Kelley, L Schmidt, ... International Conference on Machine Learning, 8614-8623, 2020	53	2020
Evaluating Machine Accuracy on ImageNet V Shankar, R Roelofs, H Mania, A Fang, B Recht, L Schmidt International Conference on Machine Learning (ICML), 2020	49	2020
Differentially private learning of structured discrete distributions I Diakonikolas, M Hardt, L Schmidt Advances in Neural Information Processing Systems 28, 2566-2574, 2015	45	2015

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