Ludwig Schmidt

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Citations	5780	5614
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i10-index	36	35

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Aleksander MądryMITVerified email at mit.edu
Piotr IndykProfessor of Electrical Engineering and Computer Science, MITVerified email at mit.edu
Chinmay HegdeAssistant Professor, New York UniversityVerified email at nyu.edu
Benjamin RechtAssociate Professor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Dimitris TsiprasMassachusetts Institute of TechnologyVerified email at mit.edu
Vaishaal ShankarAmazonVerified email at amazon.com
Jerry LiMicrosoft ResearchVerified email at mit.edu
Rebecca RoelofsUC BerkeleyVerified email at berkeley.edu
Adrian VladuIRIF, Université de ParisVerified email at mit.edu
Aleksandar MakelovGraduate student, MITVerified email at mit.edu
Ilias DiakonikolasUniversity of Wisconsin-MadisonVerified email at wisc.edu
Shibani SanturkarPhD Candidate, Massachusetts Institute of TechnologyVerified email at mit.edu
Logan EngstromMITVerified email at mit.edu
Brandon TranPh.D candidate, MITVerified email at mit.edu
Moritz HardtUniversity of California, BerkeleyVerified email at berkeley.edu
Kunal TalwarAppleVerified email at kunaltalwar.org
Jayadev AcharyaAssistant Professor, Cornell UniversityVerified email at cornell.edu
Alexandr AndoniAssociate Professor, Columbia UniversityVerified email at mit.edu
Ilya RazenshteynMicrosoft ResearchVerified email at mit.edu
Thijs LaarhovenEindhoven University of TechnologyVerified email at berkeley.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	3103	2017
Do ImageNet Classifiers Generalize to ImageNet? B Recht, R Roelofs, L Schmidt, V Shankar arXiv preprint arXiv:1902.10811, 2019	338*	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	332	2018
Exploring the Landscape of Spatial Robustness L Engstrom, B Tran, D Tsipras, L Schmidt, A Madry International Conference on Machine Learning, 1802-1811, 2019	326*	2019
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	323	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	151	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	147	2014
Approximation algorithms for model-based compressive sensing C Hegde, P Indyk, L Schmidt IEEE Transactions on Information Theory 61 (9), 5129-5147, 2015	102*	2015
A nearly-linear time framework for graph-structured sparsity C Hegde, P Indyk, L Schmidt International Conference on Machine Learning, 928-937, 2015	91	2015
Trends in circumventing web-malware detection M Rajab, L Ballard, N Jagpal, P Mavrommatis, D Nojiri, N Provos, ... Google, Google Technical Report, 2011	73	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	65	2017
On the limitations of first order approximation in GAN dynamics J Li, A Madry, J Peebles, L Schmidt	63*	2018
Large-scale speaker identification L Schmidt, M Sharifi, IL Moreno 2014 IEEE International conference on acoustics, speech and signal …, 2014	55	2014
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	46	2019
A classification-based perspective on gan distributions S Santurkar, L Schmidt, A Madry	40*	2018
Differentially private learning of structured discrete distributions I Diakonikolas, M Hardt, L Schmidt Advances in Neural Information Processing Systems 28, 2566-2574, 2015	40	2015
Fast and near-optimal algorithms for approximating distributions by histograms J Acharya, I Diakonikolas, C Hegde, JZ Li, L Schmidt Proceedings of the 34th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2015	36	2015
A fast approximation algorithm for tree-sparse recovery C Hegde, P Indyk, L Schmidt 2014 IEEE International Symposium on Information Theory, 1842-1846, 2014	36	2014
Robust and proper learning for mixtures of gaussians via systems of polynomial inequalities J Li, L Schmidt Conference on Learning Theory, 1302-1382, 2017	35*	2017
Communication-efficient distributed learning of discrete distributions I Diakonikolas, E Grigorescu, J Li, A Natarajan, K Onak, L Schmidt Advances in Neural Information Processing Systems 30, 6391-6401, 2017	31	2017

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