| Towards deep learning models resistant to adversarial attacks A Madry, A Makelov, L Schmidt, D Tsipras, A Vladu arXiv preprint arXiv:1706.06083, 2017 | 2126 | 2017 |
| 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 | 266 | 2015 |
| Adversarially robust generalization requires more data L Schmidt, S Santurkar, D Tsipras, K Talwar, A Madry Advances in Neural Information Processing Systems, 5014-5026, 2018 | 251 | 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 | 235* | 2019 |
| Do ImageNet Classifiers Generalize to ImageNet? B Recht, R Roelofs, L Schmidt, V Shankar arXiv preprint arXiv:1902.10811, 2019 | 209* | 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 | 125 | 2014 |
| Approximation algorithms for model-based compressive sensing C Hegde, P Indyk, L Schmidt IEEE Transactions on Information Theory 61 (9), 5129-5147, 2015 | 95* | 2015 |
| A nearly-linear time framework for graph-structured sparsity C Hegde, P Indyk, L Schmidt International Conference on Machine Learning, 928-937, 2015 | 79 | 2015 |
| Unlabeled data improves adversarial robustness Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang Advances in Neural Information Processing Systems, 11190-11201, 2019 | 73 | 2019 |
| Trends in circumventing web-malware detection M Rajab, L Ballard, N Jagpal, P Mavrommatis, D Nojiri, N Provos, ... Google, Google Technical Report, 2011 | 68 | 2011 |
| On the limitations of first order approximation in GAN dynamics J Li, A Madry, J Peebles, L Schmidt | 54* | 2018 |
| 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 | 54 | 2017 |
| Large-scale speaker identification L Schmidt, M Sharifi, IL Moreno 2014 IEEE International conference on acoustics, speech and signal …, 2014 | 52 | 2014 |
| A fast approximation algorithm for tree-sparse recovery C Hegde, P Indyk, L Schmidt 2014 IEEE International Symposium on Information Theory, 1842-1846, 2014 | 35 | 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 | 34 | 2019 |
| A classification-based perspective on gan distributions S Santurkar, L Schmidt, A Madry | 34* | 2018 |
| 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 | 33 | 2015 |
| Differentially private learning of structured discrete distributions I Diakonikolas, M Hardt, L Schmidt Advances in Neural Information Processing Systems, 2566-2574, 2015 | 33 | 2015 |
| Robust and proper learning for mixtures of gaussians via systems of polynomial inequalities J Li, L Schmidt Conference on Learning Theory, 1302-1382, 2017 | 27* | 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, 6391-6401, 2017 | 26 | 2017 |
Aleksander MądryMITEmail verificata su mit.edu
