Ludwig Schmidt
Ludwig Schmidt
Toyota Research and University of Washington
Email verificata su cs.washington.edu - Home page
Titolo
Citata da
Citata da
Anno
Towards deep learning models resistant to adversarial attacks
A Madry, A Makelov, L Schmidt, D Tsipras, A Vladu
arXiv preprint arXiv:1706.06083, 2017
31032017
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
3322018
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
3232015
Unlabeled data improves adversarial robustness
Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang
Advances in Neural Information Processing Systems, 11192-11203, 2019
1512019
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
1472014
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
912015
Trends in circumventing web-malware detection
M Rajab, L Ballard, N Jagpal, P Mavrommatis, D Nojiri, N Provos, ...
Google, Google Technical Report, 2011
732011
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
652017
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
552014
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
462019
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
402015
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
362015
A fast approximation algorithm for tree-sparse recovery
C Hegde, P Indyk, L Schmidt
2014 IEEE International Symposium on Information Theory, 1842-1846, 2014
362014
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
312017
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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