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Ludwig Schmidt
Ludwig Schmidt
University of Washington and Allen Institute for AI
cs.washington.edu의 이메일 확인됨 - 홈페이지
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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
71322017
Do ImageNet Classifiers Generalize to ImageNet?
B Recht, R Roelofs, L Schmidt, V Shankar
arXiv preprint arXiv:1902.10811, 2019
941*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
612*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
6042018
Unlabeled data improves adversarial robustness
Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang
Advances in Neural Information Processing Systems, 11192-11203, 2019
4652019
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
4422015
Measuring robustness to natural distribution shifts in image classification
R Taori, A Dave, V Shankar, N Carlini, B Recht, L Schmidt
2312020
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
1762014
Approximation algorithms for model-based compressive sensing
C Hegde, P Indyk, L Schmidt
IEEE Transactions on Information Theory 61 (9), 5129-5147, 2015
106*2015
A nearly-linear time framework for graph-structured sparsity
C Hegde, P Indyk, L Schmidt
International Conference on Machine Learning, 928-937, 2015
1062015
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
1032019
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
1012019
On the limitations of first order approximation in GAN dynamics
J Li, A Madry, J Peebles, L Schmidt
91*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
842017
Retiring Adult: New Datasets for Fair Machine Learning
F Ding, M Hardt, J Miller, L Schmidt
Advances in Neural Information Processing Systems 34, 2021
832021
Trends in circumventing web-malware detection
M Rajab, L Ballard, N Jagpal, P Mavrommatis, D Nojiri, N Provos, ...
Google, Google Technical Report, 2011
812011
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
M Wortsman, G Ilharco, SY Gadre, R Roelofs, R Gontijo-Lopes, ...
International Conference on Machine Learning, 23965-23998, 2022
802022
Evaluating Machine Accuracy on ImageNet
V Shankar, R Roelofs, H Mania, A Fang, B Recht, L Schmidt
International Conference on Machine Learning (ICML), 2020
772020
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
JP Miller, R Taori, A Raghunathan, S Sagawa, PW Koh, V Shankar, ...
International Conference on Machine Learning, 7721-7735, 2021
742021
The effect of natural distribution shift on question answering models
J Miller, K Krauth, B Recht, L Schmidt
International Conference on Machine Learning, 6905-6916, 2020
722020
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