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Ludwig Schmidt
Ludwig Schmidt
University of Washington and Allen Institute for AI
Bestätigte E-Mail-Adresse bei cs.washington.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Towards deep learning models resistant to adversarial attacks
A Madry, A Makelov, L Schmidt, D Tsipras, A Vladu
arXiv preprint arXiv:1706.06083, 2017
101882017
Do ImageNet Classifiers Generalize to ImageNet?
B Recht, R Roelofs, L Schmidt, V Shankar
arXiv preprint arXiv:1902.10811, 2019
1513*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
777*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
7542018
Laion-5b: An open large-scale dataset for training next generation image-text models
C Schuhmann, R Beaumont, R Vencu, C Gordon, R Wightman, M Cherti, ...
Advances in Neural Information Processing Systems 35, 25278-25294, 2022
7262022
Unlabeled data improves adversarial robustness
Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang
Advances in Neural Information Processing Systems, 11192-11203, 2019
6462019
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
4972015
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
4392022
Measuring robustness to natural distribution shifts in image classification
R Taori, A Dave, V Shankar, N Carlini, B Recht, L Schmidt
4142020
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
3542022
Robust fine-tuning of zero-shot models
M Wortsman, G Ilharco, JW Kim, M Li, S Kornblith, R Roelofs, RG Lopes, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
2852022
Retiring Adult: New Datasets for Fair Machine Learning
F Ding, M Hardt, J Miller, L Schmidt
Advances in Neural Information Processing Systems 34, 2021
2412021
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
1922014
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
1762021
Openclip (2021)
G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ...
DOI: https://doi. org/10.5281/zenodo 5143773, 0
173*
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
1542019
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
1462019
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
1292020
Evaluating Machine Accuracy on ImageNet
V Shankar, R Roelofs, H Mania, A Fang, B Recht, L Schmidt
International Conference on Machine Learning (ICML), 2020
1282020
Reproducible scaling laws for contrastive language-image learning
M Cherti, R Beaumont, R Wightman, M Wortsman, G Ilharco, C Gordon, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
1132023
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