| 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 | 1412 | 2022 |
| Test-time training with self-supervision for generalization under distribution shifts Y Sun, X Wang, Z Liu, J Miller, A Efros, M Hardt International conference on machine learning, 9229-9248, 2020 | 939 | 2020 |
| Deep Voice: Real-time neural text-to-speech SO Arik, M Chrzanowski, A Coates, G Diamos, A Gibiansky, Y Kang, X Li, ... Internation Conference on Machine Learning, 2017 | 868 | 2017 |
| Deep Voice 2: Multi-Speaker Neural Text-to-Speech S Arik, G Diamos, A Gibiansky, J Miller, K Peng, W Ping, J Raiman, ... Advances in Neural Information Processing, 2017 | 659* | 2017 |
| Deep voice 3: Scaling text-to-speech with convolutional sequence learning W Ping, K Peng, A Gibiansky, SO Arik, A Kannan, S Narang, J Raiman, ... arXiv preprint arXiv:1710.07654, 2017 | 572 | 2017 |
| Retiring adult: New datasets for fair machine learning F Ding, M Hardt, J Miller, L Schmidt Advances in neural information processing systems 34, 6478-6490, 2021 | 517 | 2021 |
| Openclip G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ... If you use this software, please cite it as below 7, 2021 | 454 | 2021 |
| Traversing Knowledge Graphs in Vector Space K Guu, J Miller, P Liang Empirical Methods in Natural Language Processing, 2015 | 424 | 2015 |
| Deep voice 3: 2000-speaker neural text-to-speech W Ping, K Peng, A Gibiansky, SO Arik, A Kannan, S Narang, J Raiman, ... proc. ICLR 79, 1094-1099, 2018 | 369 | 2018 |
| 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 | 322 | 2021 |
| Openclip, july 2021 G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ... URL https://doi. org/10.5281/zenodo 5143773 (2), 29, 2021 | 295 | 2021 |
| A meta-analysis of overfitting in machine learning R Roelofs, V Shankar, B Recht, S Fridovich-Keil, M Hardt, J Miller, ... Advances in neural information processing systems 32, 2019 | 248 | 2019 |
| The social cost of strategic classification S Milli, J Miller, AD Dragan, M Hardt Proceedings of the conference on fairness, accountability, and transparency …, 2019 | 218 | 2019 |
| Stable recurrent models J Miller, M Hardt arXiv preprint arXiv:1805.10369, 2018 | 206* | 2018 |
| 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 | 180 | 2020 |
| Strategic Classification is Causal Modeling in Disguise J Miller, S Milli, M Hardt arXiv preprint arXiv:1910.10362, 2019 | 140 | 2019 |
| Outside the echo chamber: Optimizing the performative risk JP Miller, JC Perdomo, T Zrnic International Conference on Machine Learning, 7710-7720, 2021 | 119 | 2021 |
| Model similarity mitigates test set overuse H Mania, J Miller, L Schmidt, M Hardt, B Recht Advances in Neural Information Processing Systems 32, 2019 | 60 | 2019 |
| Globally Normalized Reader J Raiman, J Miller Empirical Methods in Natural Language Processing, 2017 | 40 | 2017 |
| Adversarial scrutiny of evidentiary statistical software R Abebe, M Hardt, A Jin, J Miller, L Schmidt, R Wexler Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 13 | 2022 |
