| Contrastive learning, multi-view redundancy, and linear models C Tosh, A Krishnamurthy, D Hsu Algorithmic Learning Theory, 1179-1206, 2021 | 150 | 2021 |
| Contrastive estimation reveals topic posterior information to linear models C Tosh, A Krishnamurthy, D Hsu Journal of Machine Learning Research 22 (281), 1-31, 2021 | 67 | 2021 |
| Bayesian decision-making under misspecified priors with applications to meta-learning M Simchowitz, C Tosh, A Krishnamurthy, DJ Hsu, T Lykouris, M Dudik, ... Advances in Neural Information Processing Systems 34, 26382-26394, 2021 | 52 | 2021 |
| Diameter-based active learning C Tosh, S Dasgupta International Conference on Machine Learning, 3444-3452, 2017 | 35 | 2017 |
| Simple and near-optimal algorithms for hidden stratification and multi-group learning CJ Tosh, D Hsu International Conference on Machine Learning, 21633-21657, 2022 | 19 | 2022 |
| The piranha problem: Large effects swimming in a small pond C Tosh, P Greengard, B Goodrich, A Gelman, A Vehtari, D Hsu arXiv preprint arXiv:2105.13445, 2021 | 15 | 2021 |
| Mixing rates for the alternating Gibbs sampler over restricted Boltzmann machines and friends C Tosh International Conference on Machine Learning, 840-849, 2016 | 15 | 2016 |
| The relative complexity of maximum likelihood estimation, map estimation, and sampling C Tosh, S Dasgupta Conference on Learning Theory, 2993-3035, 2019 | 13 | 2019 |
| Maximum likelihood estimation for mixtures of spherical Gaussians is NP-hard C Tosh, S Dasgupta Journal of Machine Learning Research 18 (175), 1-11, 2018 | 11 | 2018 |
| Expressivity of expand-and-sparsify representations S Dasgupta, C Tosh arXiv preprint arXiv:2006.03741, 2020 | 9 | 2020 |
| Lower bounds for the Gibbs sampler over mixtures of Gaussians C Tosh, S Dasgupta International Conference on Machine Learning, 1467-1475, 2014 | 9 | 2014 |
| Interactive structure learning with structural query-by-committee C Tosh, S Dasgupta Advances in Neural Information Processing Systems 31, 2018 | 8 | 2018 |
| Robustifying likelihoods by optimistically re-weighting data M Dewaskar, C Tosh, J Knoblauch, DB Dunson arXiv preprint arXiv:2303.10525, 2023 | 4 | 2023 |
| Diameter-based interactive structure discovery C Tosh, D Hsu International Conference on Artificial Intelligence and Statistics, 580-590, 2020 | 4 | 2020 |
| Interactive topic modeling with anchor words S Dasgupta, S Poulis, C Tosh arXiv preprint arXiv:1907.04919, 2019 | 4 | 2019 |
| A Bayesian model of dose-response for cancer drug studies W Tansey, C Tosh, DM Blei The Annals of Applied Statistics 16 (2), 680-705, 2022 | 3 | 2022 |
| A Bayesian active learning platform for scalable combination drug screens C Tosh, M Tec, J White, JF Quinn, G Ibanez Sanchez, P Calder, AL Kung, ... bioRxiv, 2023.12. 18.572245, 2023 | 1 | 2023 |
| Targeted active learning for probabilistic models C Tosh, M Tec, W Tansey arXiv preprint arXiv:2210.12122, 2022 | 1 | 2022 |
| A Bayesian model of dose-response for cancer drug studies W Tansey, C Tosh, DM Blei arXiv preprint arXiv:1906.04072, 2019 | 1 | 2019 |
| Structural query-by-committee C Tosh, S Dasgupta arXiv preprint arXiv:1803.06586, 2018 | 1 | 2018 |
