| Concept bottleneck models PW Koh, T Nguyen, YS Tang, S Mussmann, E Pierson, B Kim, P Liang International conference on machine learning, 5338-5348, 2020 | 885 | 2020 |
| Datacomp: In search of the next generation of multimodal datasets SY Gadre, G Ilharco, A Fang, J Hayase, G Smyrnis, T Nguyen, R Marten, ... Advances in Neural Information Processing Systems 36, 2024 | 353 | 2024 |
| Selection via proxy: Efficient data selection for deep learning C Coleman, C Yeh, S Mussmann, B Mirzasoleiman, P Bailis, P Liang, ... arXiv preprint arXiv:1906.11829, 2019 | 346 | 2019 |
| Learning and inference via maximum inner product search S Mussmann, S Ermon International Conference on Machine Learning, 2587-2596, 2016 | 69 | 2016 |
| On the relationship between data efficiency and error for uncertainty sampling S Mussmann, P Liang International Conference on Machine Learning, 3674-3682, 2018 | 49 | 2018 |
| Uncertainty sampling is preconditioned stochastic gradient descent on zero-one loss S Mussmann, PS Liang Advances in Neural Information Processing Systems 31, 2018 | 22 | 2018 |
| On the importance of adaptive data collection for extremely imbalanced pairwise tasks S Mussmann, R Jia, P Liang arXiv preprint arXiv:2010.05103, 2020 | 21 | 2020 |
| Fast amortized inference and learning in log-linear models with randomly perturbed nearest neighbor search S Mussmann, D Levy, S Ermon arXiv preprint arXiv:1707.03372, 2017 | 21 | 2017 |
| Incorporating assortativity and degree dependence into scalable network models S Mussmann, J Moore, J Pfeiffer, J Neville Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 18 | 2015 |
| Comparing the value of labeled and unlabeled data in method-of-moments latent variable estimation M Chen, B Cohen-Wang, S Mussmann, F Sala, C Ré International Conference on Artificial Intelligence and Statistics, 3286-3294, 2021 | 17 | 2021 |
| Interactive programmatic labeling for weak supervision B Cohen-Wang, S Mussmann, A Ratner, C Ré Proceedings of the KDD DCCL Workshop, Anchorage, AK, USA, 4-8, 2019 | 16 | 2019 |
| Active learning with expected error reduction S Mussmann, J Reisler, D Tsai, E Mousavi, S O'Brien, M Goldszmidt arXiv preprint arXiv:2211.09283, 2022 | 12 | 2022 |
| Understanding trajectory behavior: A motion pattern approach MM Kalayeh, S Mussmann, A Petrakova, NV Lobo, M Shah arXiv preprint arXiv:1501.00614, 2015 | 11 | 2015 |
| An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models G Bhatt, Y Chen, AM Das, J Zhang, ST Truong, S Mussmann, Y Zhu, ... arXiv preprint arXiv:2401.06692, 2024 | 9 | 2024 |
| Vocalexplore: Pay-as-you-go video data exploration and model building M Daum, E Zhang, D He, S Mussmann, B Haynes, R Krishna, ... Proceedings of the VLDB Endowment 16 (13), 4188-4201, 2023 | 8 | 2023 |
| Assortativity in chung lu random graph models S Mussmann, J Moore, JJ Pfeiffer, J Neville III Proceedings of the 8th Workshop on Social Network Mining and Analysis, 1-8, 2014 | 8 | 2014 |
| LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning J Zhang, Y Chen, G Canal, AM Das, G Bhatt, S Mussmann, Y Zhu, ... Journal of Data-centric Machine Learning Research, 2024 | 7 | 2024 |
| A tight analysis of greedy yields subexponential time approximation for uniform decision tree R Li, P Liang, S Mussmann Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020 | 7 | 2020 |
| Generalized binary search for split-neighborly problems S Mussmann, P Liang International Conference on Artificial Intelligence and Statistics, 1561-1569, 2018 | 6 | 2018 |
| The price of debiasing automatic metrics in natural language evaluation C Arun, M Stephen Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 6 | 2018 |
