Stephen Mussmann
Stephen Mussmann
Incoming Assistant Professor, Georgia Tech
Verified email at - Homepage
Cited by
Cited by
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
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
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
The price of debiasing automatic metrics in natural language evalaution.
P Liang, AT Chaganty, S Mussmann
ACL (1), 643-653, 2018
Learning and inference via maximum inner product search
S Mussmann, S Ermon
International Conference on Machine Learning, 2587-2596, 2016
On the relationship between data efficiency and error for uncertainty sampling
S Mussmann, P Liang
International Conference on Machine Learning, 3674-3682, 2018
Uncertainty sampling is preconditioned stochastic gradient descent on zero-one loss
S Mussmann, PS Liang
Advances in Neural Information Processing Systems 31, 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
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
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
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
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
Understanding trajectory behavior: A motion pattern approach
MM Kalayeh, S Mussmann, A Petrakova, NV Lobo, M Shah
arXiv preprint arXiv:1501.00614, 2015
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
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
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
Generalized binary search for split-neighborly problems
S Mussmann, P Liang
International Conference on Artificial Intelligence and Statistics, 1561-1569, 2018
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
Select Via Proxy: Efficient Data Selection For Training Deep Networks
C Coleman, S Mussmann, B Mirzasoleiman, P Bailis, P Liang, J Leskovec, ...
Feb, 2019
LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning
J Zhang, Y Chen, G Canal, S Mussmann, Y Zhu, SS Du, K Jamieson, ...
arXiv preprint arXiv:2306.09910, 2023
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