Stephen Mussmann
Steve is an IFDS postdoc at the University of Washington Computer Science working with Kevin Jamieson and Ludwig Schmidt. He received his PhD in 2021 from Stanford University Computer Science advised by Percy Liang. He is interested in machine learning research and focuses on choosing informative data through the lenses of active learning and data pruning.
Steve is applying for academic jobs this year (2022-2023)!
Email: [my last name]@cs.washington.edu
Office: CSE2 232
Preprints
Data Subset Selection via Machine Teaching
Stephen Mussmann, Alex Fang, Ludwig Schmidt, Kevin Jamieson
October, 2022
Publications
Constants Matter: The Performance Gains of Active Learning
Stephen Mussmann, Sanjoy Dasgupta
International Conference on Machine Learning (ICML), 2022
Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
Mayee Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, Christopher Re
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks
Stephen Mussmann*, Robin Jia*, Percy Liang
Findings of Empirical Methods in Natural Language Processing (EMNLP), 2020
Pang Wei Koh*, Thao Nguyen*, Yew Siang Tang*, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang
International Conference on Machine Learning (ICML), 2020
Selection via Proxy: Efficient Data Selection for Deep Learning
Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia
International Conference on Learning Representations (ICLR), 2020
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree
Ray Li, Percy Liang, Stephen Mussmann
Symposium on Discrete Algorithms (SODA), 2020
Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss
Stephen Mussmann, Percy Liang
Neural Information Processing Systems (NeurIPS), 2018
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
Stephen Mussmann, Percy Liang
International Conference on Machine Learning (ICML), 2018
The Price of Debiasing Automatic Metrics in Natural Language Evaluation
Arun Chaganty*, Stephen Mussmann*, Percy Liang
Association for COmputational Linguistics (ACL), 2018
Generalized Binary Search For Split-Neighborly Problems
Stephen Mussmann, Percy Liang
International Conference on Artificial Intelligence and Statistics (AISTATS), 2018
Stephen Mussmann*, Daniel Levy*, Stefano Ermon
Uncertainty in Artificial Intelligence (UAI), 2017
Learning and Inference via Maximum Product Search
Stephen Mussmann, Stefano Ermon
International Conference on Machine Learning (ICML), 2016
Incorporating Assortativity and Degree Dependence into Scalable Network Models
Stephen Mussmann*, John Moore*, Joseph J. Pfeiffer III, Jennifer Neville
AAAI Conference on Artificial Intelligence (AAAI), 2015