Stephen Mussmann

Steve is in his sixth and final year as a PhD candidate at Stanford University in the Computer Science Department advised by Percy Liang. He is interested in machine learning research and focuses on adaptive data collection and active learning, ranging from theoretical questions about decision trees for entity identification to practical questions about common active learning algorithms.

Email: [my last name]


Google Scholar


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

Concept Bottleneck Models

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

Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search

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