Stephen Mussmann
Machine Learning Researcher, Coactive AI
Incoming Assistant Professor (Fall '24), Georgia Tech SCS
My research aims to make machine learning a more practical tool for humans to analyze information and to build into intelligent systems. I use a mixture of experimental and theoretical techniques to bring clarity to data aspects of machine learning: identifying what data is best suited to train performant systems.
Most of my current and previous work falls into one of two categories:
Active Labeling/Learning: human supervision and interaction with nature (experiments) can be expensive. Can we design efficient algorithms to iteratively choose data to label for use cases where collecting labels is expensive so that we can significantly decrease the cost and effort of labeling?
Data Selection: given increasingly large and noisy data sets, training on all available data can be expensive and can yield sub-optimal performance for specific tasks. Can we efficiently select training data that yield more accurate predictors?
I am looking to work with students at Georgia Tech starting August 2024: please feel free to reach out if you are interested in working with me!
Previously, I was an IFDS postdoc at the University of Washington Computer Science working with Kevin Jamieson and Ludwig Schmidt. I received my PhD in 2021 from Stanford University Computer Science advised by Percy Liang.
Personal Email: somussmann@gmail.com
Georgia Tech Email: mussmann@gatech.edu
Preprints
LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning
Jifan Zhang, Yifang Chen, Gregory Canal, Stephen Mussmann, Yinglun Zhu, Simon Shaolei Du, Kevin Jamieson, Robert D Nowak
June, 2023
Data Subset Selection via Machine Teaching
Stephen Mussmann, Alex Fang, Ludwig Schmidt, Kevin Jamieson
October, 2022
Publications
VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building
Maureen Daum, Enhao Zhang, Dong He, Stephen Mussmann, Brandon Haynes, Ranjay Krishna, Magdalena Balazinska
Conference on Very Large Data Bases (VLDB), 2024 (to appear)
DataComp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre*, Gabriel Ilharco*, Alex Fang*, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, and Ludwig Schmidt
Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 2023
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