Stephen Mussmann

Machine Learning Researcher, Coactive AI

Assistant Professor (starting 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 for collection and to train performant systems.

Most of my current and previous work falls into one of two categories:


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

CV 

Google Scholar

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


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