Stephen Mussmann

According to our database, Stephen Mussmann authored at least 23 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Continuous nonlinear adaptive experimental design with gradient flow.

Ruhui Jin, Qin Li, Stephen O. Mussmann, Stephen J. Wright

CoRR, 2024

An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models.

Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeff A. Bilmes, Simon S. Du, Kevin G. Jamieson, Jordan T. Ash, Robert D. Nowak

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

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

Proc. VLDB Endow., 2023

LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning.

Jifan Zhang, Yifang Chen, Gregory Canal, Stephen Mussmann, Yinglun Zhu, Simon Shaolei Du, Kevin G. Jamieson, Robert D. Nowak

CoRR, 2023

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 M. Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander J. Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Active Learning with Expected Error Reduction.

Stephen Mussmann, Julia Reisler, Daniel Tsai, Ehsan Mousavi, Shayne O'Brien, Moisés Goldszmidt

CoRR, 2022

Constants Matter: The Performance Gains of Active Learning.

Stephen O. Mussmann, Sanjoy Dasgupta

Proceedings of the International Conference on Machine Learning, 2022

2021

Understanding and analyzing the effectiveness of uncertainty sampling.

Stephen Mussmann

PhD thesis, 2021

Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation.

Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, Christopher Ré

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree.

Ray Li, Percy Liang, Stephen Mussmann

Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

Concept Bottleneck Models.

Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang

Proceedings of the 37th International Conference on Machine Learning, 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

Proceedings of the 8th International Conference on Learning Representations, 2020

On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks.

Stephen Mussmann, Robin Jia, Percy Liang

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

2018

The price of debiasing automatic metrics in natural language evaluation.

Arun Tejasvi Chaganty, Stephen Mussmann, Percy Liang

CoRR, 2018

Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss.

Stephen Mussmann, Percy Liang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On the Relationship between Data Efficiency and Error for Uncertainty Sampling.

Stephen Mussmann, Percy Liang

Proceedings of the 35th International Conference on Machine Learning, 2018

Generalized Binary Search For Split-Neighborly Problems.

Stephen Mussmann, Percy Liang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

The price of debiasing automatic metrics in natural language evalaution.

Arun Tejasvi Chaganty, Stephen Mussmann, Percy Liang

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018

2017

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

Stephen Mussmann, Daniel Levy, Stefano Ermon

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

2016

Learning and Inference via Maximum Inner Product Search.

Stephen Mussmann, Stefano Ermon

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Understanding Trajectory Behavior: A Motion Pattern Approach.

Mahdi M. Kalayeh, Stephen Mussmann, Alla Petrakova, Niels da Vitoria Lobo, Mubarak Shah

CoRR, 2015

Incorporating Assortativity and Degree Dependence into Scalable Network Models.

Stephen Mussmann, John Moore, Joseph John Pfeiffer III, Jennifer Neville

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Assortativity in Chung Lu Random Graph Models.

Stephen Mussmann, John Moore, Joseph J. Pfeiffer III, Jennifer Neville

Proceedings of the 8th Workshop on Social Network Mining and Analysis, 2014