Assortativity in Chung Lu Random Graph Models. - Stephen Mussmann; John Moore; Joseph J. Pfeiffer III; Jennifer Neville (SNAKDD, 2014)

Incorporating Assortativity and Degree Dependence into Scalable Network Models. - Stephen Mussmann; John Moore; Joseph John Pfeiffer III; Jennifer Neville (AAAI, 2015)

Understanding Trajectory Behavior: A Motion Pattern Approach. - Mahdi M. Kalayeh; Stephen Mussmann; Alla Petrakova; Niels da Vitoria Lobo; Mubarak Shah (CoRR, 2015)

Learning and Inference via Maximum Inner Product Search. - Stephen Mussmann; Stefano Ermon (ICML, 2016)

Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. - Stephen Mussmann; Daniel Levy; Stefano Ermon (UAI, 2017)

The price of debiasing automatic metrics in natural language evalaution. - Arun Tejasvi Chaganty; Stephen Mussmann; Percy Liang (ACL (1), 2018)

Generalized Binary Search For Split-Neighborly Problems. - Stephen Mussmann; Percy Liang (AISTATS, 2018)

On the Relationship between Data Efficiency and Error for Uncertainty Sampling. - Stephen Mussmann; Percy Liang (ICML, 2018)

Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss. - Stephen Mussmann; Percy Liang (NeurIPS, 2018)

The price of debiasing automatic metrics in natural language evaluation. - Arun Tejasvi Chaganty; Stephen Mussmann; Percy Liang (CoRR, 2018)

On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks. - Stephen Mussmann; Robin Jia; Percy Liang (EMNLP (Findings), 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 (ICLR, 2020)

Concept Bottleneck Models. - Pang Wei Koh; Thao Nguyen; Yew Siang Tang; Stephen Mussmann; Emma Pierson; Been Kim; Percy Liang (ICML, 2020)

A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree. - Ray Li; Percy Liang; Stephen Mussmann (SODA, 2020)

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 (AISTATS, 2021)

Understanding and analyzing the effectiveness of uncertainty sampling. - Stephen Mussmann (2021)

Constants Matter: The Performance Gains of Active Learning. - Stephen O. Mussmann; Sanjoy Dasgupta (ICML, 2022)

Active Learning with Expected Error Reduction. - Stephen Mussmann; Julia Reisler; Daniel Tsai; Ehsan Mousavi; Shayne O'Brien; Moisés Goldszmidt (CoRR, 2022)

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 001; Eyal Orgad; Rahim Entezari; et al. (NeurIPS, 2023)

LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning. - Jifan Zhang; Yifang Chen 001; Gregory Canal; Stephen Mussmann; Yinglun Zhu; Simon Shaolei Du; Kevin G. Jamieson; Robert D. Nowak (CoRR, 2023)

VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building. - Maureen Daum; Enhao Zhang 001; Dong He 002; Stephen Mussmann; Brandon Haynes; Ranjay Krishna; Magdalena Balazinska (Proc. VLDB Endow., 2023)

An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. - Gantavya Bhatt; Yifang Chen 001; 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 (ACL (Findings), 2024)

Continuous nonlinear adaptive experimental design with gradient flow. - Ruhui Jin; Qin Li 007; Stephen O. Mussmann; Stephen J. Wright 001 (CoRR, 2024)