Stephen Mussmann

36 papers A* 8A 3Journal 20Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Sum Estimation via Vector Similarity Search. CoRR Stephen Mussmann, Mehul Smriti Raje, Kavya Tumkur, Oumayma Messoussi, Cyprien Hachem, Seby Jacob
2025	J	`jnl`	Myopic Bayesian Decision Theory for Batch Active Learning with Partial Batch Label Sampling. CoRR Kangping Hu, Stephen Mussmann
2024	—	`conf`	An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. ACL (Findings) Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeff A. Bilmes, Simon S. Du, Kevin Jamieson, Jordan T. Ash, Robert D. Nowak
2024	J	`jnl`	An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. CoRR Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeffrey A. Bilmes, Simon S. Du, Kevin Jamieson, Jordan T. Ash, Robert D. Nowak
2024	J	`jnl`	LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning. J. Data-centric Mach. Learn. Res. Jifan Zhang, Yifang Chen, Gregory Canal, Arnav Mohanty Das, Gantavya Bhatt, Stephen Mussmann, Yinglun Zhu, Jeff A. Bilmes, Simon Shaolei Du, Kevin Jamieson, Robert D. Nowak
2023	A*	`conf`	DataComp: In search of the next generation of multimodal datasets. NeurIPS 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
2023	J	`jnl`	DataComp: In search of the next generation of multimodal datasets. CoRR 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 Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt
2023	J	`jnl`	LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning. CoRR Jifan Zhang, Yifang Chen, Gregory Canal, Stephen Mussmann, Yinglun Zhu, Simon Shaolei Du, Kevin Jamieson, Robert D. Nowak
2023	J	`jnl`	VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building. CoRR Maureen Daum, Enhao Zhang, Dong He, Stephen Mussmann, Brandon Haynes, Ranjay Krishna, Magdalena Balazinska
2023	J	`jnl`	VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building. Proc. VLDB Endow. Maureen Daum, Enhao Zhang, Dong He, Stephen Mussmann, Brandon Haynes, Ranjay Krishna, Magdalena Balazinska
2022	J	`jnl`	Active Learning with Expected Error Reduction. CoRR Stephen Mussmann, Julia Reisler, Daniel Tsai, Ehsan Mousavi, Shayne O'Brien, Moisés Goldszmidt
2021	A	`conf`	Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation. AISTATS Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, Christopher Ré
2021	J	`jnl`	Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation. CoRR Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, Christopher Ré
2021	—	`—`	Understanding and analyzing the effectiveness of uncertainty sampling. Stephen Mussmann
2020	A*	`conf`	A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree. SODA Ray Li, Percy Liang, Stephen Mussmann
2020	A*	`conf`	Concept Bottleneck Models. ICML Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang
2020	J	`jnl`	Concept Bottleneck Models. CoRR Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang
2020	—	`conf`	On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks. EMNLP (Findings) Stephen Mussmann, Robin Jia, Percy Liang
2020	J	`jnl`	On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks. CoRR Stephen Mussmann, Robin Jia, Percy Liang
2020	A*	`conf`	Selection via Proxy: Efficient Data Selection for Deep Learning. ICLR Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia
2019	J	`jnl`	A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree. CoRR Ray Li, Percy Liang, Stephen Mussmann
2019	J	`jnl`	Selection Via Proxy: Efficient Data Selection For Deep Learning. CoRR Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, Matei Zaharia
2018	A	`conf`	Generalized Binary Search For Split-Neighborly Problems. AISTATS Stephen Mussmann, Percy Liang
2018	J	`jnl`	Generalized Binary Search For Split-Neighborly Problems. CoRR Stephen Mussmann, Percy Liang
2018	A*	`conf`	On the Relationship between Data Efficiency and Error for Uncertainty Sampling. ICML Stephen Mussmann, Percy Liang
2018	J	`jnl`	On the Relationship between Data Efficiency and Error for Uncertainty Sampling. CoRR Stephen Mussmann, Percy Liang
2018	—	`conf`	The price of debiasing automatic metrics in natural language evalaution. ACL (1) Arun Tejasvi Chaganty, Stephen Mussmann, Percy Liang
2018	J	`jnl`	The price of debiasing automatic metrics in natural language evaluation. CoRR Arun Tejasvi Chaganty, Stephen Mussmann, Percy Liang
2018	A*	`conf`	Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss. NeurIPS Stephen Mussmann, Percy Liang
2018	J	`jnl`	Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss. CoRR Stephen Mussmann, Percy Liang
2017	A	`conf`	Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. UAI Stephen Mussmann, Daniel Levy, Stefano Ermon
2017	J	`jnl`	Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search. CoRR Stephen Mussmann, Daniel Levy, Stefano Ermon
2016	A*	`conf`	Learning and Inference via Maximum Inner Product Search. ICML Stephen Mussmann, Stefano Ermon
2015	A*	`conf`	Incorporating Assortativity and Degree Dependence into Scalable Network Models. AAAI Stephen Mussmann, John Moore, Joseph John Pfeiffer III, Jennifer Neville
2015	J	`jnl`	Understanding Trajectory Behavior: A Motion Pattern Approach. CoRR Mahdi M. Kalayeh, Stephen Mussmann, Alla Petrakova, Niels da Vitoria Lobo, Mubarak Shah
2014	—	`conf`	Assortativity in Chung Lu Random Graph Models. SNAKDD Stephen Mussmann, John Moore, Joseph J. Pfeiffer III, Jennifer Neville