Search Results - Mussmann, Stephen

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  1. 1
    VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building

    VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building by Daum, Maureen, Zhang, Enhao, He, Dong, Mussmann, Stephen, Haynes, Brandon, Krishna, Ranjay, Balazinska, Magdalena

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  2. 2
    Continuous nonlinear adaptive experimental design with gradient flow

    Continuous nonlinear adaptive experimental design with gradient flow by Jin, Ruhui, Li, Qin, Mussmann, Stephen O, Wright, Stephen J

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  3. 3
    On the Relationship between Data Efficiency and Error for Uncertainty Sampling

    On the Relationship between Data Efficiency and Error for Uncertainty Sampling by Mussmann, Stephen, Liang, Percy

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  4. 4
    Generalized Binary Search For Split-Neighborly Problems

    Generalized Binary Search For Split-Neighborly Problems by Mussmann, Stephen, Liang, Percy

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  5. 5
    On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks

    On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks by Mussmann, Stephen, Jia, Robin, Liang, Percy

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  6. 6
    VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building [Technical Report]

    VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building [Technical Report] by Daum, Maureen, Zhang, Enhao, He, Dong, Mussmann, Stephen, Haynes, Brandon, Krishna, Ranjay, Balazinska, Magdalena

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  7. 7
    A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree

    A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree by Li, Ray, Liang, Percy, Mussmann, Stephen

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  8. 8
    Active Learning with Expected Error Reduction

    Active Learning with Expected Error Reduction by Mussmann, Stephen, Reisler, Julia, Tsai, Daniel, Mousavi, Ehsan, O'Brien, Shayne, Goldszmidt, Moises

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  9. 9
    LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning

    LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning by Zhang, Jifan, Chen, Yifang, Canal, Gregory, Mussmann, Stephen, Das, Arnav M, Bhatt, Gantavya, Zhu, Yinglun, Bilmes, Jeffrey, Du, Simon Shaolei, Jamieson, Kevin, Nowak, Robert D

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  10. 10
    An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models

    An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models by Bhatt, Gantavya, Chen, Yifang, Das, Arnav M, Zhang, Jifan, Truong, Sang T, Mussmann, Stephen, Zhu, Yinglun, Bilmes, Jeffrey, Du, Simon S, Jamieson, Kevin, Ash, Jordan T, Nowak, Robert D

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  11. 11
    Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search

    Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search by Mussmann, Stephen, Levy, Daniel, Ermon, Stefano

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  12. 12
    Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation

    Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation by Chen, Mayee F, Cohen-Wang, Benjamin, Mussmann, Stephen, Sala, Frederic, Ré, Christopher

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  13. 13
    Concept Bottleneck Models

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

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  14. 14
    Selection via Proxy: Efficient Data Selection for Deep Learning

    Selection via Proxy: Efficient Data Selection for Deep Learning by Coleman, Cody, Yeh, Christopher, Mussmann, Stephen, Mirzasoleiman, Baharan, Bailis, Peter, Liang, Percy, Leskovec, Jure, Zaharia, Matei

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  15. 15
    Incorporating Assortativity and Degree Dependence into Scalable Network Models

    Incorporating Assortativity and Degree Dependence into Scalable Network Models by Mussmann, Stephen, Moore, John, Pfeiffer, Joseph, Neville, Jennifer

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  16. 16
    DataComp: In search of the next generation of multimodal datasets

    DataComp: In search of the next generation of multimodal datasets by Gadre, Samir Yitzhak, Ilharco, Gabriel, Fang, Alex, Hayase, Jonathan, Smyrnis, Georgios, Nguyen, Thao, Ryan, Marten, Wortsman, Mitchell, Ghosh, Dhruba, Zhang, Jieyu, Orgad, Eyal, Entezari, Rahim, Daras, Giannis, Pratt, Sarah, Ramanujan, Vivek, Bitton, Yonatan, Marathe, Kalyani, Mussmann, Stephen, Vencu, Richard, Cherti, Mehdi, Krishna, Ranjay, Pang Wei Koh, Saukh, Olga, Ratner, Alexander, Song, Shuran, Hannaneh Hajishirzi, Farhadi, Ali, Beaumont, Romain, Oh, Sewoong, Dimakis, Alex, Jitsev, Jenia, Carmon, Yair, Shankar, Vaishaal, Schmidt, Ludwig

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  17. 17
    Understanding Trajectory Behavior: A Motion Pattern Approach

    Understanding Trajectory Behavior: A Motion Pattern Approach by Kalayeh, Mahdi M, Mussmann, Stephen, Petrakova, Alla, Niels da Vitoria Lobo, Shah, Mubarak

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