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Willie Neiswanger

Department of Computer Science, Stanford University

Modular Conformal Calibration


Jul 05, 2022
Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon


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Betty: An Automatic Differentiation Library for Multilevel Optimization


Jul 05, 2022
Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric Xing


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A General Recipe for Likelihood-free Bayesian Optimization


Jun 27, 2022
Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon

* ICML 2022 

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Generative Modeling Helps Weak Supervision (and Vice Versa)


Mar 22, 2022
Benedikt Boecking, Willie Neiswanger, Nicholas Roberts, Stefano Ermon, Frederic Sala, Artur Dubrawski


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IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling


Dec 16, 2021
Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David Lobell, Stefano Ermon

* AAAI 2022 

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An Experimental Design Perspective on Model-Based Reinforcement Learning


Dec 09, 2021
Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger


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Personalized Benchmarking with the Ludwig Benchmarking Toolkit


Nov 08, 2021
Avanika Narayan, Piero Molino, Karan Goel, Willie Neiswanger, Christopher Ré

* 14 pages, 14 figures, 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks 

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Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification


Sep 21, 2021
Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger


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Synthetic Benchmarks for Scientific Research in Explainable Machine Learning


Jun 23, 2021
Yang Liu, Sujay Khandagale, Colin White, Willie Neiswanger


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