Neiswanger, Willie

45 publications

ICLRW 2025 Atomic Posterior Ensembles for Simulation-Based Inference Sam Griesemer, Willie Neiswanger, Yan Liu

ICLR 2025 DeLLMa: Decision Making Under Uncertainty with Large Language Models Ollie Liu, Deqing Fu, Dani Yogatama, Willie Neiswanger

CoRL 2025 Efficient Evaluation of Multi-Task Robot Policies with Active Experiment Selection Abrar Anwar, Rohan Gupta, Zain Merchant, Sayan Ghosh, Willie Neiswanger, Jesse Thomason

ICLRW 2025 Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions Jiarui Zhang, Ollie Liu, Tianyu Yu, Jinyi Hu, Willie Neiswanger

ICLR 2025 LiveBench: A Challenging, Contamination-Limited LLM Benchmark Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Sreemanti Dey, Shubh-Agrawal, Sandeep Singh Sandha, Siddartha Venkat Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum

ICLRW 2025 Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings Sang T. Truong, Duc Quang Nguyen, Willie Neiswanger, Ryan-Rhys Griffiths, Stefano Ermon, Nick Haber, Sanmi Koyejo

NeurIPS 2025 What Is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff Schneider, Eduard Hovy, Roger Baker Grosse, Eric P. Xing

NeurIPSW 2024 A Foundation Model for Metagenomic Sequences Ollie Liu, Sami Jaghouar, Johannes Hagemann, Jeff Kaufman, Willie Neiswanger

AAAI 2024 Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec

ICLR 2023 Betty: An Automatic Differentiation Library for Multilevel Optimization Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric Xing

NeurIPSW 2023 Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making Ian Char, Youngseog Chung, Rohan Shah, Willie Neiswanger, Jeff Schneider

ICLR 2023 Generative Modeling Helps Weak Supervision (and Vice Versa) Benedikt Boecking, Nicholas Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski

NeurIPS 2023 Importance-Aware Co-Teaching for Offline Model-Based Optimization Ye Yuan, Can Chen, Zixuan Liu, Willie Neiswanger, Xue Liu

ICMLW 2023 Kernelized Offline Contextual Dueling Bandits Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger

NeurIPS 2023 Making Scalable Meta Learning Practical Sang Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric P. Xing

ICLR 2023 Near-Optimal Policy Identification in Active Reinforcement Learning Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic

AAAI 2023 Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon

ICML 2022 A General Recipe for Likelihood-Free Bayesian Optimization Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon

ICLR 2022 An Experimental Design Perspective on Model-Based Reinforcement Learning Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger

NeurIPSW 2022 Betty: An Automatic Differentiation Library for Multilevel Optimization Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric Xing

NeurIPS 2022 Exploration via Planning for Information About the Optimal Trajectory Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark Boyer, Stefano Ermon, Jeff G. Schneider, Willie Neiswanger

NeurIPS 2022 Generalizing Bayesian Optimization with Decision-Theoretic Entropies Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon

AAAI 2022 IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David B. Lobell, Stefano Ermon

ICML 2022 Modular Conformal Calibration Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon

AAAI 2021 BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search Colin White, Willie Neiswanger, Yash Savani

ICML 2021 Bayesian Algorithm Execution: Estimating Computable Properties of Black-Box Functions Using Mutual Information Willie Neiswanger, Ke Alexander Wang, Stefano Ermon

NeurIPS 2021 Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification Youngseog Chung, Willie Neiswanger, Ian Char, Jeff G. Schneider

ICLR 2021 Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling Benedikt Boecking, Willie Neiswanger, Eric Xing, Artur Dubrawski

ALT 2021 Uncertainty Quantification Using Martingales for Misspecified Gaussian Processes Willie Neiswanger, Aaditya Ramdas

NeurIPS 2020 A Study on Encodings for Neural Architecture Search Colin White, Willie Neiswanger, Sam Nolen, Yash Savani

AISTATS 2020 ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing

ICLRW 2020 Neural Dynamical Systems Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider

JMLR 2020 Tuning Hyperparameters Without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing

ICML 2019 Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos

NeurIPS 2019 Offline Contextual Bayesian Optimization Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark Boyer, Egemen Kolemen, Jeff Schneider

NeurIPS 2018 Neural Architecture Search with Bayesian Optimisation and Optimal Transport Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric P Xing

JMLR 2017 Generalized P{\'o}lya Urn for Time-Varying Pitman-Yor Processes François Caron, Willie Neiswanger, Frank Wood, Arnaud Doucet, Manuel Davy

AISTATS 2017 Performance Bounds for Graphical Record Linkage Rebecca C. Steorts, Matt Barnes, Willie Neiswanger

ICML 2017 Post-Inference Prior Swapping Willie Neiswanger, Eric Xing

ICML 2016 Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric Xing

AISTATS 2015 Fast Function to Function Regression Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider

UAI 2014 Asymptotically Exact, Embarrassingly Parallel MCMC Willie Neiswanger, Chong Wang, Eric P. Xing

AISTATS 2014 Fast Distribution to Real Regression Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing

UAI 2014 Modeling Citation Networks Using Latent Random Offsets Willie Neiswanger, Chong Wang, Qirong Ho, Eric P. Xing

AISTATS 2014 The Dependent Dirichlet Process Mixture of Objects for Detection-Free Tracking and Object Modeling Willie Neiswanger, Frank D. Wood, Eric P. Xing