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ORCIDWillie Neiswanger
Person information
- affiliation: Stanford University, USA
- affiliation (PhD 2020): Carnegie Mellon University, Machine Learning Department
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Books and Theses
- 2020
[b1]Willie Neiswanger:
Post-inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making. Carnegie Mellon University, USA, 2020
Journal Articles
- 2024
- [j4]Sara A. Miskovich
, Willie Neiswanger, William Colocho, Claudio Emma, Jacqueline Garrahan, Timothy Maxwell, Christopher Mayes, Stefano Ermon, Auralee Edelen, Daniel Ratner:
Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives. Mach. Learn. Sci. Technol. 5(1): 15004 (2024) - 2020
- [j3]Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. J. Mach. Learn. Res. 21: 81:1-81:27 (2020) - [j2]Kevin Tran, Willie Neiswanger, Junwoong Yoon, Qingyang Zhang, Eric P. Xing, Zachary W. Ulissi:
Methods for comparing uncertainty quantifications for material property predictions. Mach. Learn. Sci. Technol. 1(2): 25006 (2020) - 2017
- [j1]François Caron, Willie Neiswanger, Frank D. Wood, Arnaud Doucet, Manuel Davy:
Generalized Pólya Urn for Time-Varying Pitman-Yor Processes. J. Mach. Learn. Res. 18: 27:1-27:32 (2017)
Conference and Workshop Papers
- 2024
- [c36]Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec:
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. AAAI 2024: 320-328 - 2023
- [c35]Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon:
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. AAAI 2023: 11016-11024 - [c34]Benedikt Boecking, Nicholas Carl Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski:
Generative Modeling Helps Weak Supervision (and Vice Versa). ICLR 2023 - [c33]Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing:
Betty: An Automatic Differentiation Library for Multilevel Optimization. ICLR 2023 - [c32]Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic:
Near-optimal Policy Identification in Active Reinforcement Learning. ICLR 2023 - [c31]Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric P. Xing:
Making Scalable Meta Learning Practical. NeurIPS 2023 - [c30]Ye Yuan, Can Chen, Zixuan Liu, Willie Neiswanger, Xue (Steve) Liu:
Importance-aware Co-teaching for Offline Model-based Optimization. NeurIPS 2023 - 2022
- [c29]Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David B. Lobell, Stefano Ermon:
IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling. AAAI 2022: 12034-12042 - [c28]Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency:
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis. EMNLP (Findings) 2022: 7273-7284 - [c27]Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger:
An Experimental Design Perspective on Model-Based Reinforcement Learning. ICLR 2022 - [c26]Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon:
Modular Conformal Calibration. ICML 2022: 15180-15195 - [c25]Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon:
A General Recipe for Likelihood-free Bayesian Optimization. ICML 2022: 20384-20404 - [c24]Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger:
Exploration via Planning for Information about the Optimal Trajectory. NeurIPS 2022 - [c23]Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon:
Generalizing Bayesian Optimization with Decision-theoretic Entropies. NeurIPS 2022 - 2021
- [c22]Colin White, Willie Neiswanger, Yash Savani:
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search. AAAI 2021: 10293-10301 - [c21]Willie Neiswanger, Aaditya Ramdas:
Uncertainty quantification using martingales for misspecified Gaussian processes. ALT 2021: 963-982 - [c20]Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff G. Schneider:
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. CDC 2021: 3735-3742 - [c19]Benedikt Boecking, Willie Neiswanger, Eric P. Xing, Artur Dubrawski:
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. ICLR 2021 - [c18]Willie Neiswanger, Ke Alexander Wang, Stefano Ermon:
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. ICML 2021: 8005-8015 - [c17]Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider:
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. NeurIPS 2021: 10971-10984 - [c16]Yang Liu, Sujay Khandagale, Colin White, Willie Neiswanger:
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning. NeurIPS Datasets and Benchmarks 2021 - [c15]Avanika Narayan, Piero Molino, Karan Goel, Willie Neiswanger, Christopher Ré:
Personalized Benchmarking with the Ludwig Benchmarking Toolkit. NeurIPS Datasets and Benchmarks 2021 - [c14]Aurick Qiao, Sang Keun Choe, Suhas Jayaram Subramanya, Willie Neiswanger, Qirong Ho, Hao Zhang, Gregory R. Ganger, Eric P. Xing:
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning. OSDI 2021 - 2020
- [c13]Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. AISTATS 2020: 3393-3403 - [c12]Colin White, Willie Neiswanger, Sam Nolen, Yash Savani:
A Study on Encodings for Neural Architecture Search. NeurIPS 2020 - 2019
- [c11]Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML 2019: 3222-3232 - [c10]Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen:
Offline Contextual Bayesian Optimization. NeurIPS 2019: 4629-4640 - 2018
- [c9]Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS 2018: 2020-2029 - 2017
- [c8]Rebecca C. Steorts, Matt Barnes, Willie Neiswanger:
Performance Bounds for Graphical Record Linkage. AISTATS 2017: 298-306 - [c7]Willie Neiswanger, Eric P. Xing:
Post-Inference Prior Swapping. ICML 2017: 2594-2602 - 2016
- [c6]Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing:
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. ICML 2016: 1548-1557 - 2015
- [c5]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider:
Fast Function to Function Regression. AISTATS 2015 - 2014
- [c4]Willie Neiswanger, Frank D. Wood, Eric P. Xing:
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling. AISTATS 2014: 660-668 - [c3]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Fast Distribution To Real Regression. AISTATS 2014: 706-714 - [c2]Willie Neiswanger, Chong Wang, Eric P. Xing:
Asymptotically Exact, Embarrassingly Parallel MCMC. UAI 2014: 623-632 - [c1]Willie Neiswanger, Chong Wang, Qirong Ho, Eric P. Xing:
Modeling Citation Networks Using Latent Random Offsets. UAI 2014: 633-642
Informal and Other Publications
- 2024
- [i49]Ollie Liu, Deqing Fu, Dani Yogatama, Willie Neiswanger:
DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models. CoRR abs/2402.02392 (2024) - [i48]Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec:
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. CoRR abs/2402.08383 (2024) - [i47]Deqing Fu, Ghazal Khalighinejad, Ollie Liu, Bhuwan Dhingra, Dani Yogatama, Robin Jia, Willie Neiswanger:
IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations. CoRR abs/2404.01266 (2024) - [i46]Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Minsoo Kang, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff G. Schneider, Eduard H. Hovy, Roger B. Grosse, Eric P. Xing:
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions. CoRR abs/2405.13954 (2024) - 2023
- [i45]Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon:
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. CoRR abs/2303.02569 (2023) - [i44]Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger:
Kernelized Offline Contextual Dueling Bandits. CoRR abs/2307.11288 (2023) - [i43]Zhiqiang Shen, Tianhua Tao, Liqun Ma, Willie Neiswanger, Zhengzhong Liu, Hongyi Wang, Bowen Tan, Joel Hestness, Natalia Vassilieva, Daria Soboleva, Eric P. Xing:
SlimPajama-DC: Understanding Data Combinations for LLM Training. CoRR abs/2309.10818 (2023) - [i42]Ye Yuan, Can Chen, Zixuan Liu, Willie Neiswanger, Xue Liu:
Importance-aware Co-teaching for Offline Model-based Optimization. CoRR abs/2309.11600 (2023) - [i41]Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric P. Xing:
Making Scalable Meta Learning Practical. CoRR abs/2310.05674 (2023) - [i40]Viraj Mehta, Vikramjeet Das, Ojash Neopane, Yijia Dai, Ilija Bogunovic, Jeff G. Schneider, Willie Neiswanger:
Sample Efficient Reinforcement Learning from Human Feedback via Active Exploration. CoRR abs/2312.00267 (2023) - [i39]Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Xuguang Ren, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Tim Baldwin, Eric P. Xing:
LLM360: Towards Fully Transparent Open-Source LLMs. CoRR abs/2312.06550 (2023) - 2022
- [i38]Benedikt Boecking, Willie Neiswanger, Nicholas Carl Roberts, Stefano Ermon, Frederic Sala, Artur Dubrawski:
Generative Modeling Helps Weak Supervision (and Vice Versa). CoRR abs/2203.12023 (2022) - [i37]Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon:
Modular Conformal Calibration. CoRR abs/2206.11468 (2022) - [i36]Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon:
A General Recipe for Likelihood-free Bayesian Optimization. CoRR abs/2206.13035 (2022) - [i35]Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing:
Betty: An Automatic Differentiation Library for Multilevel Optimization. CoRR abs/2207.02849 (2022) - [i34]Sara A. Miskovich, Willie Neiswanger, William Colocho, Claudio Emma, Jacqueline Garrahan, Timothy Maxwell, Christopher Mayes, Stefano Ermon, Auralee Edelen, Daniel Ratner:
Bayesian Algorithm Execution for Tuning Particle Accelerator Emittance with Partial Measurements. CoRR abs/2209.04587 (2022) - [i33]Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon:
Generalizing Bayesian Optimization with Decision-theoretic Entropies. CoRR abs/2210.01383 (2022) - [i32]Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White:
AutoML for Climate Change: A Call to Action. CoRR abs/2210.03324 (2022) - [i31]Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger:
Exploration via Planning for Information about the Optimal Trajectory. CoRR abs/2210.04642 (2022) - [i30]Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency:
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis. CoRR abs/2210.04714 (2022) - [i29]Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic:
Near-optimal Policy Identification in Active Reinforcement Learning. CoRR abs/2212.09510 (2022) - 2021
- [i28]Willie Neiswanger, Ke Alexander Wang, Stefano Ermon:
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. CoRR abs/2104.09460 (2021) - [i27]Yuxin Xiao, Eric P. Xing, Willie Neiswanger:
Amortized Auto-Tuning: Cost-Efficient Transfer Optimization for Hyperparameter Recommendation. CoRR abs/2106.09179 (2021) - [i26]Yang Liu, Sujay Khandagale, Colin White, Willie Neiswanger:
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning. CoRR abs/2106.12543 (2021) - [i25]Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger:
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification. CoRR abs/2109.10254 (2021) - [i24]Avanika Narayan, Piero Molino, Karan Goel, Willie Neiswanger, Christopher Ré:
Personalized Benchmarking with the Ludwig Benchmarking Toolkit. CoRR abs/2111.04260 (2021) - [i23]Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger:
An Experimental Design Perspective on Model-Based Reinforcement Learning. CoRR abs/2112.05244 (2021) - [i22]Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David B. Lobell, Stefano Ermon:
IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling. CoRR abs/2112.09126 (2021) - 2020
- [i21]Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider:
Offline Contextual Bayesian Optimization for Nuclear Fusion. CoRR abs/2001.01793 (2020) - [i20]Willie Neiswanger, Aaditya Ramdas:
Uncertainty quantification using martingales for misspecified Gaussian processes. CoRR abs/2006.07368 (2020) - [i19]Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider:
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. CoRR abs/2006.12682 (2020) - [i18]Colin White, Willie Neiswanger, Sam Nolen, Yash Savani:
A Study on Encodings for Neural Architecture Search. CoRR abs/2007.04965 (2020) - [i17]Aurick Qiao, Willie Neiswanger, Qirong Ho, Hao Zhang, Gregory R. Ganger, Eric P. Xing:
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning. CoRR abs/2008.12260 (2020) - [i16]Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider:
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. CoRR abs/2011.09588 (2020) - [i15]Benedikt Boecking, Willie Neiswanger, Eric Poe Xing, Artur Dubrawski:
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. CoRR abs/2012.06046 (2020) - 2019
- [i14]Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. CoRR abs/1901.11515 (2019) - [i13]Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. CoRR abs/1903.06694 (2019) - [i12]Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. CoRR abs/1908.01425 (2019) - [i11]Colin White, Willie Neiswanger, Yash Savani:
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search. CoRR abs/1910.11858 (2019) - 2018
- [i10]Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. CoRR abs/1802.07191 (2018) - [i9]Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. CoRR abs/1805.09964 (2018) - [i8]Rajesh Chidambaram, Michael Kampffmeyer, Willie Neiswanger, Xiaodan Liang, Thomas Lachmann, Eric P. Xing:
Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful. CoRR abs/1807.03711 (2018) - 2017
- [i7]Rebecca C. Steorts, Matt Barnes, Willie Neiswanger:
Performance Bounds for Graphical Record Linkage. CoRR abs/1703.02679 (2017) - 2016
- [i6]Willie Neiswanger, Eric P. Xing:
Prior Swapping for Data-Independent Inference. CoRR abs/1606.00787 (2016) - 2015
- [i5]Willie Neiswanger, Chong Wang, Eric P. Xing:
Embarrassingly Parallel Variational Inference in Nonconjugate Models. CoRR abs/1510.04163 (2015) - 2014
- [i4]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider:
Fast Function to Function Regression. CoRR abs/1410.7414 (2014) - 2013
- [i3]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Fast Distribution To Real Regression. CoRR abs/1311.2236 (2013) - [i2]Willie Neiswanger, Chong Wang, Eric P. Xing:
Asymptotically Exact, Embarrassingly Parallel MCMC. CoRR abs/1311.4780 (2013) - 2012
- [i1]Willie Neiswanger, Frank D. Wood:
Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures. CoRR abs/1210.3288 (2012)
Coauthor Index
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