Willie Neiswanger

According to our database, Willie Neiswanger authored at least 60 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives.

Sara A. Miskovich, Willie Neiswanger, William Colocho, Claudio Emma, Jacqueline Garrahan, Timothy Maxwell, Christopher Mayes, Stefano Ermon, Auralee Edelen, Daniel Ratner

Mach. Learn. Sci. Technol., March, 2024

DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models.

Ollie Liu, Deqing Fu, Dani Yogatama, Willie Neiswanger

CoRR, 2024

Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution.

Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

LLM360: Towards Fully Transparent Open-Source LLMs.

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

CoRR, 2023

Sample Efficient Reinforcement Learning from Human Feedback via Active Exploration.

Viraj Mehta, Vikramjeet Das, Ojash Neopane, Yijia Dai, Ilija Bogunovic, Jeff G. Schneider, Willie Neiswanger

CoRR, 2023

Importance-aware Co-teaching for Offline Model-based Optimization.

Ye Yuan, Can Chen, Zixuan Liu, Willie Neiswanger, Xue Liu

CoRR, 2023

SlimPajama-DC: Understanding Data Combinations for LLM Training.

Zhiqiang Shen, Tianhua Tao, Liqun Ma, Willie Neiswanger, Zhengzhong Liu, Hongyi Wang, Bowen Tan, Joel Hestness, Natalia Vassilieva, Daria Soboleva, Eric P. Xing

CoRR, 2023

Kernelized Offline Contextual Dueling Bandits.

Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger

CoRR, 2023

Importance-aware Co-teaching for Offline Model-based Optimization.

Ye Yuan, Can Chen, Zixuan Liu, Willie Neiswanger, Xue (Steve) Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Making Scalable Meta Learning Practical.

Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric Xing

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Betty: An Automatic Differentiation Library for Multilevel Optimization.

Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Generative Modeling Helps Weak Supervision (and Vice Versa).

Benedikt Boecking, Nicholas Carl Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching.

Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

AutoML for Climate Change: A Call to Action.

Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White

CoRR, 2022

Bayesian Algorithm Execution for Tuning Particle Accelerator Emittance with Partial Measurements.

Sara A. Miskovich, Willie Neiswanger, William Colocho, Claudio Emma, Jacqueline Garrahan, Timothy Maxwell, Christopher Mayes, Stefano Ermon, Auralee Edelen, Daniel Ratner

CoRR, 2022

Generalizing Bayesian Optimization with Decision-theoretic Entropies.

Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Exploration via Planning for Information about the Optimal Trajectory.

Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A General Recipe for Likelihood-free Bayesian Optimization.

Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon

Proceedings of the International Conference on Machine Learning, 2022

Modular Conformal Calibration.

Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon

Proceedings of the International Conference on Machine Learning, 2022

An Experimental Design Perspective on Model-Based Reinforcement Learning.

Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger

Proceedings of the Tenth International Conference on Learning Representations, 2022

Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis.

Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 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

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

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

Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger

CoRR, 2021

Amortized Auto-Tuning: Cost-Efficient Transfer Optimization for Hyperparameter Recommendation.

Yuxin Xiao, Eric P. Xing, Willie Neiswanger

CoRR, 2021

Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning.

Aurick Qiao, Sang Keun Choe, Suhas Jayaram Subramanya, Willie Neiswanger, Qirong Ho, Hao Zhang, Gregory R. Ganger, Eric P. Xing

Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation, 2021

Personalized Benchmarking with the Ludwig Benchmarking Toolkit.

Avanika Narayan, Piero Molino, Karan Goel, Willie Neiswanger, Christopher Ré

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Synthetic Benchmarks for Scientific Research in Explainable Machine Learning.

Yang Liu, Sujay Khandagale, Colin White, Willie Neiswanger

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification.

Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information.

Willie Neiswanger, Ke Alexander Wang, Stefano Ermon

Proceedings of the 38th International Conference on Machine Learning, 2021

Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling.

Benedikt Boecking, Willie Neiswanger, Eric P. Xing, Artur Dubrawski

Proceedings of the 9th International Conference on Learning Representations, 2021

Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction.

Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff G. Schneider

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Uncertainty quantification using martingales for misspecified Gaussian processes.

Willie Neiswanger, Aaditya Ramdas

Proceedings of the Algorithmic Learning Theory, 2021

BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search.

Colin White, Willie Neiswanger, Yash Savani

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Post-inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making.

Willie Neiswanger

PhD thesis, 2020

Methods for comparing uncertainty quantifications for material property predictions.

Kevin Tran, Willie Neiswanger, Junwoong Yoon, Qingyang Zhang, Eric P. Xing, Zachary W. Ulissi

Mach. Learn. Sci. Technol., 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, Barnabás Póczos, Eric P. Xing

J. Mach. Learn. Res., 2020

Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning.

Aurick Qiao, Willie Neiswanger, Qirong Ho, Hao Zhang, Gregory R. Ganger, Eric P. Xing

CoRR, 2020

Offline Contextual Bayesian Optimization for Nuclear Fusion.

Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider

CoRR, 2020

A Study on Encodings for Neural Architecture Search.

Colin White, Willie Neiswanger, Sam Nolen, Yash Savani

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations.

Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization.

Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing

CoRR, 2019

Offline Contextual Bayesian Optimization.

Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments.

Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful.

Rajesh Chidambaram, Michael Kampffmeyer, Willie Neiswanger, Xiaodan Liang, Thomas Lachmann, Eric P. Xing

CoRR, 2018

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming.

Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

CoRR, 2018

Neural Architecture Search with Bayesian Optimisation and Optimal Transport.

Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Generalized Pólya Urn for Time-Varying Pitman-Yor Processes.

François Caron, Willie Neiswanger, Frank D. Wood, Arnaud Doucet, Manuel Davy

J. Mach. Learn. Res., 2017

Post-Inference Prior Swapping.

Willie Neiswanger, Eric P. Xing

Proceedings of the 34th International Conference on Machine Learning, 2017

Performance Bounds for Graphical Record Linkage.

Rebecca C. Steorts, Matt Barnes, Willie Neiswanger

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Prior Swapping for Data-Independent Inference.

Willie Neiswanger, Eric P. Xing

CoRR, 2016

Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms.

Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Embarrassingly Parallel Variational Inference in Nonconjugate Models.

Willie Neiswanger, Chong Wang, Eric P. Xing

CoRR, 2015

Fast Function to Function Regression.

Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Fast Function to Function Regression.

Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider

CoRR, 2014

Asymptotically Exact, Embarrassingly Parallel MCMC.

Willie Neiswanger, Chong Wang, Eric P. Xing

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Modeling Citation Networks Using Latent Random Offsets.

Willie Neiswanger, Chong Wang, Qirong Ho, Eric P. Xing

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Fast Distribution To Real Regression.

Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling.

Willie Neiswanger, Frank D. Wood, Eric P. Xing

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2012

Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures

Willie Neiswanger, Frank D. Wood

CoRR, 2012