Willie Neiswanger

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 80

Top Topics

Neural Architecture

Reinforcement Learning

Top Venues

Publications

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)
Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Siddartha Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum
LiveBench: A Challenging, Contamination-Free LLM Benchmark. CoRR (2024)
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) (2024)
Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. CoRR (2024)
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 (2024)
Ollie Liu, Deqing Fu, Dani Yogatama, Willie Neiswanger
DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models. CoRR (2024)
Deqing Fu, Ghazal Khalighinejad, Ollie Liu, Bhuwan Dhingra, Dani Yogatama, Robin Jia, Willie Neiswanger
IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations. CoRR (2024)
Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing
Betty: An Automatic Differentiation Library for Multilevel Optimization. ICLR (2023)
Ye Yuan, Can Chen, Zixuan Liu, Willie Neiswanger, Xue (Steve) Liu
Importance-aware Co-teaching for Offline Model-based Optimization. NeurIPS (2023)
Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger
Kernelized Offline Contextual Dueling Bandits. CoRR (2023)
Benedikt Boecking, Nicholas Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski
Generative Modeling Helps Weak Supervision (and Vice Versa). ICLR (2023)
Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric Xing
Making Scalable Meta Learning Practical. CoRR (2023)
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 (2023)
Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. CoRR (2023)
Ye Yuan, Can Chen, Zixuan Liu, Willie Neiswanger, Xue Liu
Importance-aware Co-teaching for Offline Model-based Optimization. CoRR (2023)
Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. AAAI (2023)
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 (2023)
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)
Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric Xing
Making Scalable Meta Learning Practical. NeurIPS (2023)
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 (2023)
Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon
Modular Conformal Calibration. ICML (2022)
Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
Generalizing Bayesian Optimization with Decision-theoretic Entropies. NeurIPS (2022)
Benedikt Boecking, Willie Neiswanger, Nicholas Carl Roberts, Stefano Ermon, Frederic Sala, Artur Dubrawski
Generative Modeling Helps Weak Supervision (and Vice Versa). CoRR (2022)
Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon
A General Recipe for Likelihood-free Bayesian Optimization. CoRR (2022)
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 (2022)
Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing
Betty: An Automatic Differentiation Library for Multilevel Optimization. CoRR (2022)
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 (2022)
Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon
A General Recipe for Likelihood-free Bayesian Optimization. ICML (2022)
Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger
An Experimental Design Perspective on Model-Based Reinforcement Learning. ICLR (2022)
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 (2022)
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)
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)
Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
Generalizing Bayesian Optimization with Decision-theoretic Entropies. CoRR (2022)
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)
Charles Marx, Shengjia Zhou, Willie Neiswanger, Stefano Ermon
Modular Conformal Calibration. CoRR (2022)
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 (2022)
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 (2022)
Yuxin Xiao, Eric P. Xing, Willie Neiswanger
Amortized Auto-Tuning: Cost-Efficient Transfer Optimization for Hyperparameter Recommendation. CoRR (2021)
Colin White, Willie Neiswanger, Yash Savani
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search. AAAI (2021)
Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. NeurIPS (2021)
Avanika Narayan, Piero Molino, Karan Goel, Willie Neiswanger, Christopher Ré
Personalized Benchmarking with the Ludwig Benchmarking Toolkit. CoRR (2021)
Willie Neiswanger, Aaditya Ramdas
Uncertainty quantification using martingales for misspecified Gaussian processes. ALT (2021)
Yang Liu, Sujay Khandagale, Colin White, Willie Neiswanger
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning. NeurIPS Datasets and Benchmarks (2021)
Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger
An Experimental Design Perspective on Model-Based Reinforcement Learning. CoRR (2021)
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 (2021)
Willie Neiswanger, Ke Alexander Wang, Stefano Ermon
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. ICML (2021)
Avanika Narayan, Piero Molino, Karan Goel, Willie Neiswanger, Christopher Ré
Personalized Benchmarking with the Ludwig Benchmarking Toolkit. NeurIPS Datasets and Benchmarks (2021)
Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification. CoRR (2021)
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)
Yang Liu, Sujay Khandagale, Colin White, Willie Neiswanger
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning. CoRR (2021)
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)
Willie Neiswanger, Ke Alexander Wang, Stefano Ermon
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. CoRR (2021)
Benedikt Boecking, Willie Neiswanger, Eric P. Xing, Artur Dubrawski
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. ICLR (2021)
Benedikt Boecking, Willie Neiswanger, Eric Po Xing, Artur Dubrawski
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. CoRR (2020)
Colin White, Willie Neiswanger, Sam Nolen, Yash Savani
A Study on Encodings for Neural Architecture Search. NeurIPS (2020)
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)
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 (2020)
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 (2020)
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 (2020)
Colin White, Willie Neiswanger, Sam Nolen, Yash Savani
A Study on Encodings for Neural Architecture Search. CoRR (2020)
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) (2020)
Willie Neiswanger, Aaditya Ramdas
Uncertainty quantification using martingales for misspecified Gaussian processes. CoRR (2020)
Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. CoRR (2020)
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 (2020)
Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen
Offline Contextual Bayesian Optimization. NeurIPS (2019)
Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing
ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. CoRR (2019)
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 (2019)
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 (2019)
Colin White, Willie Neiswanger, Yash Savani
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search. CoRR (2019)
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML (2019)
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS (2018)
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. CoRR (2018)
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 (2018)
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. CoRR (2018)
Rebecca C. Steorts, Matt Barnes, Willie Neiswanger
Performance Bounds for Graphical Record Linkage. CoRR (2017)
Willie Neiswanger, Eric P. Xing
Post-Inference Prior Swapping. ICML (2017)
Rebecca C. Steorts, Matt Barnes, Willie Neiswanger
Performance Bounds for Graphical Record Linkage. AISTATS (2017)
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 (2017)
Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. ICML (2016)
Willie Neiswanger, Eric P. Xing
Prior Swapping for Data-Independent Inference. CoRR (2016)
Willie Neiswanger, Chong Wang, Eric P. Xing
Embarrassingly Parallel Variational Inference in Nonconjugate Models. CoRR (2015)
Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider
Fast Function to Function Regression. AISTATS (2015)
Willie Neiswanger, Frank D. Wood, Eric P. Xing
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling. AISTATS (2014)
Willie Neiswanger, Chong Wang, Eric P. Xing
Asymptotically Exact, Embarrassingly Parallel MCMC. UAI (2014)
Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider
Fast Function to Function Regression. CoRR (2014)
Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing
Fast Distribution To Real Regression. AISTATS (2014)
Willie Neiswanger, Chong Wang, Qirong Ho, Eric P. Xing
Modeling Citation Networks Using Latent Random Offsets. UAI (2014)
Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing
Fast Distribution To Real Regression. CoRR (2013)
Willie Neiswanger, Chong Wang, Eric P. Xing
Asymptotically Exact, Embarrassingly Parallel MCMC. CoRR (2013)
Willie Neiswanger, Frank D. Wood
Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures CoRR (2012)