Willie Neiswanger

116 papers A* 26A 7B 1Journal 76Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings. CoRR Sang T. Truong, Duc Q. Nguyen, Willie Neiswanger, Ryan-Rhys Griffiths, Stefano Ermon, Nick Haber, Sanmi Koyejo
2026	J	`jnl`	Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Design. CoRR Samuel Klein, Willie Neiswanger, Daniel Ratner, Michael Kagan, Sean Gasiorowski
2025	J	`jnl`	AI-University: An LLM-based platform for instructional alignment to scientific classrooms. CoRR Mostafa Faghih Shojaei, Rahul Gulati, Benjamin A. Jasperson, Shangshang Wang, Simone Cimolato, Dangli Cao, Willie Neiswanger, Krishna C. Garikipati
2025	J	`jnl`	Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test. CoRR Xiaoyuan Zhu, Yaowen Ye, Tianyi Qiu, Hanlin Zhu, Sijun Tan, Ajraf Mannan, Jonathan Michala, Raluca Ada Popa, Willie Neiswanger
2025	A*	`conf`	DeLLMa: Decision Making Under Uncertainty with Large Language Models. ICLR Ollie Liu, Deqing Fu, Dani Yogatama, Willie Neiswanger
2025	J	`jnl`	Efficient Dynamic and Momentum Aperture Optimization for Lattice Design Using Multipoint Bayesian Algorithm Execution. CoRR Z. Zhang, I. Agapov, Sean Gasiorowski, Thorsten Hellert, Willie Neiswanger, X. Huang, Daniel Ratner
2025	J	`jnl`	Efficient Evaluation of Multi-Task Robot Policies With Active Experiment Selection. CoRR Abrar Anwar, Rohan Gupta, Zain Merchant, Sayan Ghosh, Willie Neiswanger, Jesse Thomason
2025	J	`jnl`	From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered. CoRR Siddartha Devic, Tejas Srinivasan, Jesse Thomason, Willie Neiswanger, Vatsal Sharan
2025	J	`jnl`	Hubble: a Model Suite to Advance the Study of LLM Memorization. CoRR Johnny Tian-Zheng Wei, Ameya Godbole, Mohammad Aflah Khan, Ryan Wang, Xiaoyuan Zhu, James Flemings, Nitya Kashyap, Krishna P. Gummadi, Willie Neiswanger, Robin Jia
2025	J	`jnl`	LLM Unlearning Without an Expert Curated Dataset. CoRR Xiaoyuan Zhu, Muru Zhang, Ollie Liu, Robin Jia, Willie Neiswanger
2025	J	`jnl`	LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch. CoRR Zhengzhong Liu, Bowen Tan, Hongyi Wang, Willie Neiswanger, Tianhua Tao, Haonan Li, Fajri Koto, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Liqun Ma, Liping Tang, Nikhil Ranjan, Yonghao Zhuang, Guowei He, Renxi Wang, Mingkai Deng, Robin Algayres, Yuanzhi Li, Zhiqiang Shen, Preslav Nakov, Eric P. Xing
2025	J	`jnl`	LYNX: Learning Dynamic Exits for Confidence-Controlled Reasoning. CoRR Ömer Faruk Akgül, Yusuf Hakan Kalayci, Rajgopal Kannan, Willie Neiswanger, Viktor K. Prasanna
2025	A*	`conf`	LiveBench: A Challenging, Contamination-Limited LLM Benchmark. ICLR 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 V. Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, Micah Goldblum
2025	J	`jnl`	METAGENE-1: Metagenomic Foundation Model for Pandemic Monitoring. CoRR Ollie Liu, Sami Jaghouar, Johannes Hagemann, Shangshang Wang, Jason Wiemels, Jeff Kaufman, Willie Neiswanger
2025	J	`jnl`	Probabilistic Graphical Models: A Concise Tutorial. CoRR Jacqueline R. M. A. Maasch, Willie Neiswanger, Stefano Ermon, Volodymyr Kuleshov
2025	J	`jnl`	Resa: Transparent Reasoning Models via SAEs. CoRR Shangshang Wang, Julian Asilis, Ömer Faruk Akgül, Enes Burak Bilgin, Ollie Liu, Deqing Fu, Willie Neiswanger
2025	J	`jnl`	Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF. CoRR Syrine Belakaria, Joshua Kazdan, Charles Marx, Chris Cundy, Willie Neiswanger, Sanmi Koyejo, Barbara E. Engelhardt, Stefano Ermon
2025	J	`jnl`	Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models. CoRR Woody Haosheng Gan, Deqing Fu, Julian Asilis, Ollie Liu, Dani Yogatama, Vatsal Sharan, Robin Jia, Willie Neiswanger
2025	J	`jnl`	Tina: Tiny Reasoning Models via LoRA. CoRR Shangshang Wang, Julian Asilis, Ömer Faruk Akgül, Enes Burak Bilgin, Ollie Liu, Willie Neiswanger
2025	—	`conf`	TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability. EMNLP (System Demonstrations) Mohammad Aflah Khan, Ameya Godbole, Johnny Tian-Zheng Wei, Ryan Yixiang Wang, James Flemings, Krishna P. Gummadi, Willie Neiswanger, Robin Jia
2025	J	`jnl`	TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability. CoRR Mohammad Aflah Khan, Ameya Godbole, Johnny Tian-Zheng Wei, Ryan Wang, James Flemings, Krishna P. Gummadi, Willie Neiswanger, Robin Jia
2025	J	`jnl`	Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning. CoRR Ang Li, Charles Wang, Kaiyu Yue, Zikui Cai, Ollie Liu, Deqing Fu, Peng Guo, Wang Bill Zhu, Vatsal Sharan, Robin Jia, Willie Neiswanger, Furong Huang, Tom Goldstein, Micah Goldblum
2024	J	`jnl`	DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models. CoRR Ollie Liu, Deqing Fu, Dani Yogatama, Willie Neiswanger
2024	J	`jnl`	Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions. CoRR Jiarui Zhang, Ollie Liu, Tianyu Yu, Jinyi Hu, Willie Neiswanger
2024	J	`jnl`	IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations. CoRR Deqing Fu, Ghazal Khalighinejad, Ollie Liu, Bhuwan Dhingra, Dani Yogatama, Robin Jia, Willie Neiswanger
2024	J	`jnl`	LiveBench: A Challenging, Contamination-Free LLM Benchmark. CoRR 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
2024	J	`jnl`	Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives. Mach. Learn. Sci. Technol. Sara A. Miskovich, Willie Neiswanger, William Colocho, Claudio Emma, Jacqueline Garrahan, Timothy Maxwell, Christopher Mayes, Stefano Ermon, Auralee Edelen, Daniel Ratner
2024	J	`jnl`	Political-LLM: Large Language Models in Political Science. CoRR Lincan Li, Jiaqi Li, Catherine Chen, Fred Gui, Hongjia Yang, Chenxiao Yu, Zhengguang Wang, Jianing Cai, Junlong Aaron Zhou, Bolin Shen, Alex Qian, Weixin Chen, Zhongkai Xue, Lichao Sun, Lifang He, Hanjie Chen, Kaize Ding, Zijian Du, Fangzhou Mu, Jiaxin Pei, Jieyu Zhao, Swabha Swayamdipta, Willie Neiswanger, Hua Wei, Xiyang Hu, Shixiang Zhu, Tianlong Chen, Yingzhou Lu, Yang Shi, Lianhui Qin, Tianfan Fu, Zhengzhong Tu, Yuzhe Yang, Jaemin Yoo, Jiaheng Zhang, Ryan A. Rossi, Liang Zhan, Liang Zhao, Emilio Ferrara, Yan Liu, Furong Huang, Xiangliang Zhang, Lawrence Rothenberg, Shuiwang Ji, Philip S. Yu, Yue Zhao, Yushun Dong
2024	J	`jnl`	Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-Awareness. CoRR Abdelmajid Essofi, Ridwan Salahuddeen, Munachiso Nwadike, Elnura Zhalieva, Kun Zhang, Eric P. Xing, Willie Neiswanger, Qirong Ho
2024	A*	`conf`	Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. AAAI Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec
2024	J	`jnl`	Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. CoRR Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec
2024	J	`jnl`	What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions. CoRR 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
2023	A*	`conf`	Betty: An Automatic Differentiation Library for Multilevel Optimization. ICLR Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing
2023	A*	`conf`	Generative Modeling Helps Weak Supervision (and Vice Versa). ICLR Benedikt Boecking, Nicholas Carl Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski
2023	A*	`conf`	Importance-aware Co-teaching for Offline Model-based Optimization. NeurIPS Ye Yuan, Can Chen, Zixuan Liu, Willie Neiswanger, Xue (Steve) Liu
2023	J	`jnl`	Importance-aware Co-teaching for Offline Model-based Optimization. CoRR Ye Yuan, Can Chen, Zixuan Liu, Willie Neiswanger, Xue Liu
2023	J	`jnl`	Kernelized Offline Contextual Dueling Bandits. CoRR Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger
2023	J	`jnl`	LLM360: Towards Fully Transparent Open-Source LLMs. CoRR 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
2023	A*	`conf`	Making Scalable Meta Learning Practical. NeurIPS Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric P. Xing
2023	J	`jnl`	Making Scalable Meta Learning Practical. CoRR Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric P. Xing
2023	A*	`conf`	Near-optimal Policy Identification in Active Reinforcement Learning. ICLR Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic
2023	A*	`conf`	Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. AAAI Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon
2023	J	`jnl`	Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching. CoRR Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon
2023	J	`jnl`	Sample Efficient Reinforcement Learning from Human Feedback via Active Exploration. CoRR Viraj Mehta, Vikramjeet Das, Ojash Neopane, Yijia Dai, Ilija Bogunovic, Jeff G. Schneider, Willie Neiswanger
2023	J	`jnl`	SlimPajama-DC: Understanding Data Combinations for LLM Training. CoRR Zhiqiang Shen, Tianhua Tao, Liqun Ma, Willie Neiswanger, Zhengzhong Liu, Hongyi Wang, Bowen Tan, Joel Hestness, Natalia Vassilieva, Daria Soboleva, Eric P. Xing
2022	A*	`conf`	A General Recipe for Likelihood-free Bayesian Optimization. ICML Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon
2022	J	`jnl`	A General Recipe for Likelihood-free Bayesian Optimization. CoRR Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon
2022	A*	`conf`	An Experimental Design Perspective on Model-Based Reinforcement Learning. ICLR Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger
2022	J	`jnl`	AutoML for Climate Change: A Call to Action. CoRR Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White
2022	J	`jnl`	Bayesian Algorithm Execution for Tuning Particle Accelerator Emittance with Partial Measurements. CoRR Sara A. Miskovich, Willie Neiswanger, William Colocho, Claudio Emma, Jacqueline Garrahan, Timothy Maxwell, Christopher Mayes, Stefano Ermon, Auralee Edelen, Daniel Ratner
2022	J	`jnl`	Betty: An Automatic Differentiation Library for Multilevel Optimization. CoRR Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing
2022	A*	`conf`	Exploration via Planning for Information about the Optimal Trajectory. NeurIPS Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger
2022	J	`jnl`	Exploration via Planning for Information about the Optimal Trajectory. CoRR Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger
2022	A*	`conf`	Generalizing Bayesian Optimization with Decision-theoretic Entropies. NeurIPS Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
2022	J	`jnl`	Generalizing Bayesian Optimization with Decision-theoretic Entropies. CoRR Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
2022	J	`jnl`	Generative Modeling Helps Weak Supervision (and Vice Versa). CoRR Benedikt Boecking, Willie Neiswanger, Nicholas Carl Roberts, Stefano Ermon, Frederic Sala, Artur Dubrawski
2022	A*	`conf`	IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling. AAAI Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David B. Lobell, Stefano Ermon
2022	A*	`conf`	Modular Conformal Calibration. ICML Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon
2022	J	`jnl`	Modular Conformal Calibration. CoRR Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon
2022	J	`jnl`	Near-optimal Policy Identification in Active Reinforcement Learning. CoRR Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic
2022	—	`conf`	Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis. EMNLP (Findings) Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency
2022	J	`jnl`	Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis. CoRR Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency
2021	J	`jnl`	Amortized Auto-Tuning: Cost-Efficient Transfer Optimization for Hyperparameter Recommendation. CoRR Yuxin Xiao, Eric P. Xing, Willie Neiswanger
2021	J	`jnl`	An Experimental Design Perspective on Model-Based Reinforcement Learning. CoRR Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger
2021	A*	`conf`	BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search. AAAI Colin White, Willie Neiswanger, Yash Savani
2021	A*	`conf`	Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. ICML Willie Neiswanger, Ke Alexander Wang, Stefano Ermon
2021	J	`jnl`	Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information. CoRR Willie Neiswanger, Ke Alexander Wang, Stefano Ermon
2021	A*	`conf`	Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. NeurIPS Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider
2021	J	`jnl`	IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling. CoRR Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David B. Lobell, Stefano Ermon
2021	A*	`conf`	Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. ICLR Benedikt Boecking, Willie Neiswanger, Eric P. Xing, Artur Dubrawski
2021	—	`conf`	Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. CDC Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff G. Schneider
2021	—	`conf`	Personalized Benchmarking with the Ludwig Benchmarking Toolkit. NeurIPS Datasets and Benchmarks Avanika Narayan, Piero Molino, Karan Goel, Willie Neiswanger, Christopher Ré
2021	J	`jnl`	Personalized Benchmarking with the Ludwig Benchmarking Toolkit. CoRR Avanika Narayan, Piero Molino, Karan Goel, Willie Neiswanger, Christopher Ré
2021	A*	`conf`	Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning. OSDI Aurick Qiao, Sang Keun Choe, Suhas Jayaram Subramanya, Willie Neiswanger, Qirong Ho, Hao Zhang, Gregory R. Ganger, Eric P. Xing
2021	—	`conf`	Synthetic Benchmarks for Scientific Research in Explainable Machine Learning. NeurIPS Datasets and Benchmarks Yang Liu, Sujay Khandagale, Colin White, Willie Neiswanger
2021	J	`jnl`	Synthetic Benchmarks for Scientific Research in Explainable Machine Learning. CoRR Yang Liu, Sujay Khandagale, Colin White, Willie Neiswanger
2021	J	`jnl`	Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification. CoRR Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger
2021	B	`conf`	Uncertainty quantification using martingales for misspecified Gaussian processes. ALT Willie Neiswanger, Aaditya Ramdas
2020	A*	`conf`	A Study on Encodings for Neural Architecture Search. NeurIPS Colin White, Willie Neiswanger, Sam Nolen, Yash Savani
2020	J	`jnl`	A Study on Encodings for Neural Architecture Search. CoRR Colin White, Willie Neiswanger, Sam Nolen, Yash Savani
2020	J	`jnl`	Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. CoRR Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider
2020	A	`conf`	ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. AISTATS Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2020	J	`jnl`	Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. CoRR Benedikt Boecking, Willie Neiswanger, Eric Poe Xing, Artur Dubrawski
2020	J	`jnl`	Methods for comparing uncertainty quantifications for material property predictions. Mach. Learn. Sci. Technol. Kevin Tran, Willie Neiswanger, Junwoong Yoon, Qingyang Zhang, Eric P. Xing, Zachary W. Ulissi
2020	J	`jnl`	Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. CoRR Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider
2020	J	`jnl`	Offline Contextual Bayesian Optimization for Nuclear Fusion. CoRR Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider
2020	J	`jnl`	Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning. CoRR Aurick Qiao, Willie Neiswanger, Qirong Ho, Hao Zhang, Gregory R. Ganger, Eric P. Xing
2020	—	`—`	Post-inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making. Willie Neiswanger
2020	J	`jnl`	Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. J. Mach. Learn. Res. Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2020	J	`jnl`	Uncertainty quantification using martingales for misspecified Gaussian processes. CoRR Willie Neiswanger, Aaditya Ramdas
2019	J	`jnl`	BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search. CoRR Colin White, Willie Neiswanger, Yash Savani
2019	J	`jnl`	ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. CoRR Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2019	A*	`conf`	Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
2019	A*	`conf`	Offline Contextual Bayesian Optimization. NeurIPS Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen
2019	J	`jnl`	ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. CoRR Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2019	J	`jnl`	Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. CoRR Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2018	J	`jnl`	Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful. CoRR Rajesh Chidambaram, Michael Kampffmeyer, Willie Neiswanger, Xiaodan Liang, Thomas Lachmann, Eric P. Xing
2018	J	`jnl`	Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. CoRR Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
2018	A*	`conf`	Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2018	J	`jnl`	Neural Architecture Search with Bayesian Optimisation and Optimal Transport. CoRR Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2017	J	`jnl`	Generalized Pólya Urn for Time-Varying Pitman-Yor Processes. J. Mach. Learn. Res. François Caron, Willie Neiswanger, Frank D. Wood, Arnaud Doucet, Manuel Davy
2017	A	`conf`	Performance Bounds for Graphical Record Linkage. AISTATS Rebecca C. Steorts, Matt Barnes, Willie Neiswanger
2017	J	`jnl`	Performance Bounds for Graphical Record Linkage. CoRR Rebecca C. Steorts, Matt Barnes, Willie Neiswanger
2017	A*	`conf`	Post-Inference Prior Swapping. ICML Willie Neiswanger, Eric P. Xing
2016	A*	`conf`	Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. ICML Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing
2016	J	`jnl`	Prior Swapping for Data-Independent Inference. CoRR Willie Neiswanger, Eric P. Xing
2015	J	`jnl`	Embarrassingly Parallel Variational Inference in Nonconjugate Models. CoRR Willie Neiswanger, Chong Wang, Eric P. Xing
2015	A	`conf`	Fast Function to Function Regression. AISTATS Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider
2014	A	`conf`	Asymptotically Exact, Embarrassingly Parallel MCMC. UAI Willie Neiswanger, Chong Wang, Eric P. Xing
2014	A	`conf`	Fast Distribution To Real Regression. AISTATS Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing
2014	J	`jnl`	Fast Function to Function Regression. CoRR Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider
2014	A	`conf`	Modeling Citation Networks Using Latent Random Offsets. UAI Willie Neiswanger, Chong Wang, Qirong Ho, Eric P. Xing
2014	A	`conf`	The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling. AISTATS Willie Neiswanger, Frank D. Wood, Eric P. Xing
2013	J	`jnl`	Asymptotically Exact, Embarrassingly Parallel MCMC. CoRR Willie Neiswanger, Chong Wang, Eric P. Xing
2013	J	`jnl`	Fast Distribution To Real Regression. CoRR Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing
2012	J	`jnl`	Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures CoRR Willie Neiswanger, Frank D. Wood