Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures - Willie Neiswanger; Frank D. Wood (CoRR, 2012)

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

Fast Distribution To Real Regression. - Junier B. Oliva; Willie Neiswanger; Barnabás Póczos; Jeff G. Schneider; Eric P. Xing (AISTATS, 2014)

Modeling Citation Networks Using Latent Random Offsets. - Willie Neiswanger; Chong Wang 002; Qirong Ho; Eric P. Xing (UAI, 2014)

Asymptotically Exact, Embarrassingly Parallel MCMC. - Willie Neiswanger; Chong Wang 002; Eric P. Xing (UAI, 2014)

Fast Function to Function Regression. - Junier B. Oliva; Willie Neiswanger; Barnabás Póczos; Eric P. Xing; Jeff G. Schneider (CoRR, 2014)

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

Embarrassingly Parallel Variational Inference in Nonconjugate Models. - Willie Neiswanger; Chong Wang 002; Eric P. Xing (CoRR, 2015)

Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. - Yu-Xiang Wang 003; Veeranjaneyulu Sadhanala; Wei Dai 003; Willie Neiswanger; Suvrit Sra; Eric P. Xing (ICML, 2016)

Prior Swapping for Data-Independent Inference. - Willie Neiswanger; Eric P. Xing (CoRR, 2016)

Performance Bounds for Graphical Record Linkage. - Rebecca C. Steorts; Matt Barnes 001; Willie Neiswanger (AISTATS, 2017)

Post-Inference Prior Swapping. - Willie Neiswanger; Eric P. Xing (ICML, 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)

Neural Architecture Search with Bayesian Optimisation and Optimal Transport. - Kirthevasan Kandasamy; Willie Neiswanger; Jeff Schneider 001; Barnabás Póczos; Eric P. Xing (NeurIPS, 2018)

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. - Kirthevasan Kandasamy; Willie Neiswanger; Reed Zhang; Akshay Krishnamurthy; Jeff Schneider 001; Barnabás Póczos (CoRR, 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 Posterior Sampling for Adaptive Goal Oriented Design of Experiments. - Kirthevasan Kandasamy; Willie Neiswanger; Reed Zhang; Akshay Krishnamurthy; Jeff Schneider 001; Barnabás Póczos (ICML, 2019)

Offline Contextual Bayesian Optimization. - Ian Char; Youngseog Chung; Willie Neiswanger; Kirthevasan Kandasamy; Oak Nelson; Mark D. Boyer; Egemen Kolemen (NeurIPS, 2019)

ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. - Willie Neiswanger; Kirthevasan Kandasamy; Barnabás Póczos; Jeff Schneider 001; Eric P. Xing (CoRR, 2019)

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. - Ksenia Korovina; Sailun Xu; Kirthevasan Kandasamy; Willie Neiswanger; Barnabás Póczos; Jeff Schneider 001; Eric P. Xing (AISTATS, 2020)

A Study on Encodings for Neural Architecture Search. - Colin White; Willie Neiswanger; Sam Nolen; Yash Savani (NeurIPS, 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 001 (CoRR, 2020)

Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning. - Aurick Qiao; Willie Neiswanger; Qirong Ho; Hao Zhang 025; Gregory R. Ganger; Eric P. Xing (CoRR, 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 001; Barnabás Póczos; Eric P. Xing (J. Mach. Learn. Res., 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)

Post-inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making. - Willie Neiswanger (2020)

BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search. - Colin White; Willie Neiswanger; Yash Savani (AAAI, 2021)

Uncertainty quantification using martingales for misspecified Gaussian processes. - Willie Neiswanger; Aaditya Ramdas (ALT, 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 (CDC, 2021)

Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. - Benedikt Boecking; Willie Neiswanger; Eric P. Xing; Artur Dubrawski (ICLR, 2021)

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

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. - Youngseog Chung; Willie Neiswanger; Ian Char; Jeff Schneider 001 (NeurIPS, 2021)

Synthetic Benchmarks for Scientific Research in Explainable Machine Learning. - Yang Liu; Sujay Khandagale; Colin White; Willie Neiswanger (NeurIPS Datasets and Benchmarks, 2021)

Personalized Benchmarking with the Ludwig Benchmarking Toolkit. - Avanika Narayan; Piero Molino; Karan Goel; Willie Neiswanger; Christopher Ré (NeurIPS Datasets and Benchmarks, 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 025; Gregory R. Ganger; Eric P. Xing (OSDI, 2021)

Amortized Auto-Tuning: Cost-Efficient Transfer Optimization for Hyperparameter Recommendation. - Yuxin Xiao; Eric P. Xing; Willie Neiswanger (CoRR, 2021)

Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification. - Youngseog Chung; Ian Char; Han Guo; Jeff Schneider 001; Willie Neiswanger (CoRR, 2021)

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 (AAAI, 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 (EMNLP (Findings), 2022)

An Experimental Design Perspective on Model-Based Reinforcement Learning. - Viraj Mehta; Biswajit Paria; Jeff Schneider 001; Stefano Ermon; Willie Neiswanger (ICLR, 2022)

Modular Conformal Calibration. - Charles Marx; Shengjia Zhao; Willie Neiswanger; Stefano Ermon (ICML, 2022)

A General Recipe for Likelihood-free Bayesian Optimization. - Jiaming Song; Lantao Yu; Willie Neiswanger; Stefano Ermon (ICML, 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 001; Willie Neiswanger (NeurIPS, 2022)

Generalizing Bayesian Optimization with Decision-theoretic Entropies. - Willie Neiswanger; Lantao Yu; Shengjia Zhao; Chenlin Meng; Stefano Ermon (NeurIPS, 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)

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)

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

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

Betty: An Automatic Differentiation Library for Multilevel Optimization. - Sang Keun Choe; Willie Neiswanger; Pengtao Xie; 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 001; Andreas Krause 001; Ilija Bogunovic (ICLR, 2023)

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

Importance-aware Co-teaching for Offline Model-based Optimization. - Ye Yuan 017; Can Chen 005; Zixuan Liu 001; Willie Neiswanger; Xue (Steve) Liu (NeurIPS, 2023)

Kernelized Offline Contextual Dueling Bandits. - Viraj Mehta; Ojash Neopane; Vikramjeet Das; Sen Lin; Jeff Schneider 001; Willie Neiswanger (CoRR, 2023)

SlimPajama-DC: Understanding Data Combinations for LLM Training. - Zhiqiang Shen; Tianhua Tao; Liqun Ma; Willie Neiswanger; Zhengzhong Liu 001; Hongyi Wang 001; Bowen Tan; Joel Hestness; Natalia Vassilieva; Daria Soboleva; 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)

LLM360: Towards Fully Transparent Open-Source LLMs. - Zhengzhong Liu 001; Aurick Qiao; Willie Neiswanger; Hongyi Wang 001; Bowen Tan; Tianhua Tao; Junbo Li; Yuqi Wang; Suqi Sun; Omkar Pangarkar; Richard Fan; Yi Gu 002; et al. (CoRR, 2023)

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

DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models. - Ollie Liu; Deqing Fu; Dani Yogatama; Willie Neiswanger (CoRR, 2024)

IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations. - Deqing Fu; Ghazal Khalighinejad; Ollie Liu; Bhuwan Dhingra; Dani Yogatama; Robin Jia; Willie Neiswanger (CoRR, 2024)

What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions. - 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; et al. (CoRR, 2024)

LiveBench: A Challenging, Contamination-Free LLM Benchmark. - Colin White; Samuel Dooley; Manley Roberts; Arka Pal; Benjamin Feuer; Siddhartha Jain 001; Ravid Shwartz-Ziv; Neel Jain; Khalid Saifullah; Siddartha Naidu; Chinmay Hegde; Yann LeCun; et al. (CoRR, 2024)

Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-Awareness. - Abdelmajid Essofi; Ridwan Salahuddeen; Munachiso Nwadike; Elnura Zhalieva; Kun Zhang 001; Eric P. Xing; Willie Neiswanger; Qirong Ho (CoRR, 2024)

Political-LLM: Large Language Models in Political Science. - Lincan Li; Jiaqi Li; Catherine Chen 003; Fred Gui; Hongjia Yang; Chenxiao Yu; Zhengguang Wang; Jianing Cai; Junlong Aaron Zhou; Bolin Shen; Alex Qian; Weixin Chen; et al. (CoRR, 2024)

Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions. - Jiarui Zhang 002; Ollie Liu; Tianyu Yu; Jinyi Hu; Willie Neiswanger (CoRR, 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., 2024)

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

LiveBench: A Challenging, Contamination-Limited LLM Benchmark. - Colin White; Samuel Dooley; Manley Roberts; Arka Pal; Benjamin Feuer; Siddhartha Jain 001; Ravid Shwartz-Ziv; Neel Jain; Khalid Saifullah; Sreemanti Dey; Shubh-Agrawal; Sandeep Singh Sandha; et al. (ICLR, 2025)

METAGENE-1: Metagenomic Foundation Model for Pandemic Monitoring. - Ollie Liu; Sami Jaghouar; Johannes Hagemann; Shangshang Wang; Jason Wiemels; Jeff Kaufman; Willie Neiswanger (CoRR, 2025)

LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch. - Zhengzhong Liu 001; Bowen Tan; Hongyi Wang 001; Willie Neiswanger; Tianhua Tao; Haonan Li 002; Fajri Koto; Yuqi Wang; Suqi Sun; Omkar Pangarkar; Richard Fan; Yi Gu 002; et al. (CoRR, 2025)

Efficient Evaluation of Multi-Task Robot Policies With Active Experiment Selection. - Abrar Anwar; Rohan Gupta; Zain Merchant; Sayan Ghosh; Willie Neiswanger; Jesse Thomason (CoRR, 2025)

Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF. - Syrine Belakaria; Joshua Kazdan; Charles Marx; Chris Cundy; Willie Neiswanger; Sanmi Koyejo; Barbara E. Engelhardt; Stefano Ermon (CoRR, 2025)

AI-University: An LLM-based platform for instructional alignment to scientific classrooms. - Mostafa Faghih Shojaei; Rahul Gulati; Benjamin A. Jasperson; Shangshang Wang; Simone Cimolato; Dangli Cao; Willie Neiswanger; Krishna C. Garikipati (CoRR, 2025)

Tina: Tiny Reasoning Models via LoRA. - Shangshang Wang; Julian Asilis; Ömer Faruk Akgül; Enes Burak Bilgin; Ollie Liu; Willie Neiswanger (CoRR, 2025)

Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models. - Woody Haosheng Gan; Deqing Fu; Julian Asilis; Ollie Liu; Dani Yogatama; Vatsal Sharan; Robin Jia; Willie Neiswanger (CoRR, 2025)

Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test. - Xiaoyuan Zhu; Yaowen Ye; Tianyi Qiu; Hanlin Zhu; Sijun Tan; Ajraf Mannan; Jonathan Michala; Raluca Ada Popa; Willie Neiswanger (CoRR, 2025)

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered. - Siddartha Devic; Tejas Srinivasan; Jesse Thomason; Willie Neiswanger; Vatsal Sharan (CoRR, 2025)

Resa: Transparent Reasoning Models via SAEs. - Shangshang Wang; Julian Asilis; Ömer Faruk Akgül; Enes Burak Bilgin; Ollie Liu; Deqing Fu; Willie Neiswanger (CoRR, 2025)

Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning. - 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; et al. (CoRR, 2025)

Probabilistic Graphical Models: A Concise Tutorial. - Jacqueline R. M. A. Maasch; Willie Neiswanger; Stefano Ermon; Volodymyr Kuleshov (CoRR, 2025)

TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability. - Mohammad Aflah Khan; Ameya Godbole; Johnny Tian-Zheng Wei; Ryan Wang; James Flemings; Krishna P. Gummadi; Willie Neiswanger; Robin Jia (CoRR, 2025)

LLM Unlearning Without an Expert Curated Dataset. - Xiaoyuan Zhu; Muru Zhang; Ollie Liu; Robin Jia; Willie Neiswanger (CoRR, 2025)