Willie Neiswanger

Willie Neiswanger

Stanford University

H-index: 23

North America-United States

About Willie Neiswanger

Willie Neiswanger, With an exceptional h-index of 23 and a recent h-index of 21 (since 2020), a distinguished researcher at Stanford University, specializes in the field of Machine Learning, Statistics, Optimization, Sequential Decision Making, AI-for-Science.

His recent articles reflect a diverse array of research interests and contributions to the field:

Making scalable meta learning practical

Importance-aware co-teaching for offline model-based optimization

IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations

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

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

Automated experimental design of safe rampdowns via probabilistic machine learning

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

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

Willie Neiswanger Information

University

Position

Postdoc Computer Science

Citations(all)

2852

Citations(since 2020)

2503

Cited By

1011

hIndex(all)

23

hIndex(since 2020)

21

i10Index(all)

37

i10Index(since 2020)

35

Email

University Profile Page

Stanford University

Google Scholar

View Google Scholar Profile

Willie Neiswanger Skills & Research Interests

Machine Learning

Statistics

Optimization

Sequential Decision Making

AI-for-Science

Top articles of Willie Neiswanger

Title

Journal

Author(s)

Publication Date

Making scalable meta learning practical

Advances in neural information processing systems

Sang Choe

Sanket Vaibhav Mehta

Hwijeen Ahn

Willie Neiswanger

Pengtao Xie

...

2024/2/13

Importance-aware co-teaching for offline model-based optimization

Advances in Neural Information Processing Systems

Ye Yuan

Can Sam Chen

Zixuan Liu

Willie Neiswanger

Xue Steve Liu

2024/2/13

IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations

arXiv preprint arXiv:2404.01266

Deqing Fu

Ghazal Khalighinejad

Ollie Liu

Bhuwan Dhingra

Dani Yogatama

...

2024/4/1

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

arXiv preprint arXiv:2402.02392

Ollie Liu

Deqing Fu

Dani Yogatama

Willie Neiswanger

2024/2/4

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

Machine Learning: Science and Technology

Sara Ayoub Miskovich

Willie Neiswanger

William Colocho

Claudio Emma

Jacqueline Garrahan

...

2024/1/10

Automated experimental design of safe rampdowns via probabilistic machine learning

Nuclear Fusion

Viraj Mehta

Jayson Barr

Joseph Abbate

Mark D Boyer

Ian Char

...

2024/2/23

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

AAAI 2024

Tailin Wu

Willie Neiswanger

Hongtao Zheng

Stefano Ermon

Jure Leskovec

2024/2/13

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

Ian Char

Youngseog Chung

Rohan Shah

Willie Neiswanger

Jeff Schneider

2023/12/22

Sample Efficient Reinforcement Learning from Human Feedback via Active Exploration

Viraj Mehta

Vikramjeet Das

Ojash Neopane

Yijia Dai

Ilija Bogunovic

...

2023/10/13

Slimpajama-dc: Understanding data combinations for llm training

arXiv preprint arXiv:2309.10818

Zhiqiang Shen

Tianhua Tao

Liqun Ma

Willie Neiswanger

Joel Hestness

...

2023/9/19

Llm360: Towards fully transparent open-source llms

arXiv preprint arXiv:2312.06550

Zhengzhong Liu

Aurick Qiao

Willie Neiswanger

Hongyi Wang

Bowen Tan

...

2023/12/11

Kernelized Offline Contextual Dueling Bandits

arXiv preprint arXiv:2307.11288

Viraj Mehta

Ojash Neopane

Vikramjeet Das

Sen Lin

Jeff Schneider

...

2023/7/21

arXiv: Bayesian Optimization Algorithms for Accelerator Physics

Ryan Roussel

Andrea Santamaria Garcia

Weijian Lin

Tobias Boltz

Jan Kaiser

...

2023/12/9

Bayesian Optimization Algorithms for Accelerator Physics

Ryan Roussel

Auralee L Edelen

Tobias Boltz

Dylan Kennedy

Zhe Zhang

...

2023/12/9

Offline imitation learning with suboptimal demonstrations via relaxed distribution matching

Proceedings of the AAAI conference on artificial intelligence

Lantao Yu

Tianhe Yu

Jiaming Song

Willie Neiswanger

Stefano Ermon

2023/6/26

Targeted materials discovery using Bayesian algorithm execution

arXiv preprint arXiv:2312.16078

Sathya Chitturi

Akash Ramdas

Yue Wu

Brian Rohr

Stefano Ermon

...

2023/12/26

Memoization-Aware Bayesian Optimization for AI Pipelines with Unknown Costs

Abdelmajid Essofi

Ridwan Salahuddeen

Munachiso S Nwadike

Navish Kumar

Kun Zhang

...

2023/10/13

An Experimental Design Perspective on Model-Based Reinforcement Learning

Viraj Mehta

Biswajit Paria

Jeff Schneider

Stefano Ermon

Willie Neiswanger

2022

AutoML for Climate Change: A Call to Action

arXiv preprint arXiv:2210.03324

Renbo Tu

Nicholas Roberts

Vishak Prasad

Sibasis Nayak

Paarth Jain

...

2022/10/7

Generative modeling helps weak supervision (and vice versa)

Benedikt Boecking

Willie Neiswanger

Nicholas Roberts

Stefano Ermon

Frederic Sala

...

2023

See List of Professors in Willie Neiswanger University(Stanford University)

Co-Authors

H-index: 133
Michael Dustin

Michael Dustin

University of Oxford

H-index: 114
Eric Xing

Eric Xing

Carnegie Mellon University

H-index: 76
Stefano Ermon

Stefano Ermon

Stanford University

H-index: 68
Barnabas Poczos

Barnabas Poczos

Carnegie Mellon University

H-index: 64
Jeff Schneider

Jeff Schneider

Carnegie Mellon University

H-index: 31
Egemen Kolemen

Egemen Kolemen

Princeton University

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