Willie Neiswanger

@willieneis

Assistant Professor @USC in CS + AI. Previously @Stanford, @SCSatCMU. Machine Learning, Decision Making, AI-for-Science, Generative Models.

Los Angeles

willieneis.github.io

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Willie Neiswanger
@willieneis
Jul 7, 2021
(1/9) Presenting: Bayesian Algorithm Execution (BAX) and the InfoBAX algorithm. Bayesian optimization finds global optima of expensive black-box functions. But what about other function properties? w/ @KAlexanderWang @StefanoErmon at #ICML2021 URL: willieneis.github.io/bax-website
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Willie Neiswanger
@willieneis
Oct 8, 2025
It was great to see @thinkymachines LoRA w/o Regret blog, which connects nicely to our work on Tina (LoRA for RL). For wider use, we’re releasing a clean implementation of RL with LoRA, DoRA, QLoRA/QDoRA, plus speedups & more, across models from 1.5B–32B. Nice work @UpupWang!
Shangshang Wang
@UpupWang
Oct 8, 2025
We now know that LoRA can match full-parameter RL training (from x.com/thinkymachines… and our Tina paper arxiv.org/abs/2504.15777), but what about DoRA, QLoRA, and more? We are releasing a clean LoRA-for-RL repo to explore them all. github.com/shangshang-wan…
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Willie Neiswanger
@willieneis
Feb 19, 2025
An awesome set of resources on LLM reasoning and test-time compute, compiled by @UpUpWang — check it out!
Shangshang Wang
@UpupWang
Feb 19, 2025
🔍 Diving deep into LLM reasoning? From OpenAI's o-series to DeepSeek R1, from post-training to test-time compute — we break it down into structured spreadsheets. 🧵
Willie Neiswanger
@willieneis
Jan 6, 2025
Excited to release METAGENE-1, a 7B parameter metagenomic foundation model, built to aid in pathogen detection & pandemic monitoring. Pretrained on 1.5 trillion base pairs of DNA/RNA sequenced from wastewater. A collab w/ @USC, @PrimeIntellect, & the Nucleic Acid Observatory. 🧵
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Willie Neiswanger
@willieneis
Apr 3, 2024
Benchmarking multimodal foundation models via isomorphic prompts (different data modalities, same information) — great work led by @DeqingFu, @ghazalkhn, and @olliezliu!
Deqing Fu
@DeqingFu
Apr 2, 2024
Do multimodal foundation models treat every modality equally? Hint: Humans have picture superiority. How about machines? Introducing IsoBench, a benchmark for multimodal models with isomorphic inputs. 🔗 IsoBench.github.io