1. X
  2. Willie Neiswanger
Log inSign up
Willie Neiswanger
187 posts
Image
user avatar
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
Joined March 2009
291
Following
1,501
Followers
RepliesRepliesMediaMedia

New to X?

Sign up now to get your own personalized timeline!

Create account

By signing up, you agree to the Terms of Service and Privacy Policy, including Cookie Use.

Terms·Privacy·Cookies·Accessibility·Ads Info·© 2026 X Corp.
Don't miss what's happening
People on X are the first to know.
Log inSign up
  • user avatar
    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
    Image
    00:00
  • user avatar
    Willie Neiswanger
    @willieneis
    Nov 15, 2023
    Excited to share that I will join @USC as an Asst. Professor of Computer Science in Jan 2024—and I’m recruiting students for my new lab! 📣 Come work at the intersection of machine learning, decision making, generative AI, and AI-for-science. More info: willieneis.github.io/lab
    Image
    Image
    Image
    70K
  • user avatar
    Willie Neiswanger
    @willieneis
    Jul 12, 2022
    (1/4) Very excited about a new project we’ve been working on — Betty! Betty is an autodiff library for multilevel optimization and generalized meta-learning ✨ GitHub: github.com/leopard-ai/bet… ArXiv: arxiv.org/abs/2207.02849 Docs: leopard-ai.github.io/betty/ Led by @sangkeun_choe 🙌
    Image
    Image
    Image
  • user avatar
    Willie Neiswanger
    @willieneis
    Jan 8, 2023
    📢 Get ready for a deep dive into the world of modern experimental design and active learning! We’re starting a Reading Group that explores these techniques, and their applications to real-world problems. 🚨 Details: realworldml.github.io cc/ @ilijabogunovic @mutny_ml
    Image
    Image
    26K
  • user avatar
    Willie Neiswanger
    @willieneis
    Sep 27, 2023
    Reminder! Oct 4 deadline for #NeurIPS2023 Workshop on Adaptive Experimental Design & Active Learning in the Real World🔬 💸$1000 best student paper award.👥Great set of speakers: @MihaelaVDS @annadgoldie @nathankallus @eytan @EmmaBrunskill @erika_alden_d realworldml.github.io/neurips2023
    Image
    12K
  • user avatar
    Willie Neiswanger
    @willieneis
    Jul 7, 2021
    Replying to @willieneis
    (9/9) Our method has connections to stats/ML areas such as Bayesian optimal experimental design, information theory, optimal sensor placement, stepwise uncertainty reduction, likelihood-free Bayesian inference, and more. See more discussion in our paper arxiv.org/abs/2104.09460
  • user avatar
    Willie Neiswanger
    @willieneis
    Jul 18, 2022
    Check out the fantastic set of accepted papers in the ReALML at #icml2022 workshop! (Workshop on Adaptive Experimental Design and Active Learning in the Real World 🌎) realworldml.github.io/accepted/ We hope to see you there, this Friday, in Room 309!
    Image
    Image
    Image
  • user avatar
    Willie Neiswanger
    @willieneis
    Jun 2, 2022
    Final reminder: ReALML @ #icml2022 workshop (realworldml.github.io) submission deadline is at the end-of-day on June 3 (anywhere on earth) — still plenty of time to submit! Note that we accept papers submitted to NeurIPS, and there is a $1000 best student paper award!
    Image
  • user avatar
    Willie Neiswanger
    @willieneis
    Jul 7, 2021
    Replying to @willieneis
    (2/9) Methods like Bayes opt / quadrature can be viewed as estimating properties of a black-box function (e.g. global optima, integrals). But in many applications we also care about local optima, level sets, top-k optima, boundaries, integrals, roots, graph properties, and more
    Example properties of black-box functions and associated applications in which it is useful to estimate these properties.
  • user avatar
    Willie Neiswanger
    @willieneis
    Jan 31, 2023
    This week in our online REALML reading group: Emmanuel Bengio (@folinoid) will present “Introduction to GFlowNet” Thursday Feb 2 at 10am PT / 6pm GMT / 7pm CET — see you there! For more info: realworldml.github.io cc @ilijabogunovic @mutny_ml
    Image
    Image
    Image
    1.7K
  • user avatar
    Willie Neiswanger
    @willieneis
    Jul 17, 2020
    See the list of accepted papers here: realworldml.github.io/accepted/ Lots of interesting work!
    user avatar
    Yisong Yue
    @yisongyue
    Jul 17, 2020
    (Virtual) Workshop on Real World Experiment Design & Active Learning! Tune in Saturday July 18th! #ICML2020 site: icml.cc/virtual/2020/w… Talk videos will be made publicly available at a later date.
    Image
  • user avatar
    Willie Neiswanger
    @willieneis
    May 20, 2021
    Check out our upcoming ICML 2021 workshop on Machine Learning for Data (ml4data)! This workshop will focus on how ML techniques can be used to facilitate a range of data operations (e.g. ML-assisted labeling, synthesis, selection, augmentation), and the associated challenges.
    user avatar
    ml4data
    @ml4data
    May 20, 2021
    🚨Announcing the ML4data workshop at ICML 2021🚨 — a workshop focused on how we use ML for our most precious resource: data sites.google.com/view/ml4data #icml2021 #icml21 #ml4data
    Image
  • user avatar
    Willie Neiswanger
    @willieneis
    Jul 12, 2022
    Replying to @willieneis
    (4/4) Fun fact: Betty is named after @sangkeun_choe’s dog, Betty. A big thanks to our other collaborators and co-developers @ericxing and @cmuptx 🙌 To contribute: github.com/leopard-ai/bet… Happy multilevel optimization programming!
    Image
  • user avatar
    Willie Neiswanger
    @willieneis
    Jul 12, 2022
    Replying to @willieneis
    (2/4) Betty can be used for many applications: github.com/leopard-ai/bet… * Differentiable hyperparameter tuning * Data/sample reweighting * Domain adaptation in pretraining & finetuning * Differentiable architecture search (DARTS) * Implicit MAML/few shot learning * RL, GANs, ++
    Image
Advertisement
Advertisement