Log inSign up
Stephen Bates
278 posts
user avatar
Stephen Bates
@stats_stephen
Assistant Professor, MIT EECS. Rigorous stats & ML methods for data-driven science and reliable AI systems. My research group is hiring postdocs & PhDs!
stephenbates19.github.io
Joined April 2018
330
Following
3,458
Followers

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
  • Pinned
    user avatar
    Stephen Bates
    @stats_stephen
    Oct 15, 2021
    📰 Excited to share our new work on risk control in prediction! Multiple testing leads to practical calibration algorithms with PAC guarantees for any statistical error rate. Works with any model + data distribution! arxiv.org/abs/2110.01052 #Statistics #MachineLearning
    user avatar
    Anastasios Nikolas Angelopoulos
    Arena.ai
    @ml_angelopoulos
    Oct 15, 2021
    Thrilled to share Learn then Test, a tool to calibrate any model to control risk (eg. IOU, recall in object detection). No assns on model/data. See arXiv arxiv.org/abs/2110.01052 + Colab colab.research.google.com/github/aangelo… ✍️w/@stats_stephen, E.J. Candes, M.I. Jordan, @lihua_lei_stat! 🧵1/n
    00:00
  • user avatar
    Stephen Bates
    @stats_stephen
    Sep 13, 2023
    Excited to share that I've joined MIT as an assistant professor in EECS! I'm thrilled to join many thoughtful, inspiring colleagues. Looking ahead, I'm working to develop statistical principles for AI models so that we can use them for science and reliable automated systems.
    89K
  • user avatar
    Stephen Bates
    @stats_stephen
    Dec 13, 2022
    Want to learn about concentration inequalities and high-dimensional statistics? Roman Vershynin just released 41 lecture videos! They go along with his beautiful book. This is an amazing new resource! math.uci.edu/~rvershyn/teac…
  • user avatar
    Stephen Bates
    @stats_stephen
    Nov 19, 2024
    Our new textbook Theoretical Foundations of Conformal Prediction is out! Conformal prediction is a a statistical technique that augments ML systems with uncertainty information for safe deployment. This book lays out the core theory.
    arXiv logo
    arxiv.org
    Theoretical Foundations of Conformal Prediction
    This book is about conformal prediction and related inferential techniques that build on permutation tests and exchangeability. These techniques are useful in a diverse array of tasks, including...
    41K
  • user avatar
    Stephen Bates
    @stats_stephen
    Feb 10, 2022
    Excited to share a new, simple regression adjustment to get causal estimates with longitudinal data! arxiv.org/abs/2201.13451 Causal inference with longitudinal data is hard!! Why? A 🧵👇 w/ @edwardhkennedy, @robtibshirani, V Ventura, and L Wasserman 1/n
  • user avatar
    Stephen Bates
    @stats_stephen
    Dec 5, 2023
    If you're a prospective PhD student interested in statistics & uncertainty for ML 🤖⁉️, AI for science 🧪, or data with strategic behavior ♟️, I have openings in my group! Consider applying to the MIT EECS PhD program and mention me in your application. gradapply.mit.edu/eecs/apply/log…
    46K
  • user avatar
    Stephen Bates
    @stats_stephen
    Jul 2, 2021
    Need to give your ML model 🤖 reliable uncertainty quantification? Check out our new Gentle Intro to Conformal Prediction tutorial + video. You get valid confidence sets with any model for any (unknown) distribution, no retraining. sites.google.com/berkeley.edu/d… with @ml_angelopoulos
  • user avatar
    Stephen Bates
    @stats_stephen
    Sep 16, 2024
    🚨Postdoc positions🚨 Excited to announce two postdoc openings in my research group at @MITEECS & @MITLIDS! We're working on statistical uncertainty quantification with AI models, AI for science, and statistics with strategic agents. Join us😀 stephenbates19.github.io/postdoc_call.h…
    18K
  • user avatar
    Stephen Bates
    @stats_stephen
    Nov 17, 2023
    Excited to share that our work on prediction-powered inference just came out in Science!
    user avatar
    Science Magazine
    @ScienceMagazine
    Nov 16, 2023
    A new statistical framework dubbed “prediction-powered inference” could enable researchers to draw valid and more data-efficient scientific conclusions using datasets enriched with machine-learning predictions. Learn more in Science: scim.ag/4QJ
    23K
  • user avatar
    Stephen Bates
    @stats_stephen
    Sep 7, 2022
    Deep learning predictions should *always* come with error bars. Conformal prediction is a practical, easy-to-use statistical technique for this. Check out our tutorial 👇 for a simple introduction. Lots of real examples with Jupyter notebooks!! 🦾 arxiv.org/abs/2107.07511
    user avatar
    Anastasios Nikolas Angelopoulos
    Arena.ai
    @ml_angelopoulos
    Sep 7, 2022
    📢Huge update to Gentle Introduction to Conformal Prediction📢 arxiv.org/abs/2107.07511 Notebooks for EVERY example, easy-2-run WITHOUT model/data download. Open+run in Colab!✅ New repo here: github.com/aangelopoulos/… New sections on time-series and risk control!✅ More in 🧵
    Updated table of contents to Gentle Introduction, including conformal risk control, conformal under distribution drift, 5 worked examples of conformal prediction, and full conformal prediction.
  • user avatar
    Stephen Bates
    @stats_stephen
    Apr 11, 2021
    What is cross-validation estimating? It turns out the answer is *not* "the accuracy of the model from my data" but is instead "the average accuracy over many unseen training sets" (at least for regression). New work with Trevor Hastie and Rob Tibshirani.
    user avatar
    rob tibshirani
    @robtibshirani
    Apr 1, 2021
    With postdoc Stephen Bates and Trevor Hastie, I have just completed a new paper "Cross-validation: what does it estimate and how well does it do it?" statweb.stanford.edu/~tibs/ftp/NCV.…
  • user avatar
    Stephen Bates
    @stats_stephen
    Jul 23, 2021
    🚨The Distribution-free Uncertainty Quantification ICML workshop kicks off tomorrow!🚨 Leading off the morning session will be Rina Barber, Michael Jordan, Vladimir Vovk, Larry Wasserman, and Leying Guan. sites.google.com/berkeley.edu/d…
  • user avatar
    Stephen Bates
    @stats_stephen
    Jan 16, 2025
    Announcing ICLR '25 workshop: Quantify Uncertainty and Hallucination in Foundation Models: The Next Frontier in Reliable AI How can we trust large language models (LLMs) when they generate text with confidence, but sometimes hallucinate or fail to recognize their own
    5.3K
  • user avatar
    Stephen Bates
    @stats_stephen
    Jan 25, 2023
    If you want to use ML outputs in your regression model but need valid confidence intervals, we have a solution for you. Check out Prediction-Powered Inference! New work now online: arxiv.org/abs/2301.09633 Valid with any ML model and any data set -- no assumptions. 🤖🙌
    user avatar
    Anastasios Nikolas Angelopoulos
    Arena.ai
    @ml_angelopoulos
    Jan 25, 2023
    📯Prediction-Powered Inference📯 arxiv.org/abs/2301.09633 With the rise of AlphaFold etc., people are using ML predictions to replace costly experimental data. But predictions aren't perfect; can we still use them for rigorous downstream inferences? The answer: yes. A 🧵
    Left: A picture of a phosphorylated protein. Middle-Right: confidence intervals. The prediction-powered confidence interval is correct, while the imputed one is too small and the classical one is too big.
    11K