Maxime Cauchois maxcauch [at] stanford [dot] edu

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Bio. I am a fifth-year Ph.D. student in Statistics at Stanford University advised by John Duchi. My research interests lie in a convex combination of machine learning, statstics and optimization, and especially in how to quantify models uncertainty and make them more robust to changing environments.

Previously, I completed my undergraduate studies at Ecole Polytechnique from which I obtained a B.S. and a M.S. in 2016 and 2017. I also spent internships at Bloomberg LP as a Quantitative Researcher in 2017, where I worked with Bruno Dupire and Julien Guyon, at Google as a Data Scientist in 2019, and at Two Sigma Investments as a quantitative modeler in 2021.

You can find my complete Resume here.

Publications

Knowing what you know: valid confidence sets in multiclass and multilabel prediction.
Maxime Cauchois, Suyash Gupta, John Duchi.
Journal of Machine Learning Research, 22(81):1--42, 2021
[jmlr] [arxiv]

Preprints

The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting
Alnur Ali, Maxime Cauchois, John Duchi.
In submission, 2022
[arxiv] [pdf]
A comment and erratum on "Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE?"
Maxime Cauchois, Alnur Ali, John Duchi.
In submission, 2022
[arxiv] [pdf]
Predictive Inference with Weak Supervision
Maxime Cauchois, Suyash Gupta, Alnur Ali, John Duchi.
In submission, 2022
[arxiv] [pdf]
Robust Validation: Confident Predictions Even When Distributions Shift
Maxime Cauchois, Suyash Gupta, Alnur Ali, John Duchi.
Major Revision at Journal of American Statistical Association, 2021
[arxiv] [pdf]

Teaching

Instructor

  • STATS 100: Mathematics of Sports (Fall 2019).
  • STATS 302: Qualifying Exams Workshop (Summer 2020).

    • Teaching Assistant

  • STATS 310B: Theory of Probability II (Winter 2022).
  • STATS 310A: Theory of Probability I (Fall 2021).
  • STATS 300C: Theory of Statistics III (Spring 2021).
  • STATS 300C: Theory of Statistics II (Winter 2021).
  • STATS 300C: Theory of Statistics I (Fall 2020).
  • STATS 361: Causal Inference (Spring 2020).
  • EE 364A: Convex Optimization (Winter 2020).
  • STATS 322: Gaussian estimation: Sequence and wavelet models (Fall 2019).
  • STATS 310A: Theory of Probability I (Fall 2018).
  • STATS 101: Introduction to Data Science (Summer 2018).
  • STATS 116: Introduction to Probability (Fall 2017).