Bio. I am a
fifthyear 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):142, 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).