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I am now at Bloomberg AI in New York! Please reach out if you’d like to know more about what we do.

Previously, I spent five wonderful years at Carnegie Mellon University, where I obtained a PhD in Machine Learning under the guidance of Aaditya Ramdas. My research was awarded the Bloomberg Data Science Fellowship. My PhD dissertation, titled “Post-hoc calibration without distributional assumptions”, can be found here. Earlier, I was a Research Fellow at Microsoft Research, India with Prateek Jain. I did my undergrad (B. Tech) in Computer Science at IIT Kanpur.

Publications and preprints

(in reverse chronological order of first preprint)

Parity Calibration
Youngseog Chung, Aaron Rumack, Chirag Gupta
UAI 2023 (oral). [arxiv]
Online Platt Scaling with Calibeating
Chirag Gupta, Aaditya Ramdas
ICML 2023. [arxiv]
Faster online calibration without randomization: interval forecasts and the power of two choices
Chirag Gupta, Aaditya Ramdas
COLT 2022. [proc] [arxiv]
Top-label calibration and multiclass-to-binary reductions
Chirag Gupta, Aaditya Ramdas
ICLR 2022. [proc] [arxiv] [code]
Distribution-free calibration guarantees for histogram binning without sample splitting
Chirag Gupta, Aaditya Ramdas
ICML 2021. [arxiv] [proc] [code] [talk (from 37’ to 50’)]
Distribution-free binary classification: prediction sets, confidence intervals and calibration
Chirag Gupta*, Aleksandr Podkopaev*, Aaditya Ramdas
Neurips 2020 (spotlight). [arxiv] [proc] [talk (from 24’ to 38’)]
Nested conformal prediction and quantile out-of-bag ensemble methods
Chirag Gupta, Arun K Kuchibhotla, Aaditya Ramdas
Pattern Recognition (Special Issue on Conformal Prediction) 2022. [journal] [arxiv] [code] [talk (from 3’ to 24’)]
Path length bounds for gradient descent and flow
Chirag Gupta, Sivaraman Balakrishnan, Aaditya Ramdas
Journal of Machine Learning Research (JMLR) 2021. [journal] [arxiv] [blog]
Support recovery for orthogonal matching pursuit: upper and lower bounds
Raghav Somani*, Chirag Gupta*, Prateek Jain, Praneeth Netrapalli
Neurips 2018 (spotlight). [proc]
Protonn: Compressed and accurate knn for resource-scarce devices
Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, Prateek Jain
ICML 2017. [proc] [code]

Invited talks

Provably calibrating ML classifiers without distributional assumptions (TrustML Young Scientist Seminar, September 2022) [link]
Recent advances in distribution-free uncertainty quantification (International Seminar on Selective Inference, May 2021) [link]

Academic service

Reviewer: ICML 2020, ALT 2020, Neurips 2021, ICML 2022, JMLR
TA: Mathematical and Computational Foundations for ML (CMU), Machine Learning, Ethics, and Society (CMU)