Aaditya Ramdas

 

Aaditya Ramdas
Department of Statistics and Data Science
Machine Learning Department
Carnegie Mellon University


132H Baker Hall
aramdas AT cmu FULLSTOP edu
[http://www.stat.cmu.edu/~aramdas]

Selected publications

  • A theoretical treatment of conditional independence testing under model-X
    E. Katsevich, A. Ramdas       arxiv

  • Time-uniform, nonparametric, nonasymptotic confidence sequences
    S. Howard, A. Ramdas, J. Sekhon, J. McAuliffe       (Annals of Stat., minor revision)   arxiv   talk

  • Time-uniform Chernoff bounds via nonnegative supermartingales
    S. Howard, A. Ramdas, J. Sekhon, J. McAuliffe       Prob. Surveys, 2020   arxiv   proc

  • Universal inference
    L. Wasserman, A. Ramdas, S. Balakrishnan       PNAS, 2020   arxiv

  • Are sample means in multi-armed bandits positively or negatively biased?
    J. Shin, A. Ramdas, A. Rinaldo       NeurIPS, 2019   arxiv   poster   proc

  • A unified treatment of multiple testing with prior knowledge using the p-filter
    A. Ramdas, R. F. Barber, M. Wainwright, M. Jordan       Annals of Stat., 2019   arxiv   code   proc

  • Classification accuracy as a proxy for two sample testing
    I. Kim*, A. Ramdas*, A. Singh, L. Wasserman       Annals of Stat., 2020   arxiv

  • Nested conformal prediction and quantile out-of-bag ensemble methods
    C. Gupta, A. Kuchibhotla, A. Ramdas       arxiv   code

  • Predictive inference with the jackknife+
    R. Barber, E. Candes, A. Ramdas, R. Tibshirani       Annals of Stat., 2020   arxiv   code

  • Simultaneous high-probability bounds on the FDP in structured, regression and online settings
    E. Katsevich, A. Ramdas       Annals of Stat., 2020   arxiv   code

  • Sequential estimation of quantiles with applications to A/B-testing and best-arm identification
    S. Howard, A. Ramdas       (Bernoulli, major revision)   arxiv   code

Biography

Aaditya Ramdas is an assistant professor in the Departments of Statistics and Machine Learning at Carnegie Mellon University.

He is one of the organizers of the amazing and diverse StatML Group at CMU.

These days, he has 3 major directions of research:
1. selective and simultaneous inference (interactive, structured, post-hoc control of false discovery/coverage rate,…),
2. sequential uncertainty quantification (confidence sequences, always-valid p-values, bias in bandits,…), and
3. assumption-free black-box predictive inference (conformal prediction, calibration,…).

Outside of work, here are three easy topics for conversation: travel/outdoors (hiking, scuba, etc.), trash-free living, Ironman triathlon.

Curriculum Vitae