Aaditya Ramdas

 

Aaditya Ramdas (PhD, 2015)
Assistant Professor
Department of Statistics and Data Science (75%)
Machine Learning Department (25%)
Carnegie Mellon University

Visiting academic, Amazon (20%).

132H Baker Hall
aramdas AT {empty or stat or cs} DOT cmu FULLSTOP edu
[http://www.stat.cmu.edu/~aramdas]

These keywords quickly get my attention:

I work on “practical theory”, meaning that the vast majority of my papers are about designing theoretically principled algorithms that directly solve practical problems, and are usually based on simple, aesthetically elegant (in my opinion) ideas. A theoretician's goal is not to prove theorems, just as a writer's goal is not to write sentences. My goals are to improve my own (and eventually the field's) understanding of important problems, design creative algorithms for unsolved questions and figure out when and why they work (or don't), and often simply to ask an intriguing question that has not yet been asked.

Group

Courses, Workshops, Tutorials, Software, Talks, etc.

Biography

Aaditya Ramdas (PhD, 2015) is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. He was a postdoc at UC Berkeley (2015–2018) mentored by Michael Jordan and Martin Wainwright, and obtained his PhD at CMU (2010–2015) under Aarti Singh and Larry Wasserman, receiving the Umesh K. Gavaskar Memorial Thesis Award. His undergraduate degree was in Computer Science from IIT Bombay (2005-09).

Aaditya received the IMS Peter Gavin Hall Early Career Prize (2023), was an inaugural recipient of the COPSS Emerging Leader Award (2021), and a recipient of the Bernoulli New Researcher Award (2021). His work is supported by an NSF CAREER Award, an Adobe Faculty Research Award (2019), a Google Research Scholar award (2022), amongst others. He was a CUSO lecturer in 2022 and will be a Lunteren lecturer in 2023.

Aaditya's research is in mathematical statistics and learning, with an eye towards designing algorithms that both have strong theoretical guarantees and also work well in practice. His main research interests include selective and simultaneous inference (interactive, structured, online, post-hoc control of false decision rates, etc), game-theoretic statistics (sequential uncertainty quantification, confidence sequences, always-valid p-values, safe anytime-valid inference, e-processes, supermartingales, etc), and distribution-free black-box predictive inference (conformal prediction, post-hoc calibration, etc). His areas of applied interest include privacy, neuroscience, genetics and auditing (elections, real-estate, financial, fairness), and his group's work has received multiple best paper awards.

He is one of the organizers of the amazing and diverse StatML Group at CMU. Outside of work, some easy topics for conversation include travel/outdoors (hiking, scuba, etc.), trash-free living, completing the Ironman triathlon and long-distance bicycle rides.

Curriculum Vitae

Preprints (under review or revision)

  1. More powerful multiple testing under dependence via randomization (with Z. Xu).       arXiv

  2. Sequential predictive two-sample and independence testing (with A. Podkopaev).       arXiv

  3. Randomized and exchangeable improvements of Markov's, Chebyshev's and Chernoff's inequalities (with T. Manole).       arXiv

  4. The extended Ville's inequality for nonintegrable nonnegative supermartingales (with H. Wang).       arXiv

  5. A unified recipe for deriving (time-uniform) PAC-Bayes bounds (with B. Chugg, H. Wang).       arXiv

  6. A sequential test for log-concavity (with A. Gangrade, A. Rinaldo).       arXiv

  7. Game-theoretic statistics and safe anytime-valid inference (with P. Grunwald, V. Vovk, G. Shafer).       arXiv

  8. Anytime-valid off-policy inference for contextual bandits (with I. Waudby-Smith, L. Wu, N. Karampatziakis, P. Mineiro).       arXiv

  9. A composite generalization of Ville's martingale theorem (with J. Ruf, M. Larsson, W. Koolen).       arXiv

  10. Nonparametric two-sample testing by betting (with S. Shekhar).       arXiv | code | slides

  11. E-detectors: a nonparametric framework for online changepoint detection (with J. Shin, A. Rinaldo).       arXiv  

  12. Comparing sequential forecasters (with Y.J. Choe).       arXiv | code | talk | poster | slides (Citadel, Research Showcase Runner-up)

  13. Admissible anytime-valid sequential inference must rely on nonnegative martingales (with J. Ruf, M. Larsson, W. Koolen).       arXiv

  14. Time-uniform central limit theory and asymptotic confidence sequences (with I. Waudby-Smith, D. Arbour, R. Sinha, E. H. Kennedy).       arXiv | code

  15. Post-selection inference for e-value based confidence intervals (with Z. Xu, R. Wang).       arXiv

  16. Online multiple hypothesis testing for reproducible research (with D. Robertson, J. Wason).       arXiv

  17. Data fission: splitting a single data point (with J. Leiner, B. Duan, L. Wasserman).       arXiv

  18. Interactive identification of individuals with positive treatment effect while controlling false discoveries (with B. Duan, L. Wasserman).       arXiv

  19. Multiple testing under negative dependence (with Z. Chi, R. Wang).       arXiv

  20. E-values as unnormalized weights in multiple testing (with N. Ignatiadis, R. Wang).       arXiv

  21. An efficient doubly-robust test for the kernel treatment effect (with D. Martinez-Taboada, E. H. Kennedy).       arXiv

  22. A permutation-free kernel independence test (with S. Shekhar, I. Kim).       arXiv | code

  23. Universal inference meets random projections: a scalable test for log-concavity (with R. Dunn, A. Gangrade, L. Wasserman).       arXiv | code

  24. De Finettiā€™s Theorem and related results for infinite weighted exchangeable sequences (with R. Barber, E. Candes, R. Tibshirani).       arXiv

  25. When do exact and powerful p-values and e-values exist? (with Z. Zhang, R. Wang).       arXiv

  26. Auditing fairness by betting (with B. Chugg, S Cortes-Gomez, B. Wilder).       arXiv | code

  27. Counterfactually comparing abstaining classifiers (with Y. J. Choe, A. Gangrade).       arXiv

Published (or accepted) papers

About half the list below are journal papers, and the other half are full-length peer-reviewed papers with proceedings in top-tier venues in AI/ML, where conference publications are the norm.
  1. Martingale methods for sequential estimation of convex functionals and divergences (with T. Manole), IEEE Trans. on Information Theory, 2023 arXiv | article | talk (Student Research Award, Stat Soc Canada)

  2. Estimating means of bounded random variables by betting (with I. Waudby-Smith), J. of the Royal Statistical Society, Series B, 2023 arXiv (Discussion paper) | code

  3. Sequential change detection via backward confidence sequences (with S. Shekhar). Intl. Conf. on Machine Learning (ICML), 2023   arXiv | code | slides

  4. Fully adaptive composition in differential privacy (with J. Whitehouse, R. Rogers, Z. S. Wu), Intl. Conf. on Machine Learning (ICML), 2023 arXiv

  5. Online Platt scaling with calibeating (with C. Gupta), Intl. Conf. on Machine Learning, 2023 arXiv

  6. A nonparametric extension of randomized response for locally private confidence sets (with I. Waudby-Smith, Z. S. Wu), Intl. Conf. on Machine Learning (ICML), 2023 arXiv | code (oral talk)

  7. Sequential kernelized independence testing (with A. Podkopaev, P. Bloebaum, S. Kasiviswanathan), Intl. Conf. on Machine Learning (ICML), 2023 arXiv

  8. Risk-limiting financial audits via weighted sampling without replacement (with S. Shekhar, Z. Xu, Z. Lipton, P. Liang), Intl. Conf. Uncertainty in AI (UAI), 2023 arXiv

  9. Huber-robust confidence sequences (with H. Wang), Intl. Conf. on AI and Statistics (AISTATS), 2023, arXiv (full oral talk)

  10. Catoni-style confidence sequences for heavy-tailed mean estimation (with H. Wang), Stochastic Processes and Applications, 2023 arXiv | code

  11. Anytime-valid confidence sequences in an enterprise A/B testing platform (with A. Maharaj, R. Sinha, D. Arbour, I. Waudby-Smith, S. Liu, M. Sinha, R. Addanki, M. Garg, V. Swaminathan), ACM Web Conference (WWW), 2023 arXiv

  12. Dimension-agnostic inference using cross U-statistics (with I. Kim), Bernoulli, 2023 arXiv

  13. On the power of conditional independence testing under model-X (with E. Katsevich), Electronic J. Stat, 2023 arXiv | article

  14. Permutation tests using arbitrary permutation distributions (with R. Barber, E. Candes, R. Tibshirani), Sankhya A, 2023 arXiv | article

  15. Conformal prediction beyond exchangeability (with R. Barber, A. Ramdas, R. Tibshirani), Annals of Stat., 2023 arXiv | article

  16. Faster online calibration without randomization: interval forecasts and the power of two choices (with C. Gupta), Conf. on Learning Theory (COLT), 2022 arXiv | article

  17. Top-label calibration and multiclass-to-binary reductions (with C. Gupta), Intl. Conf. on Learning Representations, 2022 arXiv | article

  18. Gaussian universal likelihood ratio testing (with R. Dunn, S. Balakrishnan, L. Wasserman), Biometrika, 2022 arXiv | article

  19. A permutation-free kernel two sample test (with S. Shekhar, I. Kim), Conf. on Neural Information Processing Systems (NeurIPS), 2022 arXiv | article | code (oral talk)"

  20. Testing exchangeability: fork-convexity, supermartingales, and e-processes (with J. Ruf, M. Larsson, W. Koolen). Intl J. of Approximate Reasoning, 2022 arXiv | article

  21. Tracking the risk of a deployed model and detecting harmful distribution shifts (with A. Podkopaev). Intl. Conf. on Learning Representations (ICLR), 2022 arXiv | article

  22. Brownian noise reduction: maximizing privacy subject to accuracy constraints (with J. Whitehouse, Z.S. Wu, R. Rogers), Conf. on Neural Information Processing Systems (NeurIPS), 2022 arXiv | article

  23. Sequential estimation of quantiles with applications to A/B-testing and best-arm identification (with S. Howard), Bernoulli, 2022 arXiv | article | code

  24. Brainprints: identifying individuals from magnetoencephalograms (with S. Wu, L. Wehbe), Nature Communications Biology, 2022 bioRxiv | article

  25. Interactive rank testing by betting (with B. Duan, L. Wasserman), Conf. on Causal Learning and Reasoning (CLEAR), 2022 arXiv | article

  26. Large-scale simultaneous inference under dependence (with J. Tian, X. Chen, E. Katsevich, J. Goeman), Scandanavian J of Stat., 2022 arXiv | article

  27. False discovery rate control with e-values (with R. Wang), J. of the Royal Stat. Soc., Series B, 2022 arXiv | article

  28. Nested conformal prediction and quantile out-of-bag ensemble methods (with C. Gupta, A. Kuchibhotla), Pattern Recognition, 2022 arXiv | article | code

  29. Distribution-free prediction sets for two-layer hierarchical models (with R. Dunn, L. Wasserman), J of American Stat. Assoc., 2022 arXiv | article | code

  30. Fast and powerful conditional randomization testing via distillation (with M. Liu, E. Katsevich, L. Janson), Biometrika, 2021 arXiv | article | code

  31. Uncertainty quantification using martingales for misspecified Gaussian processes (with W. Neiswanger), Algorithmic Learning Theory (ALT), 2021 arXiv | article | code | talk

  32. RiLACS: Risk-limiting audits via confidence sequences (with I. Waudby-Smith, P. Stark), Intl. Conf. for Electronic Voting (EVoteID), 2021 arXiv | article | code (Best Paper award)

  33. Predictive inference with the jackknife+ (with R. Barber, E. Candes, R. Tibshirani), Annals of Stat., 2021 arXiv | article | code

  34. Path length bounds for gradient descent and flow (with C. Gupta, S. Balakrishnan), J. of Machine Learning Research, 2021 arXiv | article | blog

  35. Nonparametric iterated-logarithm extensions of the sequential generalized likelihood ratio test (with J. Shin, A. Rinaldo), IEEE J. on Selected Areas in Info. Theory, 2021 arXiv | article

  36. Time-uniform, nonparametric, nonasymptotic confidence sequences (with S. Howard, J. Sekhon, J. McAuliffe), The Annals of Stat., 2021 arXiv | article | code | tutorial

  37. Off-policy confidence sequences (with N. Karampatziakis, P. Mineiro), Intl. Conf. on Machine Learning (ICML), 2021 arXiv | article

  38. Best arm identification under additive transfer bandits (with O. Neopane, A. Singh), Asilomar Conf. on Signals, Systems and Computers, 2021 arXiv | article (Best Student Paper award)

  39. On the bias, risk and consistency of sample means in multi-armed bandits (with J. Shin, A. Rinaldo), SIAM J. on the Math. of Data Science, 2021 arXiv | article | talk

  40. Dynamic algorithms for online multiple testing (with Z. Xu), Conf. on Math. and Scientific Machine Learning, 2021 arXiv | article

  41. Online control of the familywise error rate (with J. Tian), Statistical Methods in Medical Research, 2021 arXiv | article

  42. Asynchronous online testing of multiple hypotheses (with T. Zrnic, M. Jordan), J. of Machine Learning Research, 2021 arXiv | article | code | blog

  43. Classification accuracy as a proxy for two sample testing (with I. Kim, A. Singh, L. Wasserman), Annals of Stat., 2021 arXiv | article (JSM Stat Learning Student Paper Award)

  44. Distribution-free calibration guarantees for histogram binning without sample splitting (with C. Gupta), Intl. Conf. on Machine Learning, 2021 arXiv | article

  45. Distribution-free uncertainty quantification for classification under label shift (with A. Podkopaev), Conf. on Uncertainty in AI, 2021 arXiv | article

  46. Distribution-free binary classification: prediction sets, confidence intervals and calibration (with C. Gupta, A. Podkopaev), Conf. on Neural Information Processing Systems (NeurIPS), 2020 arXiv | article (spotlight talk)

  47. The limits of distribution-free conditional predictive inference (with R. Barber, E. Candes, R. Tibshirani), Information and Inference, 2020 arXiv | article

  48. Analyzing student strategies in blended courses using clickstream data (with N. Akpinar, U. Acar), Educational Data Mining, 2020 arXiv | article | talk (oral talk)

  49. The power of batching in multiple hypothesis testing (with T. Zrnic, D. Jiang, M. Jordan), Intl. Conf. on AI and Statistics, 2020 arXiv | article | talk

  50. Online control of the false coverage rate and false sign rate (with A. Weinstein), Intl. Conf. on Machine Learning (ICML), 2020 arXiv | article

  51. Confidence sequences for sampling without replacement (with I. Waudby-Smith), Conf. on Neural Information Processing Systems (NeurIPS), 2020 arXiv | article | code (spotlight talk)

  52. Universal inference (with L. Wasserman, S. Balakrishnan), Proc. of the National Academy of Sciences, 2020 arXiv | article | talk

  53. A unified framework for bandit multiple testing (with Z. Xu, R. Wang), Conf. on Neural Information Processing Systems (NeurIPS), 2020 arXiv | article

  54. Simultaneous high-probability bounds on the FDP in structured, regression and online settings (with E. Katsevich), Annals of Stat., 2020 arXiv | article | code

  55. Time-uniform Chernoff bounds via nonnegative supermartingales (with S. Howard, J. Sekhon, J. McAuliffe), Prob. Surveys, 2020 arXiv | article | talk

  56. STAR: A general interactive framework for FDR control under structural constraints (with L. Lei, W. Fithian), Biometrika, 2020 arXiv | article | poster | code

  57. Familywise error rate control by interactive unmasking (with B. Duan, L. Wasserman), Intl. Conf. on Machine Learning (ICML), 2020 arXiv | article | code

  58. Interactive martingale tests for the global null (with B. Duan, S. Balakrishnan, L. Wasserman), Electronic J. of Stat., 2020 arXiv | article | code

  59. On conditional versus marginal bias in multi-armed bandits (with J. Shin, A. Rinaldo), Intl. Conf. on Machine Learning (ICML), 2020 arXiv | article

  60. Are sample means in multi-armed bandits positively or negatively biased? (with J. Shin, A. Rinaldo), Conf. on Neural Information Processing Systems (NeurIPS), 2019 arXiv | article | poster

  61. A higher order Kolmogorov-Smirnov test (with V. Sadhanala, Y. Wang, R. Tibshirani), Intl. Conf. on AI and Statistics, 2019 arXiv | article

  62. ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls (with J. Tian), Conf. on Neural Information Processing Systems (NeurIPS), 2019 arXiv | code | article

  63. A unified treatment of multiple testing with prior knowledge using the p-filter (with R. F. Barber, M. Wainwright, M. Jordan), Annals of Stat., 2019 arXiv | article | code

  64. DAGGER: A sequential algorithm for FDR control on DAGs (with J. Chen, M. Wainwright, M. Jordan), Biometrika, 2019 arXiv | article | code

  65. Conformal prediction under covariate shift (with R. Tibshirani, R. Barber, E. Candes), Conf. on Neural Information Processing Systems (NeurIPS), 2019 arXiv | article | poster

  66. Optimal rates and tradeoffs in multiple testing (with M. Rabinovich, M. Wainwright, M. Jordan), Statistica Sinica, 2019 arXiv | article | poster

  67. Function-specific mixing times and concentration away from equilibrium (with M. Rabinovich, M. Wainwright, M. Jordan), Bayesian Analysis, 2019 arXiv | article | poster

  68. Decoding from pooled data (II): sharp information-theoretic bounds (with A. El-Alaoui, F. Krzakala, L. Zdeborova, M. Jordan), SIAM J. on Math. of Data Science, 2019 arXiv | article

  69. Decoding from pooled data (I): phase transitions of message passing (with A. El-Alaoui, A. Ramdas, F. Krzakala, L. Zdeborova, M. Jordan), IEEE Trans. on Info. Theory, 2018 arXiv | article

  70. On the power of online thinning in reducing discrepancy (with R. Dwivedi, O. N. Feldheim, Ori Gurel-Gurevich), Prob. Theory and Related Fields, 2018 arXiv | article | poster

  71. On kernel methods for covariates that are rankings (with H. Mania, M. Wainwright, M. Jordan, B. Recht), Electronic J. of Stat., 2018 arXiv | article

  72. SAFFRON: an adaptive algorithm for online FDR control (with T. Zrnic, M. Wainwright, M. Jordan), Intl. Conf. on Machine Learning (ICML), 2018 arXiv | article | code (full oral talk)

  73. Online control of the false discovery rate with decaying memory (with F. Yang, M. Wainwright, M. Jordan), Conf. on Neural Information Processing Systems (NeurIPS), 2017 arXiv | article | poster | talk (from 44:00) (full oral talk)

  74. MAB-FDR: Multi (A)rmed\/(B)andit testing with online FDR control (with F. Yang, K. Jamieson, M. Wainwright), Conf. on Neural Information Processing Systems (NeurIPS), 2017 arXiv | article | code (spotlight talk)

  75. QuTE: decentralized FDR control on sensor networks (with J. Chen, M. Wainwright, M. Jordan), IEEE Conf. on Decision and Control, 2017 arXiv | article | code | poster

  76. Iterative methods for solving factorized linear systems (with A. Ma, D. Needell), SIAM J. on Matrix Analysis and Applications, 2017 arXiv | article

  77. Rows vs. columns : randomized Kaczmarz or Gauss-Seidel for ridge regression (with A. Hefny, D. Needell), SIAM J. on Scientific Computing, 2017 arXiv | article

  78. On Wasserstein two sample testing and related families of nonparametric tests (with N. Garcia, M. Cuturi), Entropy, 2017 arXiv | article

  79. Generative models and model criticism via optimized maximum mean discrepancy (with D. Sutherland, H. Tung, H. Strathmann, S. De, A. Smola, A. Gretton), Intl. Conf. on Learning Representations, 2017 arXiv | article | poster | code

  80. Minimax lower bounds for linear independence testing (with D. Isenberg, A. Singh, L. Wasserman), IEEE Intl. Symp. on Information Theory, 2016 arXiv | article

  81. p-filter: multi-layer FDR control for grouped hypotheses (with COAUTHORS), J. of the Royal Stat. Society, Series B, 2016 arXiv | article |code | poster

  82. Sequential nonparametric testing with the law of the iterated logarithm (with A. Balsubramani), Conf. on Uncertainty in AI, 2016 arXiv | article | errata

  83. Asymptotic behavior of Lq-based Laplacian regularization in semi-supervised learning (with A. El-Alaoui, X. Cheng, M. Wainwright, M. Jordan), Conf. on Learning Theory, 2016 arXiv | article

  84. Regularized brain reading with shrinkage and smoothing (with L. Wehbe, R. Steorts, C. Shalizi), Annals of Applied Stat., 2015 arXiv | article

  85. On the high-dimensional power of a linear-time two sample test under mean-shift alternatives (with S. Reddi, A. Singh, B. Poczos, L. Wasserman), Intl. Conf. on AI and Statistics, 2015 arXiv | article | errata

  86. On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions (with S. Reddi\*, B. Poczos, A. Singh, L. Wasserman), AAAI Conf. on Artificial Intelligence, 2015 arXiv | article | supp

  87. Fast two-sample testing with analytic representations of probability measures (with K. Chwialkowski, D. Sejdinovic, A. Gretton), Conf. on Neural Information Processing Systems (NeurIPS), 2015 arXiv | article | code

  88. Nonparametric independence testing for small sample sizes (with L. Wehbe), Intl. Joint Conf. on AI, 2015 arXiv | article (oral talk)

  89. Convergence properties of the randomized extended Gauss-Seidel and Kaczmarz methods (with A. Ma, D. Needell), SIAM J. on Matrix Analysis and Applications, 2015 arXiv | article | code

  90. Fast & flexible ADMM algorithms for trend filtering (with R. Tibshirani), J. of Computational and Graphical Statistics, 2015 arXiv | article | talk | code

  91. Towards a deeper geometric, analytic and algorithmic understanding of margins (with J. Pena), Opt. Methods and Software, 2015 arXiv | article

  92. Margins, kernels and non-linear smoothed perceptrons (with J. Pena), Intl. Conf. on Machine Learning (ICML), 2014 arXiv | article | poster | talk oral talk

  93. Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses (with L. Wehbe, B. Murphy, P. Talukdar, A. Fyshe, T. Mitchell), PLoS ONE, 2014 website | article

  94. An analysis of active learning with uniform feature noise (with A. Singh, L. Wasserman, B. Poczos), Intl. Conf. on AI and Statistics, 2014 arXiv | article | poster | talk (oral talk)

  95. Algorithmic connections between active learning and stochastic convex optimization (with A. Singh), Conf. on Algorithmic Learning Theory (ALT), 2013 arXiv | article | poster

  96. Optimal rates for stochastic convex optimization under Tsybakov's noise condition (with A. Singh), Intl. Conf. on Machine Learning (ICML), 2013 arXiv | article | poster | talk (oral talk)

Miscellaneous

  1. Adaptivity & computation-statistics tradeoffs for kernel & distance based high-dimensional two sample testing (with S. Reddi, B. Poczos, A. Singh, L. Wasserman).       arXiv | poster

  2. Algorithms for graph similarity and subgraph matching (with D. Koutra, A. Parikh, J. Xiang).       report