Aaditya Ramdas

Associate Professor (with tenure) Carnegie Mellon University Department of Statistics and Data Science (75%) Machine Learning Department (25%) 132H Baker Hall aramdas AT {empty or stat or cs} DOT cmu FULLSTOP edu [http://www.stat.cmu.edu/~aramdas]

Biography

Aaditya Ramdas is an Associate Professor (with tenure) at Carnegie Mellon University in the Department of Statistics and Data Science and the Machine Learning Department. 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, All India Rank 47).

His work has been recognized by the Presidential Early Career Award (PECASE), the highest distinction bestowed by the US government to young scientists. He has also received a Kavli fellowship from the National Academy of Sciences, a Sloan fellowship in Mathematics, the CAREER award from the National Science Foundation, the Emerging Leader Award from COPSS (Committee of Presidents of Statistical Societies), early career awards from the Bernoulli Society and the Institute of Mathematical Statistics, and faculty research awards from Adobe and Google. He was recently elected Fellow of the IMS, was awarded Statistician of the Year 2025 by the ASA's Pittsburgh Chapter, and is the program chair of AISTATS 2026.

He has published over 70 papers at top journals like The Annals of Statistics, Biometrika, IEEE Transactions on Information Theory and PNAS, including prestigious discussion papers at the Journal of the Royal Statistical Society and Journal of the American Statistical Association, and over 70 papers the top AI conferences like NeurIPS, ICML, ICLR, UAI and AISTATS, including over a dozen orals/spotlights. He has given several keynote talks, including at Lunteren, AISTATS and VCMF, and invited tutorials at CUSO, KDD and ICML.

Aaditya's research in mathematical statistics and learning has an eye towards designing algorithms that both have strong theoretical guarantees and also work well in practice. His main interests include post-selection inference (multiple testing, simultaneous inference), game-theoretic statistics (e-values, confidence sequences) and predictive uncertainty quantification (conformal prediction, calibration). His areas of applied interest include privacy, neuroscience, genetics and auditing (elections, real-estate, finance, fairness).

He co-organizes of the StatML Group at CMU. He loves to talk about backpacking adventures through over 70 countries, trash-free living, completing the Ironman triathlon, long-distance bicycle rides, books and parenthood.

Curriculum Vitae

The E-book (hypothesis testing with e-values)

Group

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

Advice for PhD students and assistant professors.

These keywords quickly get my attention:

• e-values and confidence sequences (supermartingales, testing by betting, sequential anytime-valid inference, optional stopping, peeking and p-hacking, change detection, game-theoretic statistics)

• conformal prediction and calibration (distribution-free inference, uncertainty quantification for black-box machine learning, covariate/label shift, beyond exchangeability)

• multiple testing and post-selection inference (false discovery rate, inference after model selection, online or interactive or bandit testing, post-hoc simultaneous inference)

• high-dimensional, nonparametric statistics and machine learning (kernel methods, minimax rates, dimension-agnostic inference, universal inference, differential privacy, optimization)

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.

News

I am the program chair (along with Arno Solin) of AISTATS 2026.

I was invited to give a 2.5hr tutorial at ICML 2025 on game-theoretic statistics (e-values, confidence sequences, safe anytime-valid inference).

I was elected to be an IMS Fellow.

I was received the Statistician of the Year award for 2025 by ASA's Pittsburgh Chapter.

I co-organized the third week-long workshop on game-theoretic statistics and sequential anytime-valid inference (SAVI) at BIRS Chennai (Jun 29 -- Jul 3, 2025).

The E-book will be published as the inaugural issue of the new book series Foundations and Trends in Statistics.

I received the PECASE award, the highest award given by the US government to early career scientists.

I received a Kavli Fellowship and represented the National Academy of Sciences of the US in a joint meeting with the Chinese Academy of Sciences (Nov'24).

I received a Sloan Fellowship in Mathematics.

I co-edited a special issue on Conformal Prediction, Probabilistic Calibration and Distribution-Free Uncertainty Quantification, to appear in early 2025.

I co-edited (with P. Grunwald) a special issue on Game-theoretic statistics and safe, anytime-valid inference, publishing date: May'24.

I like designing new classes: I recently taught a course on game-theoretic probability, statistics and learning to 25 PhD students. I've taught other new PhD courses on multiple testing, sequential analysis, statistical machine learning, the history of machine learning, etc. (see misc)

I recently taught a 6-hour tutorial on game-theoretic statistics at GeorgiaTech, this is the first video.

On this page, I maintain a list of companies that use our anytime-valid inference methods in publicly deployed software or products.

Preprints (under review or unsubmitted)

1. Quantum sequential universal hypothesis testing (with M. Zecchin, O. Simeone).       arXiv

2. A variational approach to dimension-free self-normalized concentration (with B. Chugg).       arXiv

3. On admissibility in post-hoc hypothesis testing (with B. Chugg, T. Lardy, P. Grunwald).       arXiv

4. Density estimation with atoms, and functional estimation for mixed discrete-continuous data (with A. Saha).       arXiv

5. Private evolution converges (with T. Gonzalez-Lara, G. Fanti).       arXiv

6. Sharp empirical Bernstein bounds for the variance of bounded random variables (with D. Martinez-Taboada).       arXiv

7. Conformal changepoint localization (with S. Dandapanthula, R. Hore).       arXiv

8. Conditional independence testing with a single realization of a multivariate nonstationary nonlinear time series (with M. Wieck-Sosa, M. Haddad).       arXiv

9. On stopping times of power-one sequential tests: tight lower and upper bounds (with S. Agrawal).       arXiv

10. Testing hypotheses generated by constraints (with M. Larsson, J. Ruf).       arXiv

11. Bringing closure to FDR control: a general principle for multiple testing (with Z. Xu, A. Solari, L. Fischer, R. de Heide, J. Goeman).       arXiv

12. Locally minimax optimal confidence sets for the best model (with I. Kim).       arXiv

13. Post-detection inference for sequential changepoint localization (with A. Saha).       arXiv

14. Active multiple testing with proxy p-values and e-values (with Z. Xu, C. Wang, K. Roeder, L. Wasserman).       arXiv

15. Multiple testing in multi-stream sequential change detection (with S. Dandapanthula).       arXiv

16. Mean estimation in Banach spaces under infinite variance and martingale dependence (with J. Whitehouse, B. Chugg, D. Martinez-Taboada).       arXiv

17. Sharp matrix Empirical Bernstein inequalities (with H. Wang).       arXiv

18. Improving the (approximate) sequential probability ratio test by avoiding overshoot (with L. Fischer).       arXiv

19. Asymptotic and compound e-values: multiple testing and empirical Bayes (with N. Ignatiadis, R. Wang) arXiv

20. Empirical Bernstein in smooth Banach spaces (with D. Martinez-Taboada).       arXiv

21. Huber-robust likelihood ratio tests for composite nulls and alternatives (with A. Saha).       arXiv

22. Practical maximally flexible sampling designs for experiments based on e-values (with A. Ly, U. Boehm, P. Grunwald, D. van Ravenzwaaij).       psyarXiv

23. Anytime-valid inference for double/debiased machine learning of causal parameters (with A. Dalal, P. Blobaum, S. Kasiviswanathan).       arXiv

24. Matrix concentration: order versus anti-order (with R. Malekian).       arXiv

25. An online generalization of the (e-)Benjamini-Hochberg procedure (with L. Fischer, Z. Xu).       arXiv

26. Admissible online closed testing must employ e-values (with L. Fischer).       arXiv

27. Multiple testing with anytime-valid Monte-Carlo p-values (with L. Fischer, T. Barry).       arXiv

28. Conformal online model aggregation (with M. Gasparin).       arXiv

29. Combining evidence across filtrations (with Y.J. Choe).       arXiv | code | slides

30. Distribution-uniform strong laws of large numbers (with I. Waudby-Smith, M. Larsson).       arXiv | (Student Research Award, Stat Soc Canada)

31. Nonasymptotic and distribution-uniform Komlos-Major-Tusnady approximation (with I. Waudby-Smith, M. Larsson).       arXiv

32. Positive semidefinite matrix supermartingales (with H. Wang).       arXiv

33. Testing by betting while borrowing and bargaining (with H. Wang).       arXiv

34. Merging uncertainty sets via majority vote (with M. Gasparin).       arXiv

35. Distribution-uniform anytime-valid inference (with I. Waudby-Smith).       arXiv

36. Time-uniform self-normalized concentration for vector-valued processes (with J. Whitehouse, S. Wu).       arXiv

37. On the near-optimality of betting confidence sets for bounded means (with S. Shekhar).       arXiv

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

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

Published books

Hypothesis testing with e-values (with R. Wang), Foundations and Trends in Statistics, 2026.       arXiv

Published (or accepted) papers

1. Online selective conformal prediction: errors and solutions (with Y. Sale), Transactions of ML Research (TMLR), 2025.       arXiv

2. Time-uniform confidence spheres for means of random vectors (with B. Chugg, H. Wang), Transactions of ML Research (TMLR), 2025.       arXiv | proc

3. Anytime-valid FDR control with the stopped e-BH procedure (with S. Dandapanthula, H. Wang), Statistics and Probability Letters, 2025.       arXiv | proc

4. Improving the statistical efficiency of cross-conformal prediction (with M. Gasparin), Intl. Conf. on Machine Learning (ICML), 2025.       arXiv

5. Optimistic algorithms for adaptive estimation of the average treatment effect (with O. Neopane, A. Singh), Intl. Conf. on Machine Learning (ICML), 2025.       arXiv

6. Sequential Monte-Carlo testing by betting (with L. Fischer), J of the Royal Statistical Society (JRSSB), 2025.       arXiv

7. Combining exchangeable p-values (with M. Gasparin, R. Wang), Proc. of the National Academy of Sciences (PNAS), 2025.       arXiv | proc

8. The numeraire e-variable and reverse information projection (with M. Larsson, J. Ruf), Annals of Statistics, 2025.       arXiv | proc

9. Sequential kernelized Stein discrepancy (with D. Martinez-Taboada), Intl. Conf. on AI and Stat. (AISTATS), 2025.       arXiv

10. Logarithmic Neyman regret for adaptive estimation of the average treatment effect (with O. Neopane, A. Singh), Intl. Conf. on AI and Stat. (AISTATS), 2025.       arXiv

11. Scalable causal structure learning via amortized conditional independence testing (with J. Leiner, B. Manzo, W. Tansey), CLEAR, 2025.       arXiv | code | poster (oral talk)

12. Conformalized interactive imitation learning: handling expert shift and intermittent feedback (with M. Zhao, R. Simmons, H. Admoni, A. Bajcsy), Intl. Conf. on Learning Representations (ICLR), 2025. arXiv | website

13. QA-Calibration of Language Model Confidence Scores (with P. Manggala, A. Mastakouri, E. Kirschbaum, S. Kasiviswanathan), Intl. Conf. on Learning Representations (ICLR), 2025. arXiv

14. Anytime-valid t-tests and confidence sequences for Gaussian means with unknown variance (with H. Wang), Sequential Analysis, 2025.       arXiv | proc

15. The extended Ville's inequality for nonintegrable nonnegative supermartingales (with H. Wang), Bernoulli, 2025.       arXiv | proc

16. Bias detection via signaling (with T. Lin, I. Shapira, Y. Chen, A. Procaccia), Conf. on Neural Information Processing Systems (NeurIPS), 2024.       arXiv | proc

17. On the existence of powerful p-values and e-values for composite hypotheses (with Z. Zhang, R. Wang), Annals of Stat., 2024.       arXiv | proc

18. Time-uniform central limit theory and asymptotic confidence sequences (with I. Waudby-Smith, D. Arbour, R. Sinha, E. H. Kennedy), Annals of Stat., 2024.       arXiv | code | proc

19. Randomized and exchangeable improvements of Markov's, Chebyshev's and Chernoff's inequalities (with T. Manole), Statistical Science, 2024.       arXiv

20. Post-selection inference for e-value based confidence intervals (with Z. Xu, R. Wang), Elec J. Stat., 2024.       arXiv | proc | talk | slides

21. Interactive identification of individuals with positive treatment effect while controlling false discoveries (with B. Duan, L. Wasserman), J. of Causal Inference, 2024.       arXiv | proc

22. Multiple testing under negative dependence (with Z. Chi, R. Wang), Bernoulli, 2024.       arXiv | proc

23. Total variation floodgate for variable importance inference in classification (with W. Wang, L. Janson, L. Lei), Intl. Conf. on Machine Learning (ICML), 2024.       arXiv | proc

24. Reducing sequential change detection to sequential estimation (with S. Shekhar), Intl. Conf. on Machine Learning (ICML), 2024.       arXiv | proc

25. Universal inference meets random projections: a scalable test for log-concavity (with R. Dunn, A. Gangrade, L. Wasserman), J Comp & Graphical Stat, 2024.       arXiv | code | proc

26. De Finetti's Theorem and related results for infinite weighted exchangeable sequences (with R. Barber, E. Candes, R. Tibshirani), Bernoulli, 2024       arXiv | proc

27. Semiparametric efficient inference in adaptive experiments (with T. Cook, A. Mishler), Conference on Causal Learning and Reasoning (CLeaR), 2024. (oral talk)       arXiv |

28. Anytime-valid off-policy inference for contextual bandits (with I. Waudby-Smith, L. Wu, N. Karampatziakis, P. Mineiro), ACM/IMS J of Data Science, 2024.       arXiv | proc

29. Testing exchangeability by pairwise betting (with A. Saha), Intl. Conf. on AI and Stat. (AISTATS), 2024. (oral talk)       arXiv | proc | poster

30. Graph fission and cross-validation (with J. Leiner), Intl. Conf. on AI and Stat. (AISTATS), 2024       arXiv | proc | poster

31. Online multiple testing with e-values (with Z. Xu), Intl. Conf. on AI and Stat. (AISTATS), 2024.       arXiv | proc

32. Deep anytime-valid hypothesis testing (with T. Pandeva, P. Forre, S. Shekhar), Intl. Conf. on AI and Stat. (AISTATS), 2024.       arXiv | proc

33. Differentially private conditional independence testing (with I. Kalemaj, S. Kasiviswanathan), Intl. Conf. on AI and Stat. (AISTATS), 2024.       arXiv | proc

34. E-detectors: a nonparametric framework for online changepoint detection (with J. Shin, A. Rinaldo), New England J of Stat. and Data Science, 2023.       arXiv | proc

35. A unified recipe for deriving (time-uniform) PAC-Bayes bounds (with B. Chugg, H. Wang), J of ML Research, 2023.       arXiv | proc

36. A permutation-free kernel independence test (with S. Shekhar, I. Kim), J of ML Research, 2023.       arXiv | code | proc

37. Data fission: splitting a single data point (with J. Leiner, B. Duan, L. Wasserman), J of American Stat Assoc, 2023 arXiv | proc | poster | slides | code | talk | (Discussion paper)

38. A composite generalization of Ville's martingale theorem using e-processes (with J. Ruf, M. Larsson, W. Koolen), Elec. J. of Prob., 2023 arXiv | proc

39. Online multiple hypothesis testing (with D. Robertson, J. Wason), Statistical Science, 2023 arXiv | proc

40. Nonparametric two-sample testing by betting (with S. Shekhar), IEEE Trans. on Info. Theory, 2023       arXiv | proc | code | slides

41. E-values as unnormalized weights in multiple testing (with N. Ignatiadis, R. Wang), Biometrika, 2023 arXiv | proc

42. Comparing sequential forecasters (with Y.J. Choe), Operations Research, 2023 arXiv | proc | code | talk | poster | slides (Citadel, Research Showcase Runner-up)

43. Game-theoretic statistics and safe anytime-valid inference (with P. Grunwald, V. Vovk, G. Shafer), Statistical Science, 2023 arXiv | proc

44. Adaptive privacy composition for accuracy-first mechanisms (with R. Rogers, G. Samorodnitsky, S. Wu), Conf. on Neural Information Processing Systems (NeurIPS), 2023 arXiv | proc

45. Sequential predictive two-sample and independence testing (with A. Podkopaev), Conf. on Neural Information Processing Systems (NeurIPS), 2023 arXiv | proc

46. Auditing fairness by betting (with B. Chugg, S. Cortes-Gomez, B. Wilder), Conf. on Neural Information Processing Systems (NeurIPS), 2023 arXiv | code | proc

47. Counterfactually comparing abstaining classifiers (with Y. J. Choe, A. Gangrade), Conf. on Neural Information Processing Systems (NeurIPS), 2023 arXiv | slides | proc

48. An efficient doubly-robust test for the kernel treatment effect (with D. Martinez-Taboada, E. Kennedy), Conf. on Neural Information Processing Systems (NeurIPS), 2023 arXiv | proc

49. On the sublinear regret of GP-UCB (with J. Whitehouse, S. Wu), Conf. on Neural Information Processing Systems (NeurIPS), 2023 arXiv

50. Martingale methods for sequential estimation of convex functionals and divergences (with T. Manole), IEEE Trans. on Information Theory, 2023 arXiv | proc | talk (Student Research Award, Stat Soc Canada)

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

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

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

54. Online Platt scaling with calibeating (with C. Gupta), Intl. Conf. on Machine Learning (ICML), 2023 arXiv | proc

55. 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)

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

57. 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 | proc

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

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

60. 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 | proc

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

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

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

64. Conformal prediction beyond exchangeability (with R. Barber, E. Candes, R. Tibshirani), Annals of Stat., 2023 arXiv | proc

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

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

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

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

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

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

71. 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 | proc

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

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

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

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

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

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

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

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

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

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

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

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

84. 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 | proc

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

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

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

88. 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 | proc | talk

89. Dynamic algorithms for online multiple testing (with Z. Xu), Conf. on Math. and Scientific Machine Learning, 2021 arXiv | proc | talk | slides | code

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

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

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

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

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

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

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

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

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

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

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

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

102. A unified framework for bandit multiple testing (with Z. Xu, R. Wang), Conf. on Neural Information Processing Systems (NeurIPS), 2020 arXiv | proc | talk | slides | code

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

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

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

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

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

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

109. 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 | proc | poster

110. A higher order Kolmogorov-Smirnov test (with V. Sadhanala, Y. Wang, R. Tibshirani), Intl. Conf. on AI and Stat. (AISTATS), 2019 arXiv | proc

111. 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 | proc

112. 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 | proc | code

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

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

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

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

117. 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 | proc

118. 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 | proc

119. 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 | proc | poster

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

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

122. 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 | proc | poster | talk (from 44:00) (full oral talk)

123. 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 | proc | code (spotlight talk)

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

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

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

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

128. 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 (ICLR), 2017 arXiv | proc | poster | code

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

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

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

132. 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 | proc

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

134. 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 Stat. (AISTATS), 2015 arXiv | proc | errata

135. 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 (AAAI), 2015 arXiv | proc | supp

136. 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 | proc | code

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

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

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

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

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

142. 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 | proc

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

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

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

Miscellaneous

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

2. Rejoinder for the discussion of “Data Fission” (with J. Leiner, B. Duan, L. Wasserman), JASA 2024.       proc

3. Discussion of “Poisson-FOCuS” (with A. Saha), JASA 2024.       proc

4. Discussion of ``Safe Testing'' (with M. Larsson, J. Ruf), JRSSB 2024.       proc

5. Rejoinder for the discussion of “Estimating means of bounded random variables by betting” (with I. Waudby-Smith), JRSSB 2024.       proc

6. Discussion of “A note on universal inference” (with L. Wasserman, S. Balakrishnan), STAT 2023.       proc

7. Discussion of “Testing by betting”, JRSSA 2021.       proc

8. Discussion of “Covariate-assisted ranking and screening for two-sample inference”, JRSSB 2019.       proc

9. Computational and statistical advances in testing and learning (PhD Thesis, CMU).       arXiv

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

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