Aaditya Ramdas

Associate Professor (with tenure) Carnegie Mellon University (Stanford from Sep 1, 2026) Department of Statistics and Data Science Machine Learning Department 132H Baker Hall aramdas AT stanford FULLSTOP edu [https://web.stanford.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 (Stanford from Sep 1, 2026). 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), from whom he recently received a Young Alumnus Achiever Award (2026).

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. He was the program chair of AISTATS 2026, the general chair of AISTATS 2027, and has been a area chair at UAI, COLT, NeurIPS, ICML, ICLR, ALT, etc.

He has published over 150 peer-reviewed papers, about half 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 about half at 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 (publisher version)

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 moving permanently to Stanford in Fall 2026.

I will be the general chair (along with Arno Solin) of AISTATS 2027.

I received a Young Alumnus Achiever Award from IIT Bombay.

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 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 has been 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, which appeared in 2025.

I co-edited (with P. Grunwald) a special issue on Game-theoretic statistics and safe, anytime-valid inference, which appeared in 2024.

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)

• Strong duality for the GROW criterion (with A. Ram, M. Larsson, J. Ruf).       arXiv

• Distribution-free changepoint localization after sequential change detection (with A. Saha).       arXiv

• M-estimation with e-statistics (with H. Wang).       arXiv

• Distribution-free root cause analysis (with R. Hore).       arXiv

• Bentkus-type asymptotic e-values (with D. Martinez-Taboada, B. Chugg).       arXiv

• Optimal sequential tests yield log-optimal e-processes (with A. Ram).       arXiv

• The optimal betting wealth growth rate (with A. Ram).       arXiv

• Intrinsic-dimension empirical Bernstein inequalities for bounded self-adjoint operators (with D. Martinez-Taboada).       arXiv

• Cover meets Robbins while betting on bounded data: log-n regret and almost sure log-log-n regret (with S. Agrawal).       arXiv

• Optimal rates for differentially private hypothesis testing with e-values (with B. Jacobsen, T. Gonzalez, G. Brown, K. Fawaz).       arXiv

• E-values and sequential power-one tests for monotonicity and unimodality (with H. Wang).       arXiv

• Optimal e-values under constraints (with A. Saha).       arXiv

• E-values as statistical evidence: a comparison to Bayes factors, likelihoods and p-values (with B. Chugg, P. Grunwald).       arXiv

• Tiny but uniform improvements of adaptive BH procedures via compound e-values (with N. Ignatiadis, R. Wang).       arXiv

• Besag-Clifford e-values for unnormalized testing (with A. Dombowsky, B. Engelhardt).       arXiv

• Power one sequential tests exist for weakly compact \P against \P^c (with A. Ram).       arXiv

• Tighter confidence intervals under without replacement sampling via empirical rate functions (with S. Shekhar).       arXiv

• Post-hoc large-sample statistical inference (with B. Chugg, E. Gauthier, M. Jordan, I. Waudby-Smith).       arXiv

• Universal sequential changepoint detection of quantum observables via classical shadows (with M. Zecchin, O. Simeone).       arXiv

• Operationalizing Stein's method for online linear optimization: CLT-based optimal tradeoffs (with Z. Zhang).       arXiv

• Asymptotically optimal sequential change detection for bounded means (with A. Ram).       arXiv

• Intrinsic dimension concentration inequalities for self-adjoint operators (with D. Martinez-Taboada).       arXiv

• An asymptotic law of the iterated logarithm for KL-inf (with A. Ram).       arXiv

• Conformal changepoint localization (with R. Hore).       arXiv

• Improving the adjusted Benjamini--Hochberg method using e-values in knockoff-assisted variable selection (with A. Biswas).       arXiv

• A complete characterization of testable hypotheses (with M. Larsson, J. Ruf).       arXiv

• Closed-form empirical Bernstein confidence sequences for scalars and matrices (with B. Chugg).       arXiv

• Rao-Blackwellized e-variables (with D. de Roos, B. Chugg, P. Grunwald).       arXiv

• Optimal transportation and alignment between Gaussian measures (with S. Dandapanthula, A. Podkopaev, S. Kasiviswanathan, Z. Goldfeld).       arXiv

• Gradient flow for deep equilibrium single-index models (with S. Dandapanthula).       arXiv

• Theoretical guarantees for change localization using conformal p-values (with S. Bhattacharyya).       arXiv

• Adaptive off-policy inference for M-estimators under model misspecification (with J. Leiner, R. Dunn).       arXiv

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• Testing by betting while borrowing and bargaining (with H. Wang, M. Perez-Ortiz, W. Koolen).       arXiv

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

• Distribution-uniform anytime-valid sequential inference and the Robbins-Siegmund distributions (with I. Waudby-Smith, E. Kennedy).       arXiv

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

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

Published books

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

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. Distribution-uniform strong laws of large numbers (with I. Waudby-Smith, M. Larsson), Annals of Applied Prob., 2026.       arXiv | (Student Research Award, Stat Soc Canada)

2. More powerful multiple testing under dependence via randomization (with Z. Xu), Bernoulli, 2026.       arXiv

3. Almost sure null bankruptcy of testing-by-betting strategies (with H. Wang, S. Agrawal), Conference on Learning Theory (COLT), 2026.       arXiv

4. Sharp empirical Bernstein bounds for the variance of bounded random variables (with D. Martinez-Taboada), Intl. Conf. on Machine Learning (ICML), 2026.       arXiv

5. Online monotone density estimation and log-optimal calibration (with R. Hore, R. Wang), Conformal Prediction and Applications, 2026.       arXiv

6. Post-detection inference for sequential changepoint localization (with A. Saha), J Royal Statistical Society, Series B, 2026.       arXiv

7. Improving online FDR procedures via online analogs of e-closure and compound e-values (with N. Xu, L. Fischer), Uncertainty in AI (UAI), 2026.       arXiv

8. Conformal online model aggregation (with M. Gasparin), Uncertainty in AI (UAI), 2026.       arXiv

9. Nonasymptotic mean estimation in Banach spaces under infinite variance and martingale dependence (with J. Whitehouse, B. Chugg, D. Martinez-Taboada), Stochastic Processes and Applications, 2026.       arXiv | proc

10. Testing hypotheses generated by constraints (with M. Larsson, J. Ruf), Mathematics of Operations Research, 2026.       arXiv | proc

11. Multiple testing with anytime-valid Monte-Carlo p-values (with L. Fischer, T. Barry), Electronic J of Statistics, 2026.       arXiv | proc

12. Offline changepoint localization using a matrix of conformal p-values (with S. Dandapanthula), Transactions on ML Research, 2026.       arXiv | proc

13. Combining evidence across filtrations (with Y.J. Choe), J Royal Stat Sociey, Series B, 2026.       arXiv | code | slides

14. Improving the (approximate) sequential probability ratio test by avoiding overshoot (with L. Fischer), IEEE Transactions on Information Theory, 2026.       arXiv | proc

15. Huber-robust likelihood ratio tests for composite nulls and alternatives (with A. Saha), IEEE Transactions on Information Theory, 2026.       arXiv | proc

16. On admissibility in post-hoc hypothesis testing (with B. Chugg, T. Lardy, P. Grunwald), Intl J on Approximate Reasoning, 2026.       arXiv

17. Empirical Bernstein in smooth Banach spaces (with D. Martinez-Taboada), Annals of Applied Prob., 2026.       arXiv

18. Eventually LIL regret: almost sure ln-ln-T regret for a sub-Gaussian mixture on unbounded data (with S. Agrawal), Algorithmic Learning Theory, 2026.       arXiv

19. Vector-valued self-normalized concentration inequalities beyond sub-Gaussianity (with D. Martinez-Taboada, T. Gonzalez-Lara), Algorithmic Learning Theory, 2026.       arXiv

20. A martingale kernel two-sample test (with A. Chatterjee), Algorithmic Learning Theory, 2026.       arXiv (elegant paper award)

21. Positive semidefinite matrix supermartingales (with H. Wang), Elec. Journal of Prob., 2025.       arXiv | proc

22. Time-uniform self-normalized concentration for vector-valued processes (with J. Whitehouse, S. Wu), Annals of Applied Prob., 2025.       arXiv | proc

23. Sharp matrix Empirical Bernstein inequalities (with H. Wang), NeurIPS, 2025.       arXiv

24. Sequentially auditing differential privacy (with T. Gonzalez-Lara, M. Dulce-Rubio, M. Ribero), NeurIPS, 2025.       arXiv

25. Private evolution converges (with T. Gonzalez-Lara, G. Fanti), NeurIPS, 2025.       arXiv | video

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

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

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

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

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

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

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

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

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

35. 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

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

37. 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

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

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

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

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

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

43. 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

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

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

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

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

48. 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

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

50. 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

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

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

53. 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

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

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

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

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

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

59. 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

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

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

62. 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)

63. 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

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

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

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

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

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

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

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

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

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

73. 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

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

75. 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)

76. 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

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

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

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

80. 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)

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

82. 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

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

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

85. 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

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

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

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

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

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

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

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

93. 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) |

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

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

96. 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

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

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

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

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

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

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

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

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

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

106. 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)

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

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

109. 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

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

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

112. 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)

113. 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

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

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

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

117. 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)

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

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

120. 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)

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

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

123. 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

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

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

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

127. 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

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

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

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

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

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

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

134. 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

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

136. 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

137. 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

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

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

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

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

142. 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

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

144. 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

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

146. 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)

147. 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)

148. 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)

149. 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

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

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

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

153. 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

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

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

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

157. 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

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

159. 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

160. 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

161. 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

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

163. 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

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

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

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

167. 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

168. 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)

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

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

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

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

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

5. Discussion of "Poisson-FOCuS" (with A. Saha), JASA 2024.       proc

6. Discussion of "Safe Testing" (with M. Larsson, J. Ruf), JRSSB 2024.       proc

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

8. Discussion of "A note on universal inference" (with L. Wasserman, S. Balakrishnan), STAT 2023.       proc

9. Discussion of "Testing by betting", JRSSA 2021.       proc

10. Discussion of "Covariate-assisted ranking and screening for two-sample inference", JRSSB 2019.       proc

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

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

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