Aaditya Ramdas

 

Associate Professor
Carnegie Mellon University

Department of Statistics and Data Science (75%)
Machine Learning Department (25%)

Visiting academic, Amazon (20%).

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

Biography

Aaditya Ramdas (PhD, 2015) is an Associate 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 Sloan fellowship in mathematics (2024), the IMS Peter Gavin Hall Early Career Prize (2023), the inaugural COPSS Emerging Leader Award (2021), and the Bernoulli New Researcher Award (2021). His work is supported by an NSF CAREER Award (2020), an Adobe Faculty Research Award (2019) and a Google Research Scholar award (2022). He was a CUSO lecturer in 2022, a Lunteren lecturer in 2023, and a keynote speaker at AISTATS 2024.

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, financial, fairness), and his group's work has received multiple paper awards, including discussion papers at JASA and JRSSB.

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, trash-free living, completing the Ironman triathlon and long-distance bicycle rides. Here are some of the books I've read recently.

Curriculum Vitae

Group

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

Advice for PhD students and assistant professors.

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.

News

I'm representing the National Academy of Sciences of the US in a joint meeting with the National Academy of Sciences of China (Nov'24, co-sponsored by the Kavli Foundation).

I'm co-editing a special issue on Conformal Prediction, Probabilistic Calibration and Distribution-Free Uncertainty Quantification (first round reviews complete).

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

I am co-organizing a week-long workshop on game-theoretic statistics and sequential anytime-valid inference at BIRS Chennai (Jun 29 -- Jul 3, 2025). I previously co-organized a week-long workshop on game-theoretic statistics and sequential anytime-valid inference at Oberwolfach (May'24), and an earlier week-long workshop at EURANDOM. I've previously co-organized day-long workshops on conformal prediction and calibration: DFUQ'22 and DFUQ'21, amongst others.

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. I also taught a 15-hour mini-course on the topic at Columbia University.

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. Conformalized interactive imitation learning: handling expert shift and intermittent feedback (with M. Zhao, R. Simmons, H. Admoni, A. Bajcsy) arXiv | website

  2. β-calibration of Language Model Confidence Scores for Generative QA (with P. Manggala, A. Mastakouri, E. Kirschbaum, S. Kasiviswanathan) arXiv

  3. Compound e-values and Empirical Bayes (with N. Ignatiadis, R. Wang) arXiv

  4. Sequential Kernelized Stein Discrepancy (with D. Taboada).       arXiv | TLDR

  5. Empirical Bernstein in smooth Banach spaces (with D. Taboada).       arXiv | TLDR

  6. Robust likelihood ratio tests for composite nulls and alternatives (with A. Saha).       arXiv | TLDR

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

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

  9. Matrix concentration: order versus anti-order (with R. Malekian).       arXiv | TLDR

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

  11. Online closed testing with e-values (with L. Fischer).       arXiv | TLDR

  12. Multiple testing with anytime-valid Monte-Carlo p-values (with L. Fischer).       arXiv | TLDR

  13. Combining exchangeable p-values (with M. Gasparin, R. Wang).       arXiv | TLDR

  14. Conformal online model aggregation (with M. Gasparin).       arXiv | TLDR

  15. The numeraire e-variable and reverse information projection (with M. Larsson, J. Ruf).       arXiv | TLDR

  16. Combining evidence across filtrations using adjusters (with Y.J. Choe).       arXiv | TLDR | code | slides

  17. Distribution-uniform strong laws of large numbers (with I. Waudby-Smith, M. Larsson).       arXiv | TLDR

  18. Positive semidefinite supermartingales and randomized matrix concentration inequalities (with H. Wang).       arXiv | TLDR

  19. Testing by betting while borrowing and bargaining (with H. Wang).       arXiv | TLDR

  20. Merging uncertainty sets via majority vote (with M. Gasparin).       arXiv | TLDR

  21. Sequential Monte-Carlo testing by betting (with L. Fischer).       arXiv | TLDR

  22. Time-uniform confidence spheres for means of random vectors (with B. Chugg, H. Wang).       arXiv | TLDR

  23. Distribution-uniform anytime-valid inference (with I. Waudby-Smith).       arXiv | TLDR

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

  25. Anytime-valid t-tests and confidence sequences for Gaussian means with unknown variance (with H. Wang).       arXiv | TLDR

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

  27. Scalable causal structure learning via amortized conditional independence testing (with J. Leiner, B. Manzo, W. Tansey).       arXiv | code | TLDR

  28. More powerful multiple testing under dependence via randomization (with Z. Xu).       arXiv | TLDR

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

  30. Admissible anytime-valid sequential inference must rely on nonnegative martingales (with J. Ruf, M. Larsson, W. Koolen).       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. The extended Ville's inequality for nonintegrable nonnegative supermartingales (with H. Wang), Bernoulli, 2025.       arXiv | TLDR

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

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

  4. 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

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

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

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

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

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

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

  11. 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 | TLDR

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

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

  14. 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

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

  16. Graph fission and cross-validation (with J. Leiner), Intl. Conf. on AI and Statistics (AISTATS), 2024       arXiv | TLDR

  17. Online multiple testing with e-values (with Z. Xu), Intl. Conf. on AI and Statistics (AISTATS), 2024.       arXiv | TLDR

  18. Deep anytime-valid hypothesis testing (with T. Pandeva, P. Forré, S. Shekhar), Intl. Conf. on AI and Statistics (AISTATS), 2024.       arXiv

  19. Differentially private conditional independence testing (with I. Kalemaj, S. Kasiviswanathan), Intl. Conf. on AI and Statistics (AISTATS), 2024.       arXiv | TLDR

  20. 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

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

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

  23. 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 | TLDR (Discussion paper)

  24. 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 | TLDR

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

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

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

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

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

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

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

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

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

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

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

  36. 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) | TLDR

  37. 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

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

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

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

  41. 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)

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

  43. 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 TLDR

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

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

  46. 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

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

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

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

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

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

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

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

  54. 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) | TLDR

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

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

  57. 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

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

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

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

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

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

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

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

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

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

  67. 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)

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

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

  70. 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

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

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

  73. 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)

  74. 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

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

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

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

  78. 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) | TLDR

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

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

  81. 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)

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

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

  84. 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

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

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

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

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

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

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

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

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

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

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

  95. 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

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

  97. 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

  98. 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

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

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

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

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

  103. 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

  104. 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

  105. 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

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

  107. 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)

  108. 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)

  109. 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)

  110. 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

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

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

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

  114. 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

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

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

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

  118. 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

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

  120. 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

  121. 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

  122. 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

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

  124. 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

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

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

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

  128. 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

  129. 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)

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

  131. 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