Associate Professor
Department of Statistics and Data Science (75%) |
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.
e-values and confidence sequences (e-processes, supermartingales, testing by betting, sequential inference, optional stopping, peeking and p-hacking, change detection, anytime p-values, Ville's inequality, 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.
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.
Conformalized interactive imitation learning: handling expert shift and intermittent feedback (with M. Zhao, R. Simmons, H. Admoni, A. Bajcsy) arXiv | website
β-calibration of Language Model Confidence Scores for Generative QA (with P. Manggala, A. Mastakouri, E. Kirschbaum, S. Kasiviswanathan) arXiv
Compound e-values and Empirical Bayes (with N. Ignatiadis, R. Wang) arXiv
Sequential Kernelized Stein Discrepancy (with D. Taboada). arXiv | TLDR
Empirical Bernstein in smooth Banach spaces (with D. Taboada). arXiv | TLDR
Robust likelihood ratio tests for composite nulls and alternatives (with A. Saha). arXiv | TLDR
Practical maximally flexible sampling designs for experiments based on e-values (with A. Ly, U. Boehm, P. Grünwald, D. van Ravenzwaaij). psyarXiv | TLDR
Anytime-valid inference for double/debiased machine learning of causal parameters (with A. Dalal, P. Blobaum, S. Kasiviswanathan). arXiv | TLDR
Matrix concentration: order versus anti-order (with R. Malekian). arXiv | TLDR
An online generalization of the (e-)Benjamini-Hochberg procedure (with L. Fischer, Z. Xu). arXiv | TLDR
Online closed testing with e-values (with L. Fischer). arXiv | TLDR
Multiple testing with anytime-valid Monte-Carlo p-values (with L. Fischer). arXiv | TLDR
Combining exchangeable p-values (with M. Gasparin, R. Wang). arXiv | TLDR
Conformal online model aggregation (with M. Gasparin). arXiv | TLDR
The numeraire e-variable and reverse information projection (with M. Larsson, J. Ruf). arXiv | TLDR
Combining evidence across filtrations using adjusters (with Y.J. Choe). arXiv | TLDR | code | slides
Distribution-uniform strong laws of large numbers (with I. Waudby-Smith, M. Larsson). arXiv | TLDR
Positive semidefinite supermartingales and randomized matrix concentration inequalities (with H. Wang). arXiv | TLDR
Testing by betting while borrowing and bargaining (with H. Wang). arXiv | TLDR
Merging uncertainty sets via majority vote (with M. Gasparin). arXiv | TLDR
Sequential Monte-Carlo testing by betting (with L. Fischer). arXiv | TLDR
Time-uniform confidence spheres for means of random vectors (with B. Chugg, H. Wang). arXiv | TLDR
Distribution-uniform anytime-valid inference (with I. Waudby-Smith). arXiv | TLDR
Time-uniform self-normalized concentration for vector-valued processes (with J. Whitehouse, S. Wu). arXiv | TLDR
Anytime-valid t-tests and confidence sequences for Gaussian means with unknown variance (with H. Wang). arXiv | TLDR
On the near-optimality of betting confidence sets for bounded means (with S. Shekhar). arXiv | TLDR
Scalable causal structure learning via amortized conditional independence testing (with J. Leiner, B. Manzo, W. Tansey). arXiv | code | TLDR
More powerful multiple testing under dependence via randomization (with Z. Xu). arXiv | TLDR
A sequential test for log-concavity (with A. Gangrade, A. Rinaldo). arXiv
Admissible anytime-valid sequential inference must rely on nonnegative martingales (with J. Ruf, M. Larsson, W. Koolen). arXiv
The extended Ville's inequality for nonintegrable nonnegative supermartingales (with H. Wang), Bernoulli, 2025. arXiv | TLDR
Bias detection via signaling (with T. Lin, I. Shapira, Y. Chen, A. Procaccia), Conf. on Neural Information Processing Systems (NeurIPS), 2024. arXiv | TLDR
On the existence of powerful p-values and e-values for composite hypotheses (with Z. Zhang, R. Wang), Annals of Stat., 2024. arXiv
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
Randomized and exchangeable improvements of Markov's, Chebyshev's and Chernoff's inequalities (with T. Manole), Statistical Science, 2024. arXiv
Post-selection inference for e-value based confidence intervals (with Z. Xu, R. Wang), Elec J. Stat., 2024. arXiv | proc | talk | slides | TLDR
Interactive identification of individuals with positive treatment effect while controlling false discoveries (with B. Duan, L. Wasserman), J. of Causal Inference, 2024. arXiv | proc
Multiple testing under negative dependence (with Z. Chi, R. Wang), Bernoulli, 2024. arXiv
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
Reducing sequential change detection to sequential estimation (with S. Shekhar), Intl. Conf. on Machine Learning (ICML), 2024. arXiv | TLDR
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
De Finetti's Theorem and related results for infinite weighted exchangeable sequences (with R. Barber, E. Candes, R. Tibshirani), Bernoulli, 2024 arXiv | proc
Semiparametric efficient inference in adaptive experiments (with T. Cook, A. Mishler), Conference on Causal Learning and Reasoning (CLeaR), 2024. arXiv | TLDR
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
Testing exchangeability by pairwise betting (with A. Saha), Intl. Conf. on AI and Statistics (AISTATS), 2024. (oral talk) arXiv | proc | poster | TLDR
Graph fission and cross-validation (with J. Leiner), Intl. Conf. on AI and Statistics (AISTATS), 2024 arXiv | TLDR
Online multiple testing with e-values (with Z. Xu), Intl. Conf. on AI and Statistics (AISTATS), 2024. arXiv | TLDR
Deep anytime-valid hypothesis testing (with T. Pandeva, P. Forré, S. Shekhar), Intl. Conf. on AI and Statistics (AISTATS), 2024. arXiv
Differentially private conditional independence testing (with I. Kalemaj, S. Kasiviswanathan), Intl. Conf. on AI and Statistics (AISTATS), 2024. arXiv | TLDR
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
A unified recipe for deriving (time-uniform) PAC-Bayes bounds (with B. Chugg, H. Wang), J of ML Research, 2023. arXiv | proc
A permutation-free kernel independence test (with S. Shekhar, I. Kim), J of ML Research, 2023. arXiv | code | proc | TLDR
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)
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
Online multiple hypothesis testing (with D. Robertson, J. Wason), Statistical Science, 2023 arXiv | proc
Nonparametric two-sample testing by betting (with S. Shekhar), IEEE Trans. on Info. Theory, 2023 arXiv | proc | code | slides | TLDR
E-values as unnormalized weights in multiple testing (with N. Ignatiadis, R. Wang), Biometrika, 2023 arXiv | proc
Comparing sequential forecasters (with Y.J. Choe), Operations Research, 2023 arXiv | proc | code | talk | poster | slides (Citadel, Research Showcase Runner-up)
Game-theoretic statistics and safe anytime-valid inference (with P. Grunwald, V. Vovk, G. Shafer), Statistical Science, 2023 arXiv | proc
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
Sequential predictive two-sample and independence testing (with A. Podkopaev), Conf. on Neural Information Processing Systems (NeurIPS), 2023 arXiv | proc
Auditing fairness by betting (with B. Chugg, S. Cortes-Gomez, B. Wilder), Conf. on Neural Information Processing Systems (NeurIPS), 2023 arXiv | code | proc
Counterfactually comparing abstaining classifiers (with Y. J. Choe, A. Gangrade), Conf. on Neural Information Processing Systems (NeurIPS), 2023 arXiv | slides | proc
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
On the sublinear regret of GP-UCB (with J. Whitehouse, S. Wu), Conf. on Neural Information Processing Systems (NeurIPS), 2023 arXiv | TLDR
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
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
Sequential change detection via backward confidence sequences (with S. Shekhar). Intl. Conf. on Machine Learning (ICML), 2023 arXiv | code | slides | TLDR
Fully adaptive composition in differential privacy (with J. Whitehouse, R. Rogers, Z. S. Wu), Intl. Conf. on Machine Learning (ICML), 2023 arXiv | proc
Online Platt scaling with calibeating (with C. Gupta), Intl. Conf. on Machine Learning (ICML), 2023 arXiv | proc
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)
Sequential kernelized independence testing (with A. Podkopaev, P. Bloebaum, S. Kasiviswanathan), Intl. Conf. on Machine Learning (ICML), 2023 arXiv | proc
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
Huber-robust confidence sequences (with H. Wang), Intl. Conf. on AI and Statistics (AISTATS), 2023, arXiv (full oral talk) | TLDR
Catoni-style confidence sequences for heavy-tailed mean estimation (with H. Wang), Stochastic Processes and Applications, 2023 arXiv | article | code | TLDR
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
Dimension-agnostic inference using cross U-statistics (with I. Kim), Bernoulli, 2023 arXiv | proc | TLDR
On the power of conditional independence testing under model-X (with E. Katsevich), Electronic J. Stat, 2023 arXiv | article
Permutation tests using arbitrary permutation distributions (with R. Barber, E. Candes, R. Tibshirani), Sankhya A, 2023 arXiv | article
Conformal prediction beyond exchangeability (with R. Barber, E. Candes, R. Tibshirani), Annals of Stat., 2023 arXiv | article
Faster online calibration without randomization: interval forecasts and the power of two choices (with C. Gupta), Conf. on Learning Theory (COLT), 2022 arXiv | article
Top-label calibration and multiclass-to-binary reductions (with C. Gupta), Intl. Conf. on Learning Representations, 2022 arXiv | article
Gaussian universal likelihood ratio testing (with R. Dunn, S. Balakrishnan, L. Wasserman), Biometrika, 2022 arXiv | article | TLDR
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
Testing exchangeability: fork-convexity, supermartingales, and e-processes (with J. Ruf, M. Larsson, W. Koolen). Intl J. of Approximate Reasoning, 2022 arXiv | article
Tracking the risk of a deployed model and detecting harmful distribution shifts (with A. Podkopaev). Intl. Conf. on Learning Representations (ICLR), 2022 arXiv | article
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
Sequential estimation of quantiles with applications to A/B-testing and best-arm identification (with S. Howard), Bernoulli, 2022 arXiv | article | code
Brainprints: identifying individuals from magnetoencephalograms (with S. Wu, L. Wehbe), Nature Communications Biology, 2022 bioRxiv | article
Interactive rank testing by betting (with B. Duan, L. Wasserman), Conf. on Causal Learning and Reasoning (CLEAR), 2022 arXiv | article (oral talk)
Large-scale simultaneous inference under dependence (with J. Tian, X. Chen, E. Katsevich, J. Goeman), Scandanavian J of Stat., 2022 arXiv | article
False discovery rate control with e-values (with R. Wang), J. of the Royal Stat. Soc., Series B, 2022 arXiv | article
Nested conformal prediction and quantile out-of-bag ensemble methods (with C. Gupta, A. Kuchibhotla), Pattern Recognition, 2022 arXiv | article | code
Distribution-free prediction sets for two-layer hierarchical models (with R. Dunn, L. Wasserman), J of American Stat. Assoc., 2022 arXiv | article | code | TLDR
Fast and powerful conditional randomization testing via distillation (with M. Liu, E. Katsevich, L. Janson), Biometrika, 2021 arXiv | article | code
Uncertainty quantification using martingales for misspecified Gaussian processes (with W. Neiswanger), Algorithmic Learning Theory (ALT), 2021 arXiv | article | code | talk
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)
Predictive inference with the jackknife+ (with R. Barber, E. Candes, R. Tibshirani), Annals of Stat., 2021 arXiv | article | code
Path length bounds for gradient descent and flow (with C. Gupta, S. Balakrishnan), J. of Machine Learning Research, 2021 arXiv | article | blog
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
Time-uniform, nonparametric, nonasymptotic confidence sequences (with S. Howard, J. Sekhon, J. McAuliffe), The Annals of Stat., 2021 arXiv | article | code | tutorial
Off-policy confidence sequences (with N. Karampatziakis, P. Mineiro), Intl. Conf. on Machine Learning (ICML), 2021 arXiv | article
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)
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
Dynamic algorithms for online multiple testing (with Z. Xu), Conf. on Math. and Scientific Machine Learning, 2021 arXiv | article | talk | slides | code | TLDR
Online control of the familywise error rate (with J. Tian), Statistical Methods in Medical Research, 2021 arXiv | article
Asynchronous online testing of multiple hypotheses (with T. Zrnic, M. Jordan), J. of Machine Learning Research, 2021 arXiv | article | code | blog
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
Distribution-free calibration guarantees for histogram binning without sample splitting (with C. Gupta), Intl. Conf. on Machine Learning, 2021 arXiv | article
Distribution-free uncertainty quantification for classification under label shift (with A. Podkopaev), Conf. on Uncertainty in AI, 2021 arXiv | article
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)
The limits of distribution-free conditional predictive inference (with R. Barber, E. Candes, R. Tibshirani), Information and Inference, 2020 arXiv | article
Analyzing student strategies in blended courses using clickstream data (with N. Akpinar, U. Acar), Educational Data Mining, 2020 arXiv | article | talk (oral talk)
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
Online control of the false coverage rate and false sign rate (with A. Weinstein), Intl. Conf. on Machine Learning (ICML), 2020 arXiv | article
Confidence sequences for sampling without replacement (with I. Waudby-Smith), Conf. on Neural Information Processing Systems (NeurIPS), 2020 arXiv | article | code (spotlight talk)
Universal inference (with L. Wasserman, S. Balakrishnan), Proc. of the National Academy of Sciences, 2020 arXiv | article | talk
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
Simultaneous high-probability bounds on the FDP in structured, regression and online settings (with E. Katsevich), Annals of Stat., 2020 arXiv | article | code
Time-uniform Chernoff bounds via nonnegative supermartingales (with S. Howard, J. Sekhon, J. McAuliffe), Prob. Surveys, 2020 arXiv | article | talk
STAR: A general interactive framework for FDR control under structural constraints (with L. Lei, W. Fithian), Biometrika, 2020 arXiv | article | poster | code
Familywise error rate control by interactive unmasking (with B. Duan, L. Wasserman), Intl. Conf. on Machine Learning (ICML), 2020 arXiv | article | code
Interactive martingale tests for the global null (with B. Duan, S. Balakrishnan, L. Wasserman), Electronic J. of Stat., 2020 arXiv | article | code
On conditional versus marginal bias in multi-armed bandits (with J. Shin, A. Rinaldo), Intl. Conf. on Machine Learning (ICML), 2020 arXiv | article
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
A higher order Kolmogorov-Smirnov test (with V. Sadhanala, Y. Wang, R. Tibshirani), Intl. Conf. on AI and Statistics, 2019 arXiv | article
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
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
DAGGER: A sequential algorithm for FDR control on DAGs (with J. Chen, M. Wainwright, M. Jordan), Biometrika, 2019 arXiv | article | code
Conformal prediction under covariate shift (with R. Tibshirani, R. Barber, E. Candes), Conf. on Neural Information Processing Systems (NeurIPS), 2019 arXiv | article | poster
Optimal rates and tradeoffs in multiple testing (with M. Rabinovich, M. Wainwright, M. Jordan), Statistica Sinica, 2019 arXiv | article | poster
Function-specific mixing times and concentration away from equilibrium (with M. Rabinovich, M. Wainwright, M. Jordan), Bayesian Analysis, 2019 arXiv | article | poster
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
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
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
On kernel methods for covariates that are rankings (with H. Mania, M. Wainwright, M. Jordan, B. Recht), Electronic J. of Stat., 2018 arXiv | article
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)
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)
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)
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
Iterative methods for solving factorized linear systems (with A. Ma, D. Needell), SIAM J. on Matrix Analysis and Applications, 2017 arXiv | article
Rows vs. columns : randomized Kaczmarz or Gauss-Seidel for ridge regression (with A. Hefny, D. Needell), SIAM J. on Scientific Computing, 2017 arXiv | article
On Wasserstein two sample testing and related families of nonparametric tests (with N. Garcia, M. Cuturi), Entropy, 2017 arXiv | article
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
Minimax lower bounds for linear independence testing (with D. Isenberg, A. Singh, L. Wasserman), IEEE Intl. Symp. on Information Theory, 2016 arXiv | article
p-filter: multi-layer FDR control for grouped hypotheses (with COAUTHORS), J. of the Royal Stat. Society, Series B, 2016 arXiv | article |code | poster
Sequential nonparametric testing with the law of the iterated logarithm (with A. Balsubramani), Conf. on Uncertainty in AI, 2016 arXiv | article | errata
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
Regularized brain reading with shrinkage and smoothing (with L. Wehbe, R. Steorts, C. Shalizi), Annals of Applied Stat., 2015 arXiv | article
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
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
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
Nonparametric independence testing for small sample sizes (with L. Wehbe), Intl. Joint Conf. on AI, 2015 arXiv | article (oral talk)
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
Fast & flexible ADMM algorithms for trend filtering (with R. Tibshirani), J. of Computational and Graphical Statistics, 2015 arXiv | article | talk | code
Towards a deeper geometric, analytic and algorithmic understanding of margins (with J. Pena), Opt. Methods and Software, 2015 arXiv | article
Margins, kernels and non-linear smoothed perceptrons (with J. Pena), Intl. Conf. on Machine Learning (ICML), 2014 arXiv | article | poster | talk (oral talk)
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
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)
Algorithmic connections between active learning and stochastic convex optimization (with A. Singh), Conf. on Algorithmic Learning Theory (ALT), 2013 arXiv | article | poster
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)