We develop the first provably valid test for log-concavity, which is a shape constraint for density estimation with applications across economics, survival modeling, and reliability theory. The test is based on universal inference and a scalable variant using random projections is developed.
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
We show how to achieve semiparametric efficient confidence intervals and confidence sequences for the average treatment effect when performing adaptive experimentation, based on the Adaptive AIPW (A2IPW) estimator of Kato.
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
As an alternative to universal inference and conformal prediction, we propose a new method for testing exchangeability which uses betting on pairs of observations, which applies to general observation spaces and can be shown to be consistent against reasonable alternatives.
Graph fission and cross-validation (with J. Leiner), Intl. Conf. on AI and Statistics (AISTATS), 2024
arXiv |
TLDR
We extend data fission/thinning to the graphical setting to develop a procedure that takes in an input graph with noisy observations at each node, and creates multiple independent synthetic copies of the graph with the same node+edge set as the original and noisier observations at each node. We then show how this method can be used for cross-validation and structural trend estimation on graph data.
Online multiple testing with e-values (with Z. Xu), Intl. Conf. on AI and Statistics (AISTATS), 2024.
arXiv |
TLDR
We design an extension of the LOND algorithm for online FDR and FCR control with e-values.
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
We derive differentially private versions of the generalized covariance measure (GCM), in both observational settings and under the model-X assumption, the primary difficulty of which arises from the fact that changing one datapoint changes all the GCM residuals.
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
We propose a new kernel-HSIC statistic that drops half the terms of the original, and show that it has a standard normal limiting null distribution under low and high dimensional regimes, resulting in a test that is trivial to calibrate without permutations. The analysis of this statistic requires generalizing the techniques we developed earlier for the corresponding kernel-MMD statistic, to deal with the more complicated dependence structure.
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)
We devise an alternative to data splitting using external randomization called data fission that more efficiently splits information in many circumstances and then apply it to several examples in post-selection inference: interactive multiple testing, fixed-design linear regression, generalized linear models, and trend filtering.
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
Ville's famous martingale theorem relates measure-theoretic probability to betting: it states that for any event of measure zero, there exists a nonnegative martingale (a betting strategy) that multiplies its initial wealth infinitely if the event occurs. We prove a composite generalization of that theorem, which requires generalizing ``measure zero'' to a certain inverse capital outer measures and generalizing ``nonnegative martingale'' to e-processes.
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
We develop a general framework for designing sequential two-sample tests, and obtain a general characterization of the power of these tests in terms of the regret of an associated online prediction game. This yields the ``right'' sequential generalizations of many offline nonparametric two-sample tests like Kolmogorov-Smirnov or kernel-MMD.
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
We derive basic and advanced composition results and privacy filters for noise-reduction mechanisms that allow an analyst to adaptively switch between differentially private and ex-post private mechanisms subject to an overall privacy guarantee.
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
By appropriately regularizing simple confidence sequences in Hilbert spaces, we derive (for the first time) sublinear regret for GP-UCB for any kernel with polynomial decay (including Matern).
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
We derive confidence sequences for convex functionals, with an emphasis on convex divergences such as the kernel Maximum Mean Discrepancy and Wasserstein distances; our main technical contribution is to show that empirical plugins of convex functionals/divergences (and more generally processes satisfying a leave-one-out property) are partially ordered reverse submartingales, coupled with maximal inequalities for such processes.
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
We derive a general reduction from constructing confidence sequences (CSs) for some functional~(say $\theta$) to detecting changes in that functional: given a stream of observations, construct a single CS (for $\theta$) in the forward direction, and a new CS in the backward direction with each new observation, and stop and declare a changepoint as soon as they do not intersect. We obtain tight guarantees on the average run length~(ARL) and the detection delay for this general strategy, and instantiate them for several classical and modern change detection problems.
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
We introduce the notion of risk-limiting financial audits (RLFA), where the goal is to design statistical procedures to verify an assertion about a set of reported financial transactions. We propose a general RLFA strategy using confidence sequences constructed with weighted sampling without replacement, and also develop techniques that can incorporate any available side information (such as predictions from AI models).
Huber-robust confidence sequences
(with H. Wang),
Intl. Conf. on AI and Statistics (AISTATS), 2023,
arXiv (full oral talk) | TLDR
Under a slight generalization of Huber's epsilon-contamination model (where epsilon fraction of the points are arbitrarily corrupted), we derive confidence sequences for univariate means only assuming a finite p-th moment (for p between 1 and 2), which are minimax optimal and perform very well in practice.
Catoni-style confidence sequences for heavy-tailed mean estimation
(with H. Wang),
Stochastic Processes and Applications, 2023
arXiv | article | code |
TLDR
We derive confidence sequences, which are confidence intervals valid at arbitrary stopping times, for univariate means only assuming a finite p-th moment (for p between 1 and 2), which are minimax optimal and perform very well in practice.
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
We introduce dimension-agnostic inference, which is a novel approach for high-dimensional inference that ensures asymptotic validity regardless of how the dimension and sample size scale, while preserving minimax optimal power across diverse scenarios; our main tool is a cross U-statistic, which drops half of the terms of a degenerate U-statistic to yield a limiting Gaussian distribution.
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
Under a Gaussian setting, we present the first in-depth exploration of the size, power, and relationships between several universal inference variants. We find that in this setting, the power of universal inference has the same behavior in n, d, alpha and SNR as the classical Wilks' Theorem approach, only losing in a small constant of about 2.
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
We propose a new kernel-MMD statistic that drops half the terms of the original, and show that it has a standard normal limiting null distribution in low and high dimensional regimes. This results in a test that is easy to calibrate, that is up to two orders of magnitude faster than running the permutation test, at the price of a small ($\approx \sqrt{2}$ in effective sample size) reduction in power.
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
Conformal methods typically rely on exchangeable data to provide valid prediction sets in finite samples, but we extend conformal methods to construct prediction sets in a nonexchangeable two-layer hierarchical setting, where N groups of data are exchangeable, and the observations within each group are also exchangeable.
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
We develop the first practically powerful algorithms that provably controls the supremum of the false discovery proportion with high probability in online multiple testing.
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
We explore the use of classification accuracy for two-sample testing for general classifiers, proving in particular that the accuracy test based on Fisher's LDA achieves minimax rate-optimal power and establishing conditions for consistency based on general classifiers.
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
Using e-values (or e-processes) and the e-BH procedure, we formulate a framework which provides false discovery rate (FDR) control at any stopping time for multiple testing in the bandit setting, that is robust to the dependencies induced by the user’s sampling and stopping policies.
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)
Miscellaneous
-
Adaptivity & computation-statistics tradeoffs for kernel & distance based high-dimensional two sample testing (with S. Reddi, B. Poczos, A. Singh, L. Wasserman).
arXiv | poster
-
Algorithms for graph similarity and subgraph matching (with D. Koutra, A. Parikh, J. Xiang).
report