I am a postdoctoral researcher working with Michael Jordan and Martin Wainwright at UC Berkeley, hosted jointly in the departments of Statistics & EECS. I earned my PhD under Larry Wasserman and Aarti Singh at Carnegie Mellon University, jointly in the departments of Statistics & Machine Learning. I completed my Bachelors thesis under Supratik Chakraborty at IIT Bombay from the department of Computer Science and Engineering.

I only list, by topic, rigorously peer-reviewed, full-length papers at reputable journals or conferences.
I exclude all short workshop/conference papers, poster/talk abstracts and papers with little/no review.
* indicates an equally contributing (often student) author.

False Discovery Rate control (in structured, dynamic, interactive settings)


A unified treatment of multiple testing with prior knowledge using the p-filter
A. Ramdas, R. F. Barber, M. Wainwright, M. Jordan [arxiv] [code]
(in submission, the Annals of Statistics)
P-filter: multi-layer FDR control for grouped hypotheses
R. F. Barber*, A. Ramdas* [arxiv] [code] [JRSSB]
(JRSSB) Journal of the Royal Statistical Society -- Series B (Methodology), 2016
Optimal rates and tradeoffs for multiple testing
M. Rabinovich, A. Ramdas, M. Wainwright, M. Jordan [arxiv]
(in submission, Statistica Sinica)
MAB-FDR: Multi (A)rmed/(B)andit testing with online FDR control
F. Yang, A. Ramdas, K. Jamieson, M. Wainwright [arxiv] [code][spotlight talk]
(NIPS) 31st Conference on Neural Information Processing Systems, Long Beach, 2017
Online control of the false discovery rate with decaying memory
A. Ramdas, F. Yang, M. Wainwright, M. Jordan [arxiv][20-min oral]
(NIPS) 31st Conference on Neural Information Processing Systems, Long Beach, 2017
QuTE: decentralized FDR control on sensor networks
A. Ramdas, J. Chen, M. Wainwright, M. Jordan [code]
(CDC) IEEE Conference on Decision and Control, 2017
DAGGER: A sequential algorithm for FDR control on DAGs
A. Ramdas, J. Chen, M. Wainwright, M. Jordan [arxiv] [code]
(in submission, Biometrika)
STAR: A general interactive framework for FDR control under structural constraints
L. Lei, A. Ramdas, W. Fithian [arxiv] [movies]
(in submission, Journal of the Royal Statistical Society, Series B)


Hypothesis testing (in nonparametric, structured or high-dimensional settings)


Generative models and model criticism via optimized Maximum Mean Discrepancy
D. Sutherland, H. Tung, H. Strathmann, S. De, A. Ramdas, A. Smola, A. Gretton [arxiv] [ICLR] [poster] [code]
(ICLR) 5th International Conference on Learning Representations, Toulon, 2017
Adaptivity & computation-statistics tradeoffs for kernel & distance based high-dimensional two sample testing
A. Ramdas, S. Reddi, B. Poczos, A. Singh, L. Wasserman [arxiv]
(in revision)
Minimax lower bounds for linear independence testing
D. Isenberg*, A. Ramdas*, A. Singh, L. Wasserman [arxiv][ISIT]
(ISIT) IEEE International Symposium on Information Theory, Barcelona, 2016
Fast two-sample testing with analytic representations of probability measures
K. Chwialkowski, A. Ramdas, D. Sejdinovic, A. Gretton [arxiv] [github] [NIPS]
(NIPS) 29th Conference on Neural Information Processing Systems, Montreal, 2015
Nonparametric independence testing for small sample sizes
A. Ramdas*, L. Wehbe* [arxiv] [IJCAI] [20-min oral]
(IJCAI) 24th International Joint Conference on Artificial Intelligence, Buenos Aires, 2015
On the high-dimensional power of a linear-time two sample test under mean-shift alternatives
S. Reddi*, A. Ramdas*, A. Singh, B. Poczos, L. Wasserman [AISTATS] [arxiv] [pdf] [supp]
(AISTATS) 18th International Conference on Artificial Intelligence and Statistics, San Diego, 2015
On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions
A. Ramdas*, S. Reddi*, B. Poczos, A. Singh, L. Wasserman [AAAI] [arxiv] [pdf] [supp]
(AAAI) 29th AAAI Conference on Artifical Intelligence, Austin, 2015
Classification accuracy as a proxy for two sample testing
A. Ramdas, A. Singh, L. Wasserman [arxiv]
(in revision)
On Wasserstein two sample testing and related families of nonparametric tests
A. Ramdas*, N. Garcia*, M. Cuturi [arxiv] [Entropy]
(Ent) Entropy, Special Issue on Statistical Significance and the Logic of Hypothesis Testing, 2017


Convex optimization


Iterative methods for solving factorized linear systems
A. Ma, D. Needell, A. Ramdas [arxiv]
(SIMAX) SIAM Journal on Matrix Analysis and Applications, 2017
Rows vs columns : randomized Kaczmarz or Gauss-Seidel for ridge regression
A. Hefny*, D. Needell*, A. Ramdas*, [arxiv]
(SISC) SIAM Journal on Scientific Computing, 2017
Towards a deeper geometric, analytic and algorithmic understanding of margins
A. Ramdas, J. Pena [arxiv] [OMS]
(OMS) Optimization Methods and Software, 2015
Convergence properties of the randomized extended Gauss-Seidel and Kaczmarz methods
A. Ma*, D. Needell*, A. Ramdas* [arxiv] [SIMAX]
(SIMAX) SIAM Journal on Matrix Analysis and Applications, 2015
Fast & flexible ADMM algorithms for trend filtering
A. Ramdas*, R. Tibshirani* [arxiv] [JCGS] [github `glmgen'] [50 min. talk]
(JCGS) Journal of Computational and Graphical Statistics, 2015
Margins, kernels and non-linear smoothed perceptrons
A. Ramdas, J. Pena [arxiv] [ICML] [pdf] [supp] [20-min oral]
(ICML) 31st International Conference on Machine Learning, Beijing, 2014
Optimal rates for stochastic convex optimization under Tsybakov's noise condition
A. Ramdas, A. Singh [ICML] [arxiv] [pdf] [supp] [20-min oral]
(ICML) 30th International Conference on Machine Learning, Atlanta, 2013


Other problems of a sequential nature


On the power of online thinning in reducing discrepancy
R. Dwivedi, O. N. Feldheim, Ori Gurel-Gurevich, A. Ramdas [arxiv]
(in submission, Probability Theory and Related Fields)
Uniform martingale concentration and non-asymptotic confidence sequences
S. Howard, A. Ramdas, J. Sekhon, J. McAuliffe
(in preparation)
Function-specific mixing times and concentration away from equilibrium
M. Rabinovich, A. Ramdas, M. Wainwright, M. Jordan [arxiv]
(in submission, Bayesian Analysis)
Sequential nonparametric testing with the law of the iterated logarithm
A. Balsubramani*, A. Ramdas* [arxiv] [UAI]
(UAI) 32nd Conference on Uncertainty in Artificial Intelligence, New York, 2016
An analysis of active learning with uniform feature noise
A. Ramdas, A. Singh, L. Wasserman, B. Poczos [arxiv] [AISTATS] [pdf] [supp] [25-min oral]
(AISTATS) 17th International Conference on Artificial Intelligence and Statistics, Reykjavik, 2014
Algorithmic connections between active learning and stochastic convex optimization
A. Ramdas, A. Singh [arxiv] [ALT] [pdf] [25-min oral]
(ALT) 24th International Conference on Algorithmic Learning Theory, Singapore, 2013


Other collaborations


On kernel methods for covariates that are rankings
H. Mania, A. Ramdas, M. Wainwright, M. Jordan, B. Recht [arxiv]
(in submission, Electronic Journal of Statistics)
Decoding from pooled data (II): sharp information-theoretic bounds
A. El-Alaoui, A. Ramdas, F. Krzakala, L. Zdeborova, M. Jordan [arxiv]
(in submission, Mathematical Statistics and Learning)
Decoding from pooled data (I): phase transitions of message passing
A. El-Alaoui, A. Ramdas, F. Krzakala, L. Zdeborova, M. Jordan [arxiv]
(ISIT) IEEE International Symposium on Information Theory, Aachen, 2017
Asymptotic behavior of Lq-based Laplacian regularization in semi-supervised learning
A. El-Alaoui, X. Cheng, A. Ramdas, M. Wainwright, M. Jordan [arxiv][COLT]
(COLT) 29th International Conference on Learning Theory, New York, 2016
Regularized brain reading with shrinkage and smoothing
L. Wehbe, A. Ramdas, R. Steorts, C. Shalizi [arxiv] [AoAS]
(AoAS) Annals of Applied Statistics, 2015
Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses
L. Wehbe, B. Murphy, P. Talukdar, A. Fyshe, A. Ramdas, T. Mitchell [website] [PLOS] [pdf] [supp]
(PLoS ONE) Public Library of Science ONE, 2014


Theses and Reports


Computational and Statistical Advances in Testing and Learning (PhD thesis)
A. Ramdas [pdf] (Umesh K. Gavaskar Memorial Thesis Award in Statistics)
(CMU) Carnegie Mellon University, Statistics and Machine Learning, 2015
Analysis of burglaries in Pittsburgh (data analysis project)
A. Ramdas [pdf]
(CMU) Carnegie Mellon University, Statistics, 2013
Algorithms for graph similarity and subgraph matching (course project)
D. Koutra, A. Parikh, A. Ramdas, J. Xiang [pdf]
(CMU) Carnegie Mellon University, Machine Learning, 2011
Termination of single-loop linear programs (Bachelor's thesis)
A. Ramdas [pdf]
(IITB) IIT Bombay, Computer Science and Engineering, 2009
Volume-based landmark selection for dimensionality reduction (internship report)
A. Ramdas [pdf]
(INRIA) Geometrica, INRIA Sophia-Antipolis, 2008
Network of timed automata and their symbolic unfolding (internship report)
A. Ramdas [pdf]
(LaBRI) LaBRI, University of Bordeaux I, 2007