##### (my philosophy: academic honesty for websites includes avoiding being misleading to an outsider;
this involves declaring which papers were double or single blind peer-reviewed at conferences/journals with low acceptance rates,
as opposed to being accepted for submission at places with very high acceptance rates;
it also includes declaring on which work you are a primary contributor of content,
where you know and can discuss the paper details confidently if contacted)

* indicates a paper on which I was one of the primary contributors, ** indicates an equally contributing student author

## Journals

(PLoS ONE '14)__Simultaneously uncovering brain regions involved in story reading subprocesses__

Public Library of Science ONE, November Issue, 2014

Leila Wehbe, B. Murphy, P. Talukdar, A. Fyshe,**Aaditya Ramdas**, T. Mitchell [website] [PLOS] [pdf] [supp]

*****__Fast & Flexible ADMM Algorithms for Trend Filtering__

Journal of Computational and Graphical Statistics (JCGS) - in submission

**Aaditya Ramdas**, Ryan Tibshirani [arxiv] [R package] [50 min. talk]

*****__Regularized Brain Reading with Shrinkage and Smoothing__

Annals of Applied Statistics (AoAS) - in submission

Leila Wehbe,**Aaditya Ramdas**, Rebecca Steorts, Cosma Shalizi [arxiv]

*****__Towards A Deeper Geometric, Analytic and Algorithmic Understanding of Margins__

Optimization Methods and Software (OMS) - in submission

**Aaditya Ramdas**, Javier Pena [arxiv]

## Preprints

*****__Free Lunches and Computation-Statistics Tradeoffs for High Dimensional Two Sample Testing__

(in preparation)

**Aaditya Ramdas**, Sashank Reddi**, Barnabas Poczos, Aarti Singh, Larry Wasserman

*****__Stein Shrinkage for Cross-Covariance Operators and Kernel Independence Testing__

(in preparation)

**Aaditya Ramdas**, **Leila Wehbe [arxiv]

*****__Rows vs Columns for Randomized Ridge Regression: Kaczmarz vs Coordinate Descent__

(in preparation)

**Aaditya Ramdas**, Ahmed Hefny [arxiv]

*****__Randomized Extended Gauss-Seidel : convergence in the undercomlete setting__

(in preparation)

**Aaditya Ramdas**, Deanna Needell, Anna Ma

__Fast Two-Sample Tests with Random Features__

(in preparation)

Kacper Chwialkowski,**Aaditya Ramdas**, Dino Sejdinovic, Arthur Gretton

## Conferences^{}

##### full papers, respected venues, low acceptance rates, published proceedings, single/double blind detailed reviews

*****(AISTATS '15)__On the Power of a Linear-Time Nonparametric Two Sample Test in High Dimensions__

18th International Conference on Artificial Intelligence and Statistics, San Diego, 2014

**Aaditya Ramdas**, **Sashank Reddi, Aarti Singh, Barnabas Poczos, Larry Wasserman [arxiv]

*****(AAAI '15)__On the Decreasing Power of Kernel- and Distance-based Hypothesis Tests in High Dimensions__

29th AAAI Conference on Artifical Intelligence, Austin, 2015

**Aaditya Ramdas**, **Sashank Reddi, Barnabas Poczos, Aarti Singh, Larry Wasserman [arxiv]

*****(ICML '14)__Margins, Kernels and Non-linear Smoothed Perceptrons__

31st International Conference on Machine Learning, Beijing, 2014

**Aaditya Ramdas**, Javier Pena [ICML] [pdf] [supp] [20-min oral] [abs]

*****(AISTATS '14)__An Analysis of Active Learning with Uniform Feature Noise__

17th International Conference on Artificial Intelligence and Statistics, Reykjavik, 2014

**Aaditya Ramdas**, Aarti Singh, Larry Wasserman, Barnabas Poczos [AISTATS] [pdf] [supp] [25-min oral] [abs]

*****(ALT '13)__Algorithmic Connections Between Active Learning and Stochastic Convex Optimization__

24th International Conference on Algorithmic Learning Theory, Singapore, 2013

**Aaditya Ramdas**, Aarti Singh [ALT] [pdf] [25-min oral] [abs]

*****(ICML '13)__Optimal Rates for Stochastic Convex Optimization using Tsybakov's Noise Condition__

30th International Conference on Machine Learning, Atlanta, 2013

**Aaditya Ramdas**, Aarti Singh [arxiv] [ICML] [pdf] [supp] [20-min oral] [abs]