in my humble opinion, academic honesty for websites includes avoiding being misleading to an outsider - by differentiating between barely-reviewed or short papers (at workshops or some conferences with high acceptance rates) and rigorously-reviewed longer papers (at some conferences or journals with low acceptance rates).

* indicates an equally contributing (often student) author


that do not subsume (are not supersets of) conference papers

  • Fast & Flexible ADMM Algorithms for Trend Filtering
    Aaditya Ramdas, R. Tibshirani
    (JCGS'15) Journal of Computational and Graphical Statistics
    [arxiv (22pg)] [JCGS] [github `glmgen'] [50 min. talk] [bib]

  • Regularized Brain Reading with Shrinkage and Smoothing
    L. Wehbe, Aaditya Ramdas, R. Steorts, C. Shalizi
    (AoAS'15) Annals of Applied Statistics (accepted, in press)
    [arxiv (42pg)] [bib]

  • Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses
    L. Wehbe, B. Murphy, P. Talukdar, A. Fyshe, Aaditya Ramdas, T. Mitchell
    (PLoS ONE'14) Public Library of Science ONE, 2014
    [website] [PLOS] [pdf (19pg)] [supp (20pg)] [bib]

  • Adaptivity & Computation-Statistics Tradeoffs for Kernel & Distance based High-dimensional Two Sample Testing
    Aaditya Ramdas, S. Reddi, B. Poczos, A. Singh, L. Wasserman
    (in submission)
    [arxiv (35pg)]

  • Convergence properties of the randomized extended Gauss-Seidel and Kaczmarz methods
    A. Ma*, D. Needell*, Aaditya Ramdas*
    (in submission)
    [arxiv (16pg)]

  • Towards A Deeper Geometric, Analytic and Algorithmic Understanding of Margins
    Aaditya Ramdas, J. Pena
    (in submission)
    [arxiv (17pg)] [bib]

  • Rows vs Columns for Randomized Ridge Regression: Kaczmarz vs Coordinate Descent
    Ahmed Hefny*, Deanna Needell*, Aaditya Ramdas*,
    (in submission)
    [arxiv (16pg)]


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

  • Sequential Nonparametric Testing with the Law of the Iterated Logarithm
    Aaditya Ramdas, A. Balsubramani*
    (in submission)

  • One-Step Hypothesis Testing for Functional Neuroimaging
    L. Wehbe, Aaditya Ramdas, T. Mitchell
    (in submission)

  • Fast Two-Sample Tests with Analytic Representations of Random Features
    K. Chwialkowski, Aaditya Ramdas, D. Sejdinovic, A. Gretton
    (in submission)
    [arxiv] [github]

  • Nonparametric Independence Testing for Small Sample Sizes
    Aaditya Ramdas, L. Wehbe*
    (IJCAI '15, oral) 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
    Aaditya Ramdas, S. Reddi*, A. Singh, B. Poczos, L. Wasserman
    (AISTATS '15) 18th International Conference on Artificial Intelligence and Statistics, San Diego, 2014
    [proceedings] [arxiv (25pg)] [pdf (9pg)] [supp (10pg)] [bib]

  • On the Decreasing Power of Kernel and Distance based Nonparametric Hypothesis Tests in High Dimensions
    Aaditya Ramdas, S. Reddi*, B. Poczos, A. Singh, L. Wasserman
    (AAAI '15) 29th AAAI Conference on Artifical Intelligence, Austin, 2015
    [proceedings] [arxiv (19pg)] [pdf (7pg)] [supp (5pg)] [bib]

  • Margins, Kernels and Non-linear Smoothed Perceptrons
    Aaditya Ramdas, J. Pena
    (ICML '14, oral) 31st International Conference on Machine Learning, Beijing, 2014
    [arxiv] [proceedings] [pdf (9pg)] [supp (1pg)] [20-min oral] [bib]

  • An Analysis of Active Learning with Uniform Feature Noise
    Aaditya Ramdas, A. Singh, L. Wasserman, B. Poczos
    (AISTATS '14, oral) 17th International Conference on Artificial Intelligence and Statistics, Reykjavik, 2014
    [arxiv] [proceedings] [pdf (9pg)] [supp (8pg)] [25-min oral] [bib]

  • Algorithmic Connections Between Active Learning and Stochastic Convex Optimization
    Aaditya Ramdas, A. Singh
    (ALT '13, oral) 24th International Conference on Algorithmic Learning Theory, Singapore, 2013
    [arxiv] [proceedings] [pdf (15pg)] [25-min oral] [bib]

  • Optimal Rates for Stochastic Convex Optimization using Tsybakov's Noise Condition
    Aaditya Ramdas, A. Singh
    (ICML '13, oral) 30th International Conference on Machine Learning, Atlanta, 2013
    [proceedings] [arxiv] [pdf (9pg)] [supp (3pg)] [20-min oral] [bib]