Aaditya Ramdas
Aaditya Ramdas
Statistics and Machine Learning, Carnegie Mellon University
Email verificata su stat.cmu.edu - Home page
Citata da
Citata da
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
PLOS One, 2014
On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests
A Ramdas, N Garcia, M Cuturi
Entropy, Special Issue on Statistical Significance and the Logic of…, 2017
On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions
A Ramdas, S Jakkam Reddi, B Pczos, A Singh, L Wasserman
29th AAAI Conference on Artificial Intelligence, 2015
Algorithms for graph similarity and subgraph matching
D Koutra, A Parikh, A Ramdas, J Xiang
Technical report, Carnegie Mellon University, 2011
Fast Two-Sample Testing with Analytic Representations of Probability Measures
K Chwialkowski, A Ramdas, D Sejdinovic, A Gretton
29th Conference on Neural Information Processing Systems, 2015
Fast and flexible ADMM algorithms for trend filtering
A Ramdas, RJ Tibshirani
Journal of Computational and Graphical Statistics, 2014
Convergence properties of the randomized extended Gauss-Seidel and Kaczmarz methods
A Ma, D Needell, A Ramdas
SIAM Journal on Matrix Analysis and Applications, 2015
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
DJ Sutherland, HY Tung, H Strathmann, S De, A Ramdas, A Smola, ...
International Conference on Learning Representations (ICLR), 2017, 2016
Asymptotic behavior of -based Laplacian regularization in semi-supervised learning
A El Alaoui, X Cheng, A Ramdas, MJ Wainwright, MI Jordan
29th Annual Conference on Learning Theory, 879-906, 2016
A unified treatment of multiple testing with prior knowledge using the p-filter
A Ramdas, RF Barber, MJ Wainwright, MI Jordan
The Annals of Statistics, 2019
The p-filter: Multi-layer FDR control for grouped hypotheses
RF Barber, A Ramdas
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2016
Sequential Nonparametric Testing with the Law of the Iterated Logarithm
A Balsubramani, A Ramdas
32nd Conference on Uncertainty in Artificial Intelligence (UAI), 2016
On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives
SJ Reddi, A Ramdas, B Pczos, A Singh, L Wasserman
18th International Conference on Artificial Intelligence and Statistics, 772-780, 2015
Time-uniform, nonparametric, nonasymptotic confidence sequences
SR Howard, A Ramdas, J McAuliffe, J Sekhon
The Annals of Statistics (accepted), 2018
A framework for Multi-A(rmed)/B(andit) testing with online FDR control
F Yang, A Ramdas, KG Jamieson, MJ Wainwright
Advances in Neural Information Processing Systems, 5957-5966, 2017
Online control of the false discovery rate with decaying memory
A Ramdas, F Yang, MJ Wainwright, MI Jordan
Advances In Neural Information Processing Systems, 5650-5659, 2017
Rows versus Columns: Randomized Kaczmarz or Gauss--Seidel for Ridge Regression
A Hefny, D Needell, A Ramdas
SIAM Journal on Scientific Computing 39 (5), S528-S542, 2017
A general interactive framework for false discovery rate control under structural constraints
L Lei, A Ramdas, W Fithian
Biometrika, 2020
Optimal rates for stochastic convex optimization under Tsybakov noise condition
A Ramdas, A Singh
30th International Conference on Machine Learning, 2013
Time-uniform Chernoff bounds via nonnegative supermartingales
SR Howard, A Ramdas, J McAuliffe, J Sekhon
Probability Surveys 17, 257-317, 2020
Il sistema al momento non pu eseguire l'operazione. Riprova pi tardi.
Articoli 1–20