受强制性开放获取政策约束的文章 - Aaditya Ramdas了解详情
可在其他位置公开访问的文章：63 篇
Conformal prediction under covariate shift
RJ Tibshirani, RF Barber, EJ Candès, A Ramdas
Advances in Neural Information Processing Systems 32, 2019
强制性开放获取政策： US National Science Foundation, US Department of Defense
Predictive inference with the jackknife+
RF Barber, EJ Candes, A Ramdas, RJ Tibshirani
The Annals of Statistics, 2021
强制性开放获取政策： US National Science Foundation, US Department of Defense
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
强制性开放获取政策： US National Institutes of Health
Time-uniform, nonparametric, nonasymptotic confidence sequences
SR Howard, A Ramdas, J McAuliffe, J Sekhon
The Annals of Statistics, 2021
强制性开放获取政策： US National Science Foundation, US Department of Defense
Conformal prediction beyond exchangeability
RF Barber, EJ Candes, A Ramdas, RJ Tibshirani
The Annals of Statistics 51 (2), 816-845, 2023
强制性开放获取政策： US National Science Foundation, US Department of Defense
Universal Inference
L Wasserman, A Ramdas, S Balakrishnan
Proceedings of the National Academy of Sciences, 2020
强制性开放获取政策： US National Science Foundation
Estimating means of bounded random variables by betting
I Waudby-Smith, A Ramdas
Journal of the Royal Statistical Society, Series B (Methodology), with …, 2022
强制性开放获取政策： US National Science Foundation
Time-uniform Chernoff bounds via nonnegative supermartingales
SR Howard, A Ramdas, J McAuliffe, J Sekhon
Probability Surveys, 2020
强制性开放获取政策： US Department of Defense
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
强制性开放获取政策： US National Science Foundation, US Department of Defense
False discovery rate control with e-values
R Wang, A Ramdas
Journal of the Royal Statistical Society: Series B, 2022
强制性开放获取政策： US National Science Foundation, Natural Sciences and Engineering Research …
Nested conformal prediction and quantile out-of-bag ensemble methods
C Gupta, AK Kuchibhotla, AK Ramdas
Pattern Recognition, Special Issue on Conformal Prediction and Applications, 2021
强制性开放获取政策： US National Science Foundation
Classification accuracy as a proxy for two-sample testing
I Kim, A Ramdas, A Singh, L Wasserman
Annals of Statistics 49 (1), 411-434, 2021
强制性开放获取政策： US National Science Foundation, US Department of Defense
Testing exchangeability: fork-convexity, supermartingales and e-processes
A Ramdas, J Ruf, M Larsson, WM Koolen
International Journal of Approximate Reasoning, 2021
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： US National Science Foundation, US Department of Defense
Online control of the false discovery rate with decaying memory
A Ramdas, F Yang, MJ Wainwright, MI Jordan
Advances in Neural Information Processing Systems, 2017
强制性开放获取政策： US National Science Foundation, US Department of Defense
Sequential estimation of quantiles with applications to A/B-testing and best-arm identification
SR Howard, A Ramdas
Bernoulli, 2021
强制性开放获取政策： US Department of Defense
Fast and Powerful Conditional Randomization Testing via Distillation
M Liu, E Katsevich, L Janson, A Ramdas
Biometrika, 2021
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
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
强制性开放获取政策： US National Science Foundation, US Department of Defense
Top-label calibration and multiclass-to-binary reductions
C Gupta, AK Ramdas
International Conference on Learning Representations, 2022
强制性开放获取政策： US National Science Foundation
Catoni-style confidence sequences for heavy-tailed mean estimation
H Wang, A Ramdas
Stochastic Processes and Their Applications 163, 168-202, 2023
强制性开放获取政策： US Department of Defense
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