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Universal Inference

Universal Inference

24 December 2019
Larry A. Wasserman
Aaditya Ramdas
Sivaraman Balakrishnan
ArXivPDFHTML

Papers citing "Universal Inference"

11 / 11 papers shown
Title
WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales
WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales
Drew Prinster
Xing Han
Anqi Liu
S. Saria
28
0
0
07 May 2025
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
Junghyun Lee
Se-Young Yun
Kwang-Sung Jun
26
4
0
19 Jul 2024
Combining exchangeable p-values
Combining exchangeable p-values
Matteo Gasparin
Ruodu Wang
Aaditya Ramdas
45
5
0
04 Apr 2024
Discussion of "A note on universal inference" by Timmy Tse and Anthony
  Davison
Discussion of "A note on universal inference" by Timmy Tse and Anthony Davison
Mathias Drton
Hongjian Shi
David Strieder
8
2
0
30 Mar 2023
A Sequential Test for Log-Concavity
A Sequential Test for Log-Concavity
Aditya Gangrade
Alessandro Rinaldo
Aaditya Ramdas
18
12
0
09 Jan 2023
E-values as unnormalized weights in multiple testing
E-values as unnormalized weights in multiple testing
Nikolaos Ignatiadis
Ruodu Wang
Aaditya Ramdas
10
23
0
26 Apr 2022
On the choice of the splitting ratio for the split likelihood ratio test
On the choice of the splitting ratio for the split likelihood ratio test
David Strieder
Mathias Drton
16
11
0
13 Mar 2022
Significance tests of feature relevance for a black-box learner
Significance tests of feature relevance for a black-box learner
Ben Dai
Xiaotong Shen
Wei Pan
8
24
0
02 Mar 2021
False discovery rate control with e-values
False discovery rate control with e-values
Ruodu Wang
Aaditya Ramdas
17
90
0
06 Sep 2020
A Note on Likelihood Ratio Tests for Models with Latent Variables
A Note on Likelihood Ratio Tests for Models with Latent Variables
Yunxiao Chen
I. Moustaki
Haoran Zhang
CML
10
13
0
10 Aug 2020
Rejoinder: On nearly assumption-free tests of nominal confidence
  interval coverage for causal parameters estimated by machine learning
Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
Lin Liu
Rajarshi Mukherjee
J. M. Robins
CML
18
16
0
07 Aug 2020
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