Information projections have found many important applications in probab...
We develop and compare e-variables for testing whether k samples of data...
We develop a representation of a decision maker's uncertainty based on
e...
Safe anytime-valid inference (SAVI) provides measures of statistical evi...
We propose a sequential, anytime valid method to test the conditional
in...
We study worst-case growth-rate optimal (GROW) E-variables for hypothesi...
A standard practice in statistical hypothesis testing is to mention the
...
E variables are tools for designing tests that keep their type-I error
g...
Supervised classification techniques use training samples to learn a
cla...
Science is idolized as a cumulative process ("standing on the shoulders ...
We give a novel, unified derivation of conditional PAC-Bayesian and mutu...
We develop E variables for testing whether two data streams come from th...
We generalize the notion of minimax convergence rate. In contrast to the...
The task of subgroup discovery (SD) is to find interpretable description...
We study generalized Bayesian inference under misspecification, i.e. whe...
This is an up-to-date introduction to and overview of the Minimum Descri...
We present a new theory of hypothesis testing. The main concept is the
S...
It is often claimed that Bayesian methods, in particular Bayes factor me...
We formalize the idea of probability distributions that lead to reliable...
We study online learning under logarithmic loss with regular parametric
...
This is the Proceedings of the Twenty-Sixth Conference on Uncertainty in...
Most methods for decision-theoretic online learning are based on the Hed...
Bayesian model averaging, model selection and its approximations such as...