Multiple testing for signal-agnostic searches for new physics with machine learning
In this work, we address the question of how to enhance signal-agnostic searches by
leveraging multiple testing strategies. Specifically, we consider hypothesis tests relying on …
leveraging multiple testing strategies. Specifically, we consider hypothesis tests relying on …
Differentially Private Permutation Tests: Applications to Kernel Methods
Recent years have witnessed growing concerns about the privacy of sensitive data. In
response to these concerns, differential privacy has emerged as a rigorous framework for …
response to these concerns, differential privacy has emerged as a rigorous framework for …
A practical guide to statistical distances for evaluating generative models in science
Generative models are invaluable in many fields of science because of their ability to
capture high-dimensional and complicated distributions, such as photo-realistic images …
capture high-dimensional and complicated distributions, such as photo-realistic images …
[HTML][HTML] A uniform kernel trick for high and infinite-dimensional two-sample problems
We use a suitable version of the so-called” kernel trick” to devise two-sample tests,
especially focussed on high-dimensional and functional data. Our proposal entails a …
especially focussed on high-dimensional and functional data. Our proposal entails a …
A practical guide to sample-based statistical distances for evaluating generative models in science
Generative models are invaluable in many fields of science because of their ability to
capture high-dimensional and complicated distributions, such as photo-realistic images …
capture high-dimensional and complicated distributions, such as photo-realistic images …
Credal two-sample tests of epistemic ignorance
We introduce credal two-sample testing, a new hypothesis testing framework for comparing
credal sets--convex sets of probability measures where each element captures aleatoric …
credal sets--convex sets of probability measures where each element captures aleatoric …
Particle semi-implicit variational inference
JN Lim, AM Johansen - arXiv preprint arXiv:2407.00649, 2024 - arxiv.org
Semi-implicit variational inference (SIVI) enriches the expressiveness of variational families
by utilizing a kernel and a mixing distribution to hierarchically define the variational …
by utilizing a kernel and a mixing distribution to hierarchically define the variational …
Revisit Non-parametric Two-sample Testing as a Semi-supervised Learning Problem
Learning effective data representations is crucial in answering if two samples X and Y are
from the same distribution (aka the non-parametric two-sample testing problem), which can …
from the same distribution (aka the non-parametric two-sample testing problem), which can …
Practical Kernel Tests of Conditional Independence
We describe a data-efficient, kernel-based approach to statistical testing of conditional
independence. A major challenge of conditional independence testing, absent in tests of …
independence. A major challenge of conditional independence testing, absent in tests of …
Dwarf galaxies imply dark matter is heavier than
Folk wisdom dictates that a lower bound on the dark matter particle mass, $ m $, can be
obtained by demanding that the de Broglie wavelength in a given galaxy must be smaller …
obtained by demanding that the de Broglie wavelength in a given galaxy must be smaller …