arXiv Machine Learning
Jun 16
Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces
arXiv:2606. 15393v1 Announce Type: cross Abstract: Scientific discovery relies on large-scale hypothesis testing.
The Benjamini--Hochberg Procedure Can Fail to Control the FDR for Correlated Two-Sided Gaussian Tests
arXiv:2607. 12208v1 Announce Type: cross Abstract: We show that the Benjamini--Hochberg procedure can fail to control the false discovery rate (FDR) at its nominal level for correlated two-sided Gaussian $p$-values.
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arXiv Machine Learning
Jul 1
Multiple Testing of Linear Forms for Noisy Matrix Completion
arXiv:2312. 00305v3 Announce Type: replace-cross Abstract: Many important tasks of large-scale recommender systems can be naturally cast as testing multiple linear forms for noisy matrix completion.
arXiv AI
Jul 13
How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding
arXiv:2607. 09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference.
arXiv Machine Learning
Jun 18
Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference
arXiv:2605. 20726v2 Announce Type: replace-cross Abstract: Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold.
arXiv Statistics ML
4d ago
Bentkus-type asymptotic e-values
arXiv:2606.06332v2 Announce Type: replace-cross Abstract: Asymptotic e-values are emerging as a powerful alternative to asymptotic p-values, particularly in post-hoc inference and multiple testing, w...
arXiv Machine Learning
Sep 23
When are bosonic Gaussian states classical to learn?
arXiv:2609.26705v1 Announce Type: cross Abstract: A fundamental question in physics is: When does classical behavior emerge from quantum systems? Bosonic Gaussian states provide a natural setting to...
arXiv Machine Learning
Jun 2
Efficient Hamiltonian, structure and trace distance learning of Gaussian states
arXiv:2411. 03163v4 Announce Type: replace-cross Abstract: In this work, we initiate the study of Hamiltonian learning for positive temperature bosonic Gaussian states, the quantum generalization of the widely studied problem of learning Gaussian graphical models.
arXiv AI
Sep 25
FB-GDM: Fully-Bayesian Guided Diffusion Models for High-Dimensional Linear Inverse Problems via Unsupervised Variational Inference
FB‑GDM is a fully‑Bayesian guided diffusion method that eliminates the need for task‑specific hyperparameter tuning in linear inverse problems. It derives a closed‑form conditional score from a Gaussian approximation of ΦGDM, treating two precision parameters as latent variables inferred via variational inference at each reverse step. Experiments on CelebA‑HQ show that FB‑GDM outperforms ΦGDM at its nominal setting, matches a ground‑truth‑calibrated oracle within 0.1 dB, and remains robust to changes in the forward operator, noise level, or image distribution without hallucinations.
arXiv Machine Learning
Jun 2
Near-Optimal Private Tests for Simple and MLR Hypotheses
arXiv:2601. 21959v2 Announce Type: replace-cross Abstract: We develop a near-optimal testing procedure under the framework of Gaussian differential privacy for simple as well as one- and two-sided tests under monotone likelihood ratio conditions.
arXiv Statistics ML
Aug 21
Reliable conformal novelty detection at the decision boundary
arXiv:2601. 02610v3 Announce Type: replace-cross Abstract: Novelty detection via conformal $p$-values and BH procedure provides distribution-free global false discovery rate (FDR) control.
arXiv AI
Jun 2
Principle-Evolvable Scientific Discovery via Uncertainty Minimization
arXiv:2602. 06448v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based scientific agents have accelerated scientific discovery, yet they often suffer from significant inefficiencies due to adherence to fixed initial priors.
arXiv Machine Learning
Jul 10
Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables
arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).