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 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
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).
arXiv Machine Learning
1d ago
How Many Samples Are Needed to Determine Causal Direction? Sharp Minimax Bounds for Bivariate LiNGAM
arXiv:2608. 15840v1 Announce Type: cross Abstract: We study how many observations are needed to determine the causal direction between two linearly related variables.
arXiv Machine Learning
Jul 22
Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics
arXiv:2607. 18559v1 Announce Type: cross Abstract: Gaussian graphical model selection is usually studied under independent sampling, but in many applications the data arise as a single trajectory of a dependent stochastic process.
arXiv AI
22h ago
On the Subgaussianity of Quantized Linear Maps: An AI-Assisted Note
arXiv:2605. 27563v2 Announce Type: replace-cross Abstract: We prove an elementary bounded-differences inequality for functions of non-isotropic Gaussian vectors.
arXiv Machine Learning
Jun 30
Active Quantum Kernel Acquisition for Gaussian Process Regression
arXiv:2606. 28833v1 Announce Type: new Abstract: Quantum kernel estimation on near-term hardware is shot-budgeted: every entry of the kernel Gram matrix is a Bernoulli expectation that must be sampled with a finite number of circuit executions.
arXiv AI
Jun 2
Transferring Information Across Interventions in Causal Bayesian Optimization
arXiv:2606. 01457v1 Announce Type: new Abstract: Bayesian optimization is a popular way to optimize expensive systems, where every experiment, simulation, or intervention costs time or money.