stat.ML · 2026-06-17 · No. 26
Machine Learning, 2026-06-17.
6 new papers in stat.ML. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
6 entries-
01
A Diffusion Approximation for Temporal-Difference Learning with Linear Features under Markovian Noise
M. Forzo, E. Monzio Compagnoni, A. Russo, A. Pacchiano
stat.ML · cs.LG · math.PR
Temporal difference (TD) learning with linear function approximation is a core method for policy evaluation. Its classical continuous-time description is an ordinary differential equation (ODE), which captures the asymptotic mean dynamics but neglects stochastic fluctuations determining the error floor. We introduce a stochastic differential equation (SDE) approximation for linear TD(0) under Markovian noise. The resulting model distinguishes...
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02
Tensor-based second-order causal discovery
Nathan Ouyang, Kexin Wan, Anna Seigal
stat.ML · cs.LG · stat.ME
Causal discovery seeks to uncover the causal dependencies among variables. For this purpose, we propose an algorithm called Tensor-based Second-order Causal Discovery (TSCD). Its input is a tensor obtained from the covariance matrices of observational and interventional data. Assuming the causal dependencies follow a linear structural equation model on a directed acyclic graph (DAG), TSCD outputs the DAG and the functions on its edges,...
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03
Fast Nonparametric Conditional Independence Testing via Two-Stage Regression
Eric V. Strobl
stat.ML · cs.LG · stat.ME
Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration, especially when variables depend on the conditioning set through nonlinear relationships. We introduce BLITZ (Broad-to-Local Independence Testing via residualiZation), a nonparametric conditional independence test designed to run well under a second while maintaining the accuracy needed for the...
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04
Differential Privacy of Gaussian Process Posterior Sampling
Tomasz Maciazek
stat.ML · cs.CR · cs.LG
We study the privacy of releasing posterior sample paths from a Gaussian process (GP) when the entire training set including covariates and responses is private. Unlike standard differential-privacy (DP) mechanisms that add external noise, posterior sampling is random by construction. We show that this intrinsic randomness yields DP guarantees by deriving explicit Rényi-DP bounds for GP posterior sample-path release. The bounds separate...
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05
Geometrical fairness in graph neural networks
Arturo Pérez-Peralta, Sandra Benítez-Peña, Blas Kolic, Rosa E. Lillo
stat.ML · cs.CY · cs.LG
Graph-based learning methods have become increasingly prominent due to their strong performance across diverse applications. Among these, recent frameworks grounded in diffusion processes provide a unifying perspective that extends traditional graph neural network formulations while addressing limitations of standard message-passing mechanisms. Despite these advances, concerns remain regarding the fairness of such models, as they may...
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06
A Bayesian Boolean Matrix Factorization with Application to Copy Number Analysis in Cancer
Adolphus Wagala, Mehmet Samur, Giovanni Parmigiani
stat.ML · cs.LG · stat.ME
Binary data factorization is common, but real-valued methods ignore discreteness and yield hard-to-interpret factors. Boolean Matrix Factorization (BooMF) instead decomposes a binary matrix into two lower-rank binary matrices via logical AND and OR, expressing the data as a Boolean disjunction of interpretable patterns. In cancer genomics, BooMF can reveal coordinated feature changes that may drive tumor evolution, unlike rotational or...
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