stat.ML · 2026-06-15 · No. 24
Machine Learning, 2026-06-15.
9 new papers in stat.ML. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
9 entries-
01
Cluster LOCO: Feature Importance For Interpreting Clusters
Claire M. He, Genevera I. Allen
stat.ML · cs.LG · stat.AP · stat.ME
Clustering is widely used for exploratory analysis and scientific discovery, driving insights from market segmentation to biological data analysis, but its outputs can be difficult to interpret, audit, and reproduce as modern datasets become increasingly large and complex. Reliable use of clustering requires understanding which features drive the discovered structure, yet feature-level explanations for clustering remain scarce compared with...
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02
Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias
Annie Ulichney, Amanda Coston
stat.ML · cs.LG · stat.ME
Understanding how a prediction model will perform in a new environment before deployment is essential to preventing harm when algorithms inform decision-making. Two common sources of model performance degradation are (i) covariate shift, where the target covariate distribution differs from the source, and (ii) selective labels, where the observability of outcomes depends on historical decisions. We study pre-deployment model evaluation under...
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03
Nonlocal Bayesian Modeling of Continuous Spatio-Temporal Dynamics
Jaeyeong Lee, Heeyoung Kim
stat.ML · cs.LG
Real-world spatio-temporal forecasting must handle irregular time points, spatially sparse observations, and the need for uncertainty quantification. This setting is often further compounded by nonlocal interactions (long-range spatial coupling). Modeling continuous-space, continuous-time nonlocal dynamics naturally leads to infinite-dimensional integro-differential equations (IDEs), making principled Bayesian inference intractable. We...
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04
Gradient boosting for extremes: sampling theory and application to insurance
Stéphane Lhaut, Olivier Lopez
stat.ML · cs.LG
We develop a statistical learning theory for gradient boosting applied to the estimation of covariate-dependent Generalized Pareto (GP) distributions in the context of Peaks-over-Threshold modeling. After an orthogonal reparametrization of the GP likelihood that diagonalizes its Fisher information matrix, we cast the estimation problem within the Empirical Risk Minimization (ERM) framework and derive non-asymptotic error bounds for the...
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05
Hybrid Uncertainty Sensitivity Analysis Based on the HSIC for High-Dimensional Responses with Aleatory--Epistemic Separation
Shijie Zhong, Jiangfeng Fu, Pengfei Wei
stat.ML · cs.LG
Quantifying the influence of hybrid aleatory and epistemic uncertainties on high-dimensional system responses remains a major challenge in global sensitivity analysis (GSA). Existing Hilbert--Schmidt Independence Criterion (HSIC)-based approaches are primarily restricted to single-output settings and lack a rigorous decomposition of heterogeneous uncertainty sources and their interactions. To address this limitation, a novel double-space...
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06
Anytime-Valid Confirmation of Label-Shift Corrections
Seungjin Choi
stat.ML · cs.LG
In small-batch scientific deployments, labeled target outcomes may be too scarce for reliable shift estimation even when unlabeled target inputs are available. We address the complementary setting where the practitioner has a pre-specified label-shift correction from domain knowledge and asks whether incoming labeled outcomes support it. We show that the per-observation likelihood ratio between a label-shift-corrected predictive and the...
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07
Geometric Domain Adaptation via Optimal Transport for Linear Regression in R^2
Brian Britos, Mathias Bourel
stat.ML · cs.LG · stat.ME
Optimal Transport has become recently a powerful method for domain adaptation by aligning source and target distributions. We study a supervised domain adaptation problem where source and target domains are related by a rotation or a translation or a homothety in $\mathbb{R}^2$. We prove that the optimal transport map recovers the underlying map when using a $p-$norm cost with $p \geq 2$. Based on this insight, we develop a method combining...
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08
A General Framework for Decision Trees via Bregman Divergences
Mathias Bourel
stat.ML · cs.LG · stat.ME
Decision trees are one of the fundamental tools in statistical learning due to their interpretability, flexibility, and their ability to adapt to nonlinear structures. Among them, the Classification and Regression Trees, introduced by Breiman, Friedman, Olshen, and Stone in 1984, became one of the most influential algorithms and remains one of the most widely used methods for classification and regression problems. On the other hand, Bregman...
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09
Adaptive Nucleus Truncation for Long-Form Reasoning
Ousmane Amadou Dia
stat.ML · cs.LG
Sampling plays an important role in long-form language-model reasoning. Over thousands of decoding steps, small changes in the candidate token set can compound into different reasoning trajectories, stability profiles, and final answers. Existing truncation methods such as top-$p$, min-$p$, and fixed top-$nσ$ sampling improve over unrestricted sampling, but they rely on fixed thresholds that cannot adapt to changes in entropy, task...
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