stat.ML · 2026-09-15 · No. 114
Machine Learning, 2026-09-15.
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
Quenched Ensemble Sampling
David Yallup
stat.ML · cs.LG · stat.CO
Some of the sharpest challenges in sampling from the energy functions of physical systems arise at phase transitions, where the density of states changes abruptly and many sampling algorithms stall. Nested sampling is a particle method that traverses the density of states under a hard energy constraint and is known to be robust to such transitions, but its application in high dimension is limited by the difficulty of sampling under that...
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02
Learning under Target Shift: Optimal Density Ratio Estimation and Importance-Weighted Regression
Ren-Rui Liu, Zheng-Chu Guo
stat.ML · cs.LG
We study density ratio estimation and importance-weighted regression under target shift with continuous outputs. Under target shift, the conditional distribution of the inputs given the outputs remains invariant across the training and test distributions, while the output marginal distribution may change. Although this problem has been extensively studied for discrete outputs, the continuous setting is substantially less understood: the...
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03
Predictive Likelihood Ratios for Language Model Watermark Detection
Li Ma
stat.ML · cs.AI · cs.LG · stat.AP
Keyed watermark detection tests dependence between observed tokens and pseudorandom variables reconstructed from a secret key. Building on the pivotal framework of Li et al. (2025), we construct predictive likelihood ratios that average over uncertain probability deficits and residual-tail distributions. The aim is robust detection power across alternative specifications without requiring a single signal-strength tuning. A mixture prior...
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04
Graph Matching Relaxations and Amortization for Supervised Graph Prediction
Federico Méndez, Paul Krzakala, Gabriel Melo, Charlotte Laclau, Rémi Flamary, Florence d'Alché-Buc
stat.ML · cs.LG
End-to-end Supervised Graph Prediction (SGP) requires a permutation-invariant loss to compare predicted and target graphs with arbitrary node orderings. Such losses typically involve a costly graph-matching problem. We first study three Optimal Transport relaxations of this problem and show, theoretically and empirically, that the Gromov-Wasserstein (GW) objective is the most suitable for SGP. Then, to avoid solving the resulting inner...
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05
ReLU Neural Network Approximation to Smooth Functional Operator: Dimensional Decay and Error Analysis
Shuhao Jiao
stat.ML · cs.LG
We study the uniform approximation of smooth scalar-valued functionals on an infinite-dimensional separable Hilbert space by deep ReLU neural networks. Writing the functional input as $X(t)=\sum_{d\geq1}ξ_dν_d(t)$, we quantify the importance of coordinate $d$ through $w_ds_d$, where $s_d$ bounds the magnitude of the corresponding basis score and $w_d$ controls the directional Fréchet sensitivity of the target functional. Our constructive...
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06
Conformal Individual Treatment Effect Estimation under Networked Interference
Matteo Zecchin, Osvaldo Simeone
stat.ML · cs.IT · cs.LG
Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual treatment effects under the no-interference assumption. In this work, we relax this assumption by allowing each unit's potential outcomes to depend on other units' treatments and covariates. In this setting, propensity-score reweighting does not restore weighted exchangeability, and existing methods...
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