stat.ML · 2026-10-05 · No. 134

Machine Learning, 2026-10-05.

8 new papers in stat.ML. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

8 entries

01

Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models

Maksym Tretiakov, Sarah Lucie Filipp, Vincent Fortuin, Ruth Misener, Ruby Sedgwick, James Odgers

stat.ML · cs.LG

Many machine learning methods aim to approximate the lower-dimensional manifold on which the data lives. A desirable feature of such methods is that they should capture the epistemic uncertainty of this learned manifold. One model that achieves this is the Gaussian Process Latent Variable Model, in which a Gaussian Process (GP) mapping from the latent space provides an estimate of the uncertainty of the manifold. However, the effectiveness of...

arxiv.org/abs/2610.03647 · PDF
02

AREX: Affine-Residual Exponential Integrator for Few-Step Sampling in Flow Matching

Shizheng Lin, Soon Hoe Lim, N. Benjamin Erichson

stat.ML · cs.AI · cs.LG

We introduce AREX, a training-free sampler for pretrained flow matching models that uses the target mean and covariance to capture an analytically tractable part of the sampling dynamics. We show that the velocity field of the moment-matched Gaussian target is the $L^2$-optimal affine approximation to the marginal velocity field. This motivates decomposition of the learned dynamics into an affine component over the whole sampling path,...

arxiv.org/abs/2610.03483 · PDF
03

When Is Accuracy Evidence? A Unified Theory of Generalisation, Validation, and Information Fusion

JM Gorriz

stat.ML · cs.LG · physics.data-an

K-fold cross-validation (CV) is widely used as evidence of out-of-sample performance, although folds are neither independent experiments nor equally informative under heterogeneous data. Cross Upper-Bound Validation (CUBV) replaces point-wise CV accuracy by conservative upper bounds on true risk. Here we generalise CUBV through a single exponential framework in which the moment-generating function of the generalisation gap is controlled by a...

arxiv.org/abs/2610.03465 · PDF
04

Iterating Consistency Models: Stability, Error Bounds and Noise Schedules

Alessio Spagnoletti, Abdul-Lateef Haji-Ali, Andrés Almansa, Alain Oliviero Durmus, Eric Moulines, Marcelo Pereyra

stat.ML · cs.CV · cs.LG

Consistency models (CMs) have become a leading approach for generating high-quality samples in few steps. However, adding steps can improve or degrade sample quality in ways that are highly sensitive to the schedule and that existing theory does not fully explain. To provide accuracy guarantees and guide CM sampler design, we analyze multistep CM sampling as a composition of noising and approximate denoising operators. Under explicit,...

arxiv.org/abs/2610.03414 · PDF
05

DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants

Erik Wikingsson, Martin Andrae, Tomas Landelius, Fredrik Lindsten

stat.ML · cs.LG · physics.ao-ph

Flow- and diffusion-based generative models have recently emerged as flexible and highly efficient forecasting models for dynamical systems. When combined with inference-time guidance, they offer a promising route to high-dimensional non-Gaussian data assimilation (DA), the problem of combining forecasts with observations to estimate latent system states. Existing filters, however, condition on a fixed history and assimilate only the most...

arxiv.org/abs/2610.03314 · PDF
06

SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs

Maria Marchenko, Martin Andrae, Fredrik Lindsten, Christian A. Naesseth

stat.ML · cs.LG · physics.ao-ph

Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution. While highly efficient for long-range prediction, this formulation can suffer from severe error accumulation when used with shorter time steps and does not explicitly encode the locality and temporal continuity of atmospheric dynamics. To address these limitations, we introduce **SDECast**, a Neural...

arxiv.org/abs/2610.03313 · PDF
07

Predictively Oriented Gaussian Process Posteriors

Callum Lau, Jeremias Knoblauch, Louis Sharrock

stat.ML · cs.LG

Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitioners to make a number of design decisions, such as the choice of the kernel and the observation model. Suboptimal choices can produce misspecified models that do not capture the underlying data generating process. We introduce Predictively Oriented Gaussian Processes (PrO-GPs), which treat...

arxiv.org/abs/2610.03201 · PDF
08

Invariance of Clustering Operations in Causal Effect Identification

Jani Nykänen, Otto Tabell, Santtu Tikka, Juha Karvanen

stat.ML · cs.LG

Clustering variables in causal graphs reduces the size of the graph and simplifies causal inference. However, arbitrary clustering can alter crucial causal relations among variables and lead to erroneous conclusions. While the identifiability of a causal effect in the clustered graph implies the identifiability in the original graph under mild conditions, nonidentifiability in clustered graph does not imply nonidentifiability in the original...

arxiv.org/abs/2610.03101 · PDF

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