AI & Machine Learning · Computer Science & AI
Machine Learning (Statistics)
The newest papers in Machine Learning (Statistics) that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
1,749 papers, from 4 September 2026 to 5 November 2026: 1,749 arXiv preprints.
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Newest papers, page 5
Specification-Aware Predictive Uncertainty Decomposition
Classical decompositions of predictive uncertainty, such as the information-theoretic one, are defined for a fixed representation $x$ of the input object.Diagonal Equivalence in State Space Models: Characterization, Retrieval Rank, and Selective Extensions
We classify real and complex state-space models (SSMs) with linear transitions, distinguishing fixed-basis diagonal equivalence from input-output equivalence and tracking coefficient-conditioning…Causal discovery needs explicit epistemic standards
There are many causal discovery algorithms, but few guiding principles for choosing between them.Bagel: A Fast Bayesian Online Changepoint Detection Algorithm for Linear Models via Posterior Merging
Bayesian online changepoint detection provides a natural framework for quantifying uncertainty about whether and when a change has occurred.Poisson Super Learning For Survival And Competing Risks On A Discretized Time Scale
We introduce the Poisson Super Learner, a scalable ensemble method for survival and competing risks prediction with right-censored data.Strictly proper scoring rules for node selection tasks on graphs
In node selection tasks, the goal is to identify a single unknown target node in a graph.On a Class of Non-Local Elliptic-Transport Systems in Score-Guided Probability Flow on $SE(3)$: Score-Singularity Resolution, Space-Time ADN Shift, and Markov Mean-Field Games
We establish mathematical foundations for optimal spatiotemporal vector fields governing continuous score-guided probability flows over bounded submanifolds $M \subset SE(3)$ on the Special…EventGPT: Next-Event Pretraining on Banking Customer Histories
A model pretrained to predict a customer's next event can estimate the probability of future events without training on their labels and provide a reusable representation for supervised downstream…LLMs Depend on Specialist Tools for Out-of-Distribution Generalization in MS/MS Structural Elucidation
A frontier language model was recently reported to solve 1D NMR structural elucidation without tools.Neural Actor--Critic Learning with Minimax Critics and Reflected Langevin Sampling
Actor--critic methods rely on critic gradients to improve continuous-action policies, yet finite-sample guarantees must control both value and gradient errors and their propagation through the actor.Subpolynomial-Query Proximal Sampling Beyond Log-Concavity
We study the gradient query complexity of sampling from a density $π(x)\propto\mathrm{e}^{-V(x)}$ on $\mathbb{R}^d$, where $V$ has a $β$-Lipschitz gradient but need not be convex.Error Accumulation in Second-Order Sequential Machine Unlearning
Machine unlearning seeks to remove the influence of deleted training data without full retraining.Tilted Convexity and an Exact Offline-Online Separation
Convexity has three equivalent descriptions: support by affine functions, monotonicity of the subdifferential, and increasing secant slopes.ST-GFM: Spectral-Temporal Graph Forecasting with Measured Perturbation Bounds and Masked Spectral Reconstruction
Graph forecasters for sensor networks are trained in one regime and deployed in others, where sensor coupling and signal frequency content both change.ParaDeAl: Parallel Descent Algorithms for Sparse Statistics
Best subset selection is a central problem in statistical regression, drawing increasing attention from the broader optimization and machine learning communities.Efficient Closure Learning in Dynamical Systems: A Comparison of Data-Assimilated and Embedded Approaches
Increased computing power has transformed computational science and improved predictive capabilities for complex systems, but many phenomena still exceed current simulation capabilities or require…Benchmark Prediction with Sparse and Unpaired Cached Responses
Evaluating a generative model on all queries from all available benchmark tasks is prohibitively expensive.PanelGP: A Probabilistic Factor Model for Asset-Return Panels
We develop PanelGP, a conditional factor model for large panels of stock returns in which firm characteristics determine the factor loadings and economic conditions determine the expected factors.Denoising Is Stein: Moment Access and a Resource Hierarchy for Learning Hidden Structure in Diffusion Models
We study score learning from denoising pairs when the data contain a hidden non-Gaussian component.StreamLTT: Risk Certification for Policy Selection under Stream Dependence
Learn-then-Test (LTT) provides a general framework for certifying deployment policies against a prescribed risk target, but its standard formulation targets a fixed population risk estimated from…