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 19
Transformers with CoT Discover Heavy-Ball Acceleration Through Training
Modern transformers often solve complex tasks by generating and refining intermediate reasoning steps.What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules
In real-world applications, being able to explain why a prediction is made is often as important - if not more so - than achieving high accuracy.Prediction Uncertainty Controlled Computer Model Calibration
Computer-model calibration is usually undertaken in service of prediction, yet neither Bayesian Kennedy-O'Hagan (KOH) calibration nor amortized simulation-based inference is built to control the…Local Discrimination of Binary Classifiers via Localized AUC Functional
Risk prediction models are frequently evaluated with AUC, which means that during evaluation we are blind to performance heterogeneity, which could lead to bad decision-making in clinics.Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions
Deep learning (DL) methods have substantially improved predictive performance across a wide range of downstream prediction tasks, enabling new discoveries in multiple domains.Metric Conformal Prediction via the Conditional Expected Local Radius
We introduce the conditional expected local radius (CELR) and develop a new conformal prediction method for non-Euclidean responses in general metric spaces.Dimension-free regimes for unadjusted Langevin in ridge-separable models: A subspace robust Wasserstein analysis
The Unadjusted Langevin Algorithm (ULA) is widely used for high-dimensional Bayesian inference.SABLE: Scalable Affine Bayesian Latent-space Exploration
Complex objects such as engineering designs and proteins are often difficult to optimise because their design spaces exhibit high-dimensional structure and objective evaluations are expensive.Nonparametric autoregressive estimation of probability distributions with parallel gradient boosting
Nonparametric estimation of joint probability distributions is a core problem in statistics, which is notoriously challenging with high-dimensional data.Diffusion Language Models for Perplexity-Aware Black-Box Attacks
\emph{Continuous-space} attacks find prompts that trigger malicious behaviors, such as overthinking, in black-box LLMs by sampling or optimizing over a low-dimensional Gaussian latent space and…When to Follow the Teacher: Advantage-Based On-Policy Distillation
Online Policy Distillation (OPD) often outperforms offline methods such as Supervised Fine-Tuning (SFT) by training on trajectories sampled directly from the student.SympFormer: Inertial Attention Blocks from Damped Hamiltonian Dynamics on Spaces of Probability Measures
The attention mechanism underpins modern large language models, yet their dynamics remain only partially understood.Reliable AI-Assisted Decisions via Selective Auditing
We consider online monitoring of an AI system that produces a sequence of decisions.Phase Transition from Random Features to Fully Trainable Networks
Constructing accurate and computationally tractable approximation spaces is a fundamental task in scientific computing.Operationalising Weaker Faithfulness: Recovery, Ambiguity and Computational Limits
Variables can predict an outcome jointly while each is individually independent of it, causing structure learners to discard informative links.Learning to Be Monitored: Causal Bounds and Intervention-Aware Post-Training Recipes for Chain-of-Thought Monitorability
Chain-of-thought (CoT) monitoring aims to detect potentially harmful behaviors in large language models (LLMs) from their CoTs.Early learning in large residual networks
We study the early training dynamics of nonlinear residual networks with blocks of zero hidden layers as a model of more general residual networks.Combinatorial Characterizations of the Learnability of Conditional Distributions
We study the PAC-learnability of function classes that map inputs to probability distributions over a finite space.Bayesian Propensity Score Predictive Inference for Extending Trial Findings to Target Populations
The CODIACS Vanguard randomized trial compared a depression care intervention with usual care for patients with persistent depressive symptoms following acute coronary syndrome.Compatible Likelihoods for Flow Matching on Manifolds
On the simplest non-Euclidean manifold, the 1D flat torus, we exhibit a distribution where a recently proposed extension of Riemannian flow matching is globally optimized by a velocity field that…