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 39
Model-free Bayesian Clustering and Multimodal Posterior Summarisation
We develop a model-free, noise-aware Bayesian clustering framework that constructs a martingale posterior over clusterings from a predictive Bayesian perspective.One Thing Leads to Another: The Essentials of Time Series Causal Discovery
Time series causal discovery --- the process of detecting causation between time series --- has its roots in signal processing research from over a half century ago.Learning by Compression: Risk Estimates Attain the Law-of-Large-Numbers Rate
Compression schemes provide a general framework for deriving data-dependent risk bounds for learning algorithms.Gaussian BROJA Partial Information Decomposition in Closed Form
The BROJA partial information decomposition provides an operationally motivated way to separate the information that two sources carry about a target into unique, redundant, and synergistic…Defence against the dark arts of distillation: Unsupervised discovery of subliminal learning
When a language model is distilled from a teacher that shares its base parameters, the student can inherit behavioural traits of the teacher---including misalignment---even from data that is…Probabilistic Forecasting of Electric Vehicle Charging in a Time-varying Hierarchical Infrastructure
Uncertainty quantification for electric vehicle (EV) charging demand is essential to turn the increasing pressure of EV adoption on power systems into a source of flexibility.Beyond Discrepancy: Oracle Drift in Nonstationary Weighted Risk Minimization
We analyze distributional drift in nonstationary weighted risk minimization by decomposing the excess risk into estimation, drift, and approximation components.CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope
Correlation matrices combine nonlinear positive-definiteness constraints with a simultaneous row-column permutation symmetry.Some remarks on the connection between generative diffusion and optimal transport
We discuss three simple connections between generative diffusion and optimal transport.Kernel-based External Validity of Experimental Studies
The external validity of a study is the extent to which its findings generalize to unseen experimental conditions, such as datasets.A Critical Look at Power Sampling when Test Time Sharpening LLMs
Reinforcement learning with verifiable rewards (RLVR) has substantially improved LLM reasoning, yet growing evidence suggests it mainly performs distribution sharpening: concentrating probability…Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields
Latent field models are essential for spatial statistics, but the posterior distribution of the latent field, even with a Gaussian process (GP) prior, is analytically intractable whenever the…Not All Dimensions Are Equal: Outlier-Aware Attention Linearization with Grouped Quadratic Kernels
Linearizing softmax attention offers a route to efficient Transformer inference, but preserving pretrained attention remains challenging.Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks
Neural networks (NNs), while highly flexible and versatile, are often poorly calibrated.How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?
Prior-data fitted networks (PFNs) are trained to approximate the Bayesian posterior predictive distribution (PPD) of a synthetic prior, yet results about the approximation quality are surprisingly…From Pixels to Privacy-Sensitive Features: An Information-Theoretic Framework for Reconstruction Bounds
What can an adversary recover from a model trained with DP-SGD?Constrained Probabilistic Numerics: Gaussian Process Solvers for Linear PDEs under Linear Inequality Constraints
Gaussian process solvers for linear partial differential equations (PDEs) return posteriors rather than point estimates, but they condition only on equality information: the PDE residual, boundary…Finite-Sample Guarantees for Fully Discrete Conditional Diffusion Models
We establish finite-sample guarantees for conditional diffusion models learned from data and implemented with finitely many denoising steps.SLICE: Sequential Likelihood-Free Inference via Conformal E-Values
Many scientific models can be simulated but have intractable likelihoods, making frequentist uncertainty quantification difficult.Hitting-Time Bounds for Asynchronous Q-Learning
We study the last iterate of standard asynchronous Q-learning from a single trajectory of a finite Markov decision process.