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 22
Active Pilot Sampling for Multi-fidelity Estimation in Stochastic Optimization
Stochastic optimization with high-fidelity nonlinear models is often computationally prohibitive because each objective evaluation requires sampling-based uncertainty quantification inside an outer…Bayesian Neural Networks via Dirichlet Process Priors on the Data-Generating Distribution for Randomized Value Functions in Deep Reinforcement Learning
Bayesian neural networks (BNNs) offer a principled framework for quantifying epistemic uncertainty.From POMDP Theory to Deep RL with Particle Filters
Model-based reinforcement learning in partially observable environments requires inferring hidden states, learning latent dynamics, and planning under the resulting belief.Ask to Learn: Query-Based Collaborative Reinforcement Learning
Offline reinforcement learning is fundamentally limited by the coverage of the available data, while collecting additional trajectories through online exploration may be costly, unsafe, or infeasible.Divide-and-Conquer Neural Bayes Estimation for Spatial and Spatiotemporal Point Process Models
Spatial and spatiotemporal point process models are widely used to analyze event data across many scientific disciplines, but likelihood-based Bayesian inference is often computationally prohibitive…BARRR: Bidimensional Augmented Reduced Rank Regression for Multi-View Multi-Cohort Data
Bidimensional data integration across multiple cohorts (i.e., sample groups) and views (i.e., sources or modalities) is crucial in biomedical research to derive holistic insights from heterogeneous…Pattern Intelligence: Unsupervised, LLM-Interpreted Discovery of Systematic Underpayment and Denial Patterns in Healthcare Revenue Cycle Management
Healthcare providers lose substantial revenue to systematic payer underpayments and claim denials, yet identifying the recurring, correctable patterns behind them remains largely manual and does not…Privacy Accounting for Empirical Denoisers
Diffusion models have emerged as the foundation of modern generative systems, yet their high memorization capacity raises privacy concerns.Practical Advantages of Online Conformal Prediction for Data Streams
Full conformal prediction offers finite-sample marginal coverage guarantees under exchangeability, but exact computation can be costly, while its classical guarantees do not directly extend to…Annealed correlation and quenched noise sensitivity in biased deep neural networks
In earlier work by Jonasson, Steif and Zetterqvist (2023), fully connected deep neural networks with layers of width $n$, i.i.d.\ standard Gaussian weights, sign activations and no bias terms were…Optimal aggregation in multiclass classification
This paper studies the problem of aggregation in multiclass classification, generalizing existing results for binary classification.When Does Training on Synthetic Data Provably Work?
High-quality training data are a major bottleneck in machine learning, motivating increasing use of artificially generated data.Quantifying the Threat: A Bayesian Probabilistic Framework for Heavy Metal Health Risks among Children in the Densu Basin, Ghana
Groundwater in Ghana's Densu River Basin remains a key domestic water source, but multi-metal contamination threatens children's health.Near-Optimal Regret Bounds for UCB-Style Gaussian Process Bandits via Data Thinning
Gaussian process upper confidence bound (GP-UCB) has been widely studied, though its known regret upper bound is suboptimal.Predicting silent failures of learned inference under restricted access: what measurement physics and prior information explain
Learned priors are used to reconstruct regions that measurements barely reach, and to report uncertainty there.Where Has the Foreign Signal Gone?
We compare the relation between sentiment noise in the Twitter feed and losses for stocks of domestic and foreign firms.Geometry-Aware Block Selection for Sparse Variational Gaussian Processes
Sparse variational Gaussian processes (SVGPs) are a widely used, principled approach to scalable Gaussian process modelling.Debiased Prior-Data Fitted Networks for Amortized Bayesian Semi-Parametric Inference
Prior-data fitted networks (PFNs) have recently gained popularity as an end-to-end approach to amortized Bayesian inference.Stabilizing Filters for Graph Signal Generation
Generating graph signals is an important task in machine learning, and incorporating the underlying graph into a generative model enables structurally-informed distribution modeling.Confessions of a Masked MDP
In reinforcement learning, one presumes the action set to be known ahead of time, and, thus, one must know if any state-action pairs are not permissible ahead of computations.