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 53
A Residual Tree Gaussian Process Modeling Framework for High-Dimensional Data
With the advance of measurement technologies and increasing computing power, large spatial data with heterogeneous structures are often collected over high-dimensional domains.Cross-Fitting Under Nonregularity: Normality and Inference via Locality
Cross-fitting is routine in much of applied research.GTDD: Generative Test-Driven Development for AI Coding Agents with Adversarial Testing
Test-driven development gives AI coding agents executable requirements for implementing software.Shapley-based Structural Analysis of Neural Calibration for Stochastic Volatility Models
Neural network-based approaches have emerged as efficient alternatives to traditional optimization-based procedures for the calibration of stochastic volatility models.Invariance of Clustering Operations in Causal Effect Identification
Clustering variables in causal graphs reduces the size of the graph and simplifies causal inference.Predictively Oriented Gaussian Process Posteriors
Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships.Near-Optimal Convex Optimization with Lazy Second-Order Oracles
This paper studies the complexity of convex optimization using lazy second-order oracles (Doikov, Chayti, and Jaggi, ICML 2023), where an algorithm queries gradients every iteration and Hessians…SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs
Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution.DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants
Flow- and diffusion-based generative models have recently emerged as flexible and highly efficient forecasting models for dynamical systems.Iterating Consistency Models: Stability, Error Bounds and Noise Schedules
Consistency models (CMs) have become a leading approach for generating high-quality samples in few steps.When Is Accuracy Evidence? A Unified Theory of Generalisation, Validation, and Information Fusion
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.AREX: Affine-Residual Exponential Integrator for Few-Step Sampling in Flow Matching
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.Below what training size do deep tabular generators stop beating trivial baselines? A preregistered benchmark on a size ladder of clinical and standard datasets
Deep tabular generative models are benchmarked on datasets with tens of thousands of rows; clinical datasets have hundreds.Broken scale symmetries in undercomplete linear autoencoders
Neural network loss landscapes have many symmetries, which are preserved by gradient flow but broken by finite-stepsize stochastic gradient descent (SGD).When May a Bandit Leave Its Anchor? E-Process-Authorized Thompson Sampling under Non-stationarity
Stationarity rewards memory, but after a change the same history can mislead.Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
Many machine learning methods aim to approximate the lower-dimensional manifold on which the data lives.Planning to Learn
Policy-gradient methods are central to modern reinforcement learning, including LLM post-training.Simulation-Free Learning of Population Dynamics with Wasserstein Lagrangian Residuals
The dynamics of cells, organisms, and fluids are often modeled as probability distributions evolving over time.Targeted Active Learning for Preference-Based Treatment Effects on Multivariate Outcomes
Treatment efficacy is traditionally demonstrated on the basis of a single primary outcome.Probability flow ODEs in score-based and reflected diffusion models
Probability-flow ordinary differential equations (PF-ODEs) are widely used as deterministic samplers for score-based diffusion models.