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 73
Causal Bayesian Optimization: Foundations, Methods, and Applications
Causal Bayesian Optimization (CBO) combines causal inference with Bayesian optimization to enable sample-efficient intervention selection in systems with causal structure.Model-Agnostic Feature Selection via LOCO-Guided Adaptive Minipatch Sampling
Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important features, remains challenging.OSCAR: Order-aware Scoring and Calibration for AI Rankings
Judge-specific sensitivity is useful for aggregating pairwise LLM evaluations, but its interpretation depends on which systematic presentation effects the ranking model includes.Adversarially Robust PAC Learning with Optimal VC Rates
We study the problem of \emph{adversarially robust} PAC learning.A Distributional Optimisation Perspective on Combining Models in Deep Learning
Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule used to combine them are typically chosen…A Three-Way Testing Framework for Quantifying Epistemic Calibration Uncertainty in SBI
Current experimental scientists increasingly rely on simulation-based inference (SBI) to invert complex models with intractable likelihoods.Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models
Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs).Tensor Completion using Subspace Information
Tensor completion has attracted significant attention in both applications and theoretical research.Beyond Point Prediction: Artificial Representative Trees with Uncertainty
Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable.Identifying Representational Biases in Datasets Using PCA: A Max-Disparity Partition Framework
Principal Component Analysis (PCA) minimises aggregate reconstruction error, which can inadvertently represent majority subgroups with substantially higher fidelity than minority subgroups.Conformalized Quantile Regression and Minimax Limits of Fixed-Score Calibration under Known Covariate Shift
In this paper, we study nonasymptotic $L^p$ error bounds for interval length and conditional coverage in split conformalized quantile regression (CQR).Selective Inference for CART with Binary Outcomes
Binary classification trees select subgroups using the same outcomes later used to assess their differences.JAREX: An Acquisition Function for Multi-Objective Algorithmic Process Characterization
Pharmaceutical process characterization is central to Quality by Design because it defines how variations in process parameters affect the ability to meet product quality specifications, thereby…The Informational Content in Lepto-Variance and Its Relation to Higher Moments
Lepto-regression is defined as the machine learning process of constructing a Regression Tree of a target feature on itself.Variational objectives for amortized Bayesian inference in inverse problems: The role of posterior conditioning
Variational autoencoders (VAEs) offer an efficient approach to amortized Bayesian inference for inverse problems, but posterior accuracy can depend strongly on the choice of variational…Empirical Auditing of Edge-Private Graph Generators
We empirically audit privacy leakage by testing whether outputs from edge-neighbouring inputs remain distinguishable, using statistically valid lower bounds on the privacy loss witnessed by our…Penalized Nonreversible Langevin for Constrained Sampling
We propose penalized nonreversible Langevin algorithms for sampling from $π(x)\propto e^{-f(x)}\mathbf 1_{\mathcal C}(x)$, where $\mathcal C\subset\mathbb R^d$ is a compact convex set.PICPIs: Prediction-Interval-Conditional Prediction Intervals
A classical question in statistics is which observable quantities to condition on when drawing inferences about unobservable targets.A Practical Recipe for Semi-Supervised Federated ASR: Online Pseudo-Labels with Server Update Stabilization
Semi-supervised federated learning (SSFL) trains models on clients' unlabeled data using a teacher to generate pseudo-labels, with a small labeled seed dataset on the server.Continuous Optimization for p-adic Models
We present the first method for native, continuous gradient descent for machine learning models with $p$-adic parameters.