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.
Get the few papers worth your time in Machine Learning (Statistics) every morning.
Follow Machine Learning (Statistics) and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 14
Integral Probability Boundaries
Bounds based on Integral Probability Metrics (IPMs) are ubiquitous in machine learning and statistics, appearing, e.g., in generalization, alignment, fairness, and privacy.Efficient Deep Gaussian Process Surrogates via Mini-Batch Latent MAP Estimation
Deep Gaussian processes (DGPs) are flexible surrogate models for computer experiments with nonstationary response surfaces, but inference over their latent representations remains computationally…Conformal Predictive Distributions for Contextual Order Fulfillment Optimization
Ship-to-home fulfillment requires a retailer to select a fulfillment location and carrier before the delivery time is known.Interpretable Feature Discovery from Unstructured Data Using LLMs and Statistical Choice Models
Feature extraction from unstructured data typically relies on opaque embeddings unsuited to interpretable decision-making.Intrinsic Information Rates of Summaries in Simulation-Based Inference
Simulation-based inference often encodes observations $Y$ into a fixed-dimensional summary $S$, with the posterior conditioning only on $S$.Designing Customer Feedback for Subscription Boxes
Problem definition: Subscription box services often ask customers for ratings before choosing what to send.When Does Data Value Reduce to Class Balance? A Coverage View of Per-Point Data Valuation
Per-point data-valuation scores -- Data Shapley, influence, leverage -- are routinely used to select training data: keep the points with the top $k$ scores.The Noise-Transmission Burden of Diffusion Network: Why Image Prediction Can Outperform Noise Prediction
Clean-image prediction ($x$-prediction) and noise prediction yield equivalent population-optimal denoisers, yet can perform very differently in finite diffusion networks.Conformal Novelty Detection via Data-enhanced Localization
Novelty detection is an important area of research in both statistics and machine learning.Macro Mondrian Conformal Risk Control
Conformal Risk Control (CRC) extends conformal prediction beyond the coverage setting to the control of general monotone loss functions, in a distribution-free and modelagnostic fashion.Risk-Aware Output Control Should be the Focus of Machine Unlearning
Well-documented failure modes of machine learning (ML) systems, including bias, toxicity, hallucination, and leakage of sensitive information, has driven the rise of machine unlearning (MU).AutoEnergyDesign: An LLM-Driven Agent for Automatic Energy System Design Optimization
Designing building energy systems is a high-dimensional, mixed-integer search problem: when each design is evaluated by a year-long simulation, black-box optimizers typically need hundreds of…Sequential versus fully conditional specification for tree-based synthesis of health data
Disseminating synthetic data in place of original ones is a promising way to enforce privacy while complying with open science principles, especially in the healthcare domain.Identities, Not Pairs: Distribution-Free False-Accept Control for Embedding-Based Verification
Embedding-based verification often calibrates a false-accept threshold from all impostor pairs among $m$ identities, although these pairs share identities.Temporal-Difference Calibration of Black-Box Simulators
We develop a derivative-free temporal-difference (TD) framework for calibrating black-box simulators using one-step transitions from a target system and noisy simulator-generated continuation values.Beyond Wasserstein Proximity: Finding Better Transfer Sources
Source selection often favors similar data, yet marginal proximity need not identify the most useful pretraining source.On the asymptotic behaviour of $α$-divergence generalized posteriors
We study the asymptotic behaviour of the solutions to the unconstrained generalized variational inference problem regularized by an $α$-divergence.A knowledge-driven nonconvex square-root regularization framework for joint group sparsity and structured smoothness
Structured sparse learning is a leading paradigm for high-dimensional data modeling, as it enables rigorous integration of domain-validated structural priors into the learning process.Prior Predictive Matching for Bayesian LoRA
Bayesian variants of Low-Rank Adaptation (LoRA) provide principled uncertainty estimation when fine-tuning large foundation models, but require specifying the prior over the low-rank weight updates.Minimax Rates for Conditional Generative Learning under Wasserstein Loss
We study minimax rates for conditional generative learning under integrated Wasserstein loss, with Hölder continuous conditional laws and an unrestricted covariate distribution.