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 34
Limits of Statistical Assurance for Embodied and Agentic AI under Adaptive Adversaries
Finite safety assessments cannot exclude an adversary that preserves unattacked behavior until certification and activates afterward.Discrete Diffusion Models are Minimax Optimal Under Sparsity
Discrete diffusion models have emerged as a leading alternative to autoregressive models for generating text and other categorical data, yet their theoretical foundations remain less developed than…Loss Curvature as a Sampling-Free Proxy for Memorization in Diffusion Models
Understanding the mechanism behind why diffusion models memorize their training data remains an open question in generative modeling.What Information Buys in Active Inference
Information-theoretic exploration is of primary interest in methodological studies of reinforcement learning (RL) and computational cognitive neuroscience, while the established RL theory builds…Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models
Compartment models of infectious diseases are often linear in coefficients that are physical parameters or products or ratios of them.Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments
Learning an individualized treatment rule (ITR) with many treatments requires estimating treatment-specific effects from limited observations per arm.Student Copula Markov Networks
This paper extends the applicability of probabilistic graphical models to non Gaussian data.Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent
Full waveform inversion (FWI) is a powerful tool for high-resolution subsurface model reconstruction, but it remains highly nonlinear and ill-posed, especially when the initial model is inaccurate…Embedding Geometry Shapes LLM Routing: Tracing Failure Modes and Improving Generalization
With the growing number of LLMs available, selecting which one to use for a prompt has become a key question.Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings
While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited.Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation
Fairness audits with several sensitive attributes are often performed one coordinate at a time.Limit Theory for Online Expectile Estimation under Short and Long Range Dependence
Expectiles are tail-sensitive distributional summaries with important applications in financial risk measurement and forecasting, yet statistical theory for their online estimation from dependent…A Nonlinear Time-Series Theory for Constant-step SGD with Momentum
Constant-step stochastic gradient descent with momentum (SGDM) augments the parameter recursion with a momentum state that carries geometrically decaying memory of past stochastic gradients.Backward Conformal Prediction under Covariate Shift
Backward conformal prediction (BCP) fixes the size of a prediction set and estimates its miscoverage, but assumes calibration and test data are exchangeable.Distributional Regression for Arbitrary Response Variables Using ReLU Neural Networks
This paper develops a unified framework for distributional regression with arbitrary response variables.Equilibrium Learning in Unbounded Stochastic Games with Gaussian Transitions
Learning equilibria from data is difficult when several decision makers interact indefinitely and the system state has no prescribed bounds.Exploration strategies for Gaussian process bandit optimization with inference regret guarantees
Existing cumulative regret guarantees for Bayesian optimization (BO) do not necessarily characterize the quality of the algorithm's final output.When Sample-Splitting Choices Matter in TMLE and AIPW: Cross-Fitting, Targeting, and Repetition
Variants of targeted maximum likelihood estimation (TMLE) and augmented inverse-probability weighting (AIPW) with machine-learning nuisance models differ in which observations train them, how bias…Moment-Based Anomaly Detection in High-Dimensional Mixed Data
We propose an anomaly detection method for high-dimensional mixed-type data that maps heterogeneous features to a common scale via standardized moment-based transformations, yielding interpretable…Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition
The estimation of heterogeneous treatment effects is a central objective of causal inference.