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 74
Stochastic Flow Map for Count Data
High-dimensional count data are common in scientific applications, but most diffusion and flow models are designed for continuous or categorical data, and generation often requires many sequential…Bayesian Filtering in Physical Systems via Test-time Trained Flow Matching
Bayesian filtering provides a principled framework for online state estimation under uncertainty, yet its application to systems with high-dimensional states and complicated posterior distributions…Distributional Balancing with Machine Learning for Clinical Trial Augmentation Using Real-World Data
In clinical trials, randomization of treatment and control groups is typically used to ensure the groups have similar covariate distributions on average, resulting in unbiased causal effect…Decoupled Causal Discovery
Causal discovery from observational data is a fundamental yet challenging task in scientific research.Optimal High-Order Methods for Solving Monotone Variational Inequalities
We study second- and higher-order methods for solving smooth monotone variational inequalities (MVI).PACE: Plug-and-Play Contextual Embedding for Feature Screening with Pretrained Tabular Foundation Models
In high-dimensional tabular learning, feature screening provides a lightweight, model-agnostic way to remove irrelevant features before model fitting.TEMPER: Temporal Encoder-Masked Probabilistic Ensemble Regressor for Time-Series Forecasting
Probabilistic forecasting requires accurate central predictions and calibrated uncertainty estimates.Belted Engression: Sufficient Dimension Reduction for Generative Distributional Regression
Modern conditional generative models face significant challenges when learning complex covariate dependencies.Iterative Atom Refinement: A Monotonicity Principle for Dictionary Learning
Dictionary learning seeks to recover an unknown dictionary $A$ from observations ${\bf y}_i = A{\bf x}_i$ with sparse coefficient vectors ${\bf x}_i$.On Generalized Naive Bayes with Continuous Features
The Generalized Naive Bayes (GNB) model was introduced for discrete and categorical random variables as an extension of classic Naive Bayes.Density-Ratio Rescoring for Imbalanced Classification Using Raking Duals and Classifier Scores
Density-Ratio Rescoring (DRR) augments a classifier trained at the original class prior with a survey-raking dual score.Sparse Regression Distilled from a Single Robust Fit
Robust linear fits can resist response contamination yet remain too dense or unstable for useful global explanations.The Probabilistic Structure of Large Language Models
This paper presents a probabilistic perspective on large language models (LLMs), developed with the aim of bringing together, in a single self-contained account, tools that are usually treated…Subspace Learning with Interval-Censored Likelihoods for Dequantizing Percept PC LFP Snapshots
Implanted neurostimulators that sense local field potentials now enable chronic electrophysiology based biomarker tracking in patients at home.Multi-Armed Bernoulli Bandits via Minimax Single-Arm Stopping
We develop an index policy for finite-horizon Bernoulli multi-armed bandits from minimax solutions to single-arm bandit (SAB) problems.On attention heads and bilinear forms
We study the symmetric and antisymmetric parts of bilinear forms in the attention heads of trained large language models.Toscani-Fourier Distance on Probability Measures: Wasserstein Control, Topological Equivalence on Model Classes, and Duality
Comparing probability measures in machine learning trades transport geometry against computational cost: Wasserstein distances encode the geometry of $\mathbb R^d$ but require solving a transport…Conformal Robustness in Prediction-Driven Decision-Making
Modern prediction-driven decision systems often rely on black-box predictors, but a point forecast alone does not provide the uncertainty scale required for robust downstream decision-making.Bayesian Deck-of-cards-based Ordinal Regression with Sequential Preference Elicitation
The Deck-of-cards-based Ordinal Regression (DOR) infers a value function from a ranking of reference alternatives in which the Decision Maker (DM) inserts blank cards between consecutive levels to…FREESIA: Covariance-Aware Posterior Transport for Expressive and Scalable Data Assimilation
Data assimilation aims to infer the state of complex dynamical systems based on observational data.