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 2
Offline Learning of Reserve Prices from Censored Bids
We study how a seller can learn a reserve price for a second-price auction from censored historical bid data.Separation Is a Riesz Energy: How Many Environments Does ICA Need?
Can independent components be recovered when all of them are Gaussian?Flexible Intensities Matter: A Comprehensive Re-evaluation of Hawkes Processes and Neural Temporal Point Processes
Temporal point processes (TPPs) are the canonical framework for event sequence data, with applications spanning seismology, social media, and electronic health records.Understanding the Out-of-Distribution Generalization of Chain-of-Thought Reasoning in LLMs
Chain-of-Thought (CoT) reasoning substantially improves Large Language Models (LLMs), yet its out-of-distribution (OOD) generalization mechanism remains theoretically underexplored.Risk-Sensitive Non-Parametric and Kernel Bandits
We study non-parametric bandits in which arms are evaluated using Cumulative Prospect Theory (CPT) rather than expected reward.Finite-width shift of the edge of chaos in deep neural networks
Deep neural networks train best when they are initialised at the edge of chaos, the critical line in the plane of weight and bias variances on which the distance between two inputs neither grows nor…TestBED: An Open-Source Toolkit for Bayesian Experimental Design
Bayesian experimental design (BED) is a principled framework for intelligent data acquisition with applications across a wide range of scientific and industrial domains.Regularization vs. Convolution in Kernel-Based Hypothesis Testing
The maximum mean discrepancy (MMD) test is a popular nonparametric test on general domains.Fundamental Limits of Coherent Imaging in The Presence of Atmospheric Turbulence
We study the fundamental limits of jointly estimating the signal amplitude and the atmospheric phase distortion in complex-valued multi-look coherent imaging, where the measurements are corrupted by…Message-passing graph persistence
Message-passing graph neural networks (GNNs) are limited in expressive power by the one-dimensional Weisfeiler-Leman test.Alignment Myopia in Generative AI
Current generative AI alignment relies on a myopic paradigm that prioritizes immediate conversational satisfaction.Regression with Many Generated Controls
We consider linear regression when some covariates are endogenous, a variable sufficient to control for endogeneity exists but is latent, and the practitioner can generate many proxies for the…ToroFit: Fitting Wedge Sums of Tori to High-Dimensional Data
Data arising in scientific applications often exhibits complex nonlinear structure.Bayesian Tensor Train Kernel Machines with Automatic Model Complexity Inference
Tensor Network (TN) kernel machines accelerate supervised learning by using low-rank TNs to reduce computation and memory.ShapleyOG: Fast Adaptive Shapley Value Estimation with Odd Gaussian Processes
Shapley values are a central tool for attribution in machine learning.Task-Calibrated Random Features via Influence-Based Error Cancellation
Random features approximate kernel integrals by Monte Carlo, yet only selected frequency-sampling errors affect a downstream task.Double Machine Learning for Panel Data: Identification, Specification, and a Bank Regulation Effect
This paper examines robustness and causal interpretation of panel estimates using double/debiased machine learning.Nonparametric GARCH: An Iterated Neural Estimator
We study nonparametric estimation and forecasting for GARCH-type processes and propose an iterated neural estimator that alternates between estimating the recursion map and updating the unobserved…Descending Everest, Part II: Oracle Complexity of Minimizing a Sum of Convex $(L_0,L_1)$-Smooth Functions
We study the minimization of the average of $n$ convex $(L_0,L_1)$-smooth functions: the Hessian norm of each function is bounded by an affine function of its gradient norm.Statistical Machine Learning for Medicinal Plant Leaves Classification
Medicinal plants are usually identified using practitioners' experience, but reproducible, image based methods can complement this knowledge.