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 47
Scalable Regularized Vector Multiplicative Error Models for Positive-valued Financial Time Series
The logarithmic multiplicative error model (log-vMEM) has been useful in modeling and forecasting multivariate positive-valued financial time series.One-Shot Private Confidence Regions via Resampling
We propose a simple framework for constructing differentially private confidence regions \textit{in one shot}, i.e., by adding noise only to the final resampling quantile instead of privatizing the…Information-Dense Synthesis for Molecular Discovery
Machine learning can accelerate molecular discovery by designing molecules and planning experiments.Reinforcement Learning for Hierarchical Reasoning Rewards: Minimax-Optimal Rates with Transformers
Reinforcement learning (RL) has become a standard tool for post-training language models on reasoning tasks, where the policy is updated by reward feedback while exploring the space of responses.When does conformal calibration need censoring weights? Cause-of-failure prediction sets under competing risks
Split conformal prediction sets for competing-risks labels at a fixed horizon require calibration labels that right censoring can leave unobserved.Classifications in modular restricted Boltzmann machines
We consider a modular associative neural network made of $L$ Hopfield models (HMs), coupled so that intra-module interactions are Hebbian and inter-module interactions are anti-Hebbian; this…Early Memory Selection for Balanced Adam
We propose a method for choosing the shared memory parameter $β_1=β_2=β$ in Adam from a short pilot training.Feature Information Dynamics in Diffusion
Diffusion models generate data through a continuum of denoising problems, and are widely observed to reveal coarse structure before fine detail.Spectral Recovery of Point Clouds from Noisy Geometric Graphs
We study the problem of recovering low-dimensional latent geometry from a random geometric graph generated by noisy, high-dimensional data.Steering Diffusion Models to Rare Events with Sequential Monte Carlo
Diffusion models are increasingly used as surrogates for expensive simulators in weather prediction, molecular dynamics, and materials design.Prediction-powered inference for time series across space
The following motif is common in spatiotemporal settings: we have a sequence of covariate and label pairs observed for a relatively short, recent time period.Slow Beats Fast at the Kesten-Stigum Threshold: Minimax, Fisher-Information and Belief-Propagation Characterizations of the Information-Computation Gap in Sparse Stochastic Block Models
We study community recovery in the sparse symmetric stochastic block model with $q$ communities, average degree $d$ and signal strength $λ$ through statistical decision theory and Fisher…Trust-Region Optimization for Smooth Potential-Interaction Energies in Wasserstein Space
Finding low-energy configurations of interacting particles and approximating probability distributions lead to the minimization of potential-interaction energies in Wasserstein space.Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization
Black-box optimization problems are ubiquitous across science and engineering, often dealing with expensive objective functions.The Best Optimizer Depends on Batch Size
A plethora of new adaptive optimizers are designed to efficiently estimate and use minibatch gradient statistics to shape parameter updates, but they are typically benchmarked at a single batch size.LASER: Latent Space Adjoint Matching for Support-Constrained Entropy-Regularized Offline RL
While offline reinforcement learning (RL) enables policy optimization from static datasets without costly online interaction, it remains bottlenecked by the risk of executing out-of-distribution…Graph-monotone entrywise guarantees for MLE and Rank Centrality on general comparison graphs
Pairwise comparisons are widely used to infer latent scores and identify top-ranked items.Careful Judge: Safe and Efficient Human-AI Collaborative Decision Making
In human-AI collaborative decision making, human review can prevent unsafe AI decisions, but each human judgment is costly.Quadratic Weak-to-Strong Generalization in Random Feature Networks via Random Matrix Theory
Weak-to-strong generalization is the phenomenon where a strong student model trained with labels produced by a weak teacher model is able to generalize better than the teacher.Learning Transition Kernels of Jump-Diffusion Processes with Conditional Diffusion Models
We study the problem of learning transition kernels for time-homogeneous jump-diffusion processes using conditional diffusion models, with the goal of generating new sample paths from training data…