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 75
Repulsive normalizing flow mixtures for adaptive importance sampling: reliability analysis of complex systems
Accurate rare-event estimation can be computationally expensive.Diagonalized Attention for Individualized Regression: Latent-Row Localization and Prediction
Modern text and image representations are often matrix-valued, with rows corresponding to tokens, patches, or other local feature vectors.Sparse Identification for Automatic Large-Scale Screening: A Constraint-Aware Framework with Ultra Fast Decoding Algorithm
In the early stages of a pandemic, identification of a small number of infected individuals through large-scale screening is critical for pandemic control, yet remains challenging under limited…Robust Dual-Regularized Variable Selection under Outlier Contamination
Real data often contain unusual observations that can exert disproportionate effects on variable selection, especially in complex predictor settings.Brownian Heads for Deep ReLU Representations: Activation Mass and the Cost of Same-Sample Selection
Deep representation learning often selects hidden features and fits the final predictor on the same sample, so fixed-feature analysis performed after selection can omit selection cost.Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling
We revisit Breiman's observation that reducing inter-tree correlation without weakening individual trees can improve random forests.Multi-Domain Clustering via Measure Quantization
Clustering is a fundamental task in data analysis, typically addressed through centroid-based methods such as K-means.Single-Loop Stochastic Projected Damped Extragradient Methods for Stochastic Nonconvex--(Strongly) Concave Minimax Optimization
We develop single-loop stochastic projected damped extragradient methods for stochastic nonconvex--(strongly) concave minimax optimization, with complexity guarantees for both game stationarity (GS)…Neural composite likelihood estimation: simulation based inference for time series
Simulation based inference (SBI) circumvents the challenge of intractable likelihoods by using a simulator that generates data given parameter values.How Many Posterior Samples? Calibrated Stopping for Adaptive Sensing
In classification-oriented adaptive sensing, posterior samples characterize uncertainty at the current measurement state and can serve two roles: they may guide the next sensing direction, while…EnterpriseVal: Quantifying the Efficacy, Reliability and Value of Generative AI in the Enterprise
Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically valuable tasks, yet most enterprise GenAI…Geometric Mean Pooling for Equal-Weight Multiplicative Coarse-Graining
As an alternative to the additive and extremal biases of average and max pooling, we introduce Geometric Mean Pooling (GMP), a signed pooling operator that combines the product of feature signs with…Riemannian Simultaneous Inference for Tangent Vector Field Regression
We consider nonparametric tangent vector field regression on a Riemannian manifold without boundary.Schedule optimization for tau-leaping in masked discrete diffusion
Masked discrete diffusion models are commonly accelerated using the so-called tau-leaping discretization method, which reveals several coordinates in parallel at each sampling step.Moral Entropy: Auditing Bias and Uncertainty in Moral Judgment
Most work in computational ethics treats annotator disagreement on moral content as noise to be voted away, collapsed into majority vote or the more permissive any-annotator rule the moment a single…Bridging Local and Population Causal Effects: A Proximal Instrumental Variable Approach
Instrumental variable (IV) methods address treatment endogeneity, but with non-compliance and heterogeneous treatment effects a binary instrument generally identifies the local average treatment…RLVR is a Kernel, Not a Function: Statistical Inference for pass@$k$ Crossovers
Reinforcement learning with verifiable rewards (RLVR) often improves pass@1 while falling behind its base model at larger sampling budgets $k$, a crossover read as evidence that RLVR only sharpens…Classification with Abstention Under Class-Conditional Error Constraints
We study binary classification with abstention under separate class-conditional error constraints, with the objective of minimizing abstention while keeping both errors below prescribed thresholds.Locally Private Inference for Riemannian Stochastic Optimization
We develop inference for manifold-valued population minimizers when each observation belongs to a different participant and only locally private messages reach the analyst.Monotone-Constrained Diffusion Models for Long-Horizon Production Forecasting
Forecasting a long horizon from only the first observations of a sequence is ill-posed: many trajectories are consistent with the same short history.