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 21
Post-hoc Epistemic Uncertainty for Vision-Language-Action Policies
Vision-language-action (VLA) policies need to recognize gaps in their training knowledge to request useful demonstrations or assistance in new situations, especially when failures are costly.Asymmetric Likelihood-Free Hypothesis Testing
In likelihood-free hypothesis testing (LFHT), given samples from two unknown distributions $P,Q$ on $[n]$ with $\mathrm{d_{TV}}(P,Q)\geq\varepsilon$, and from a third distribution $D\in\{P,Q\}$, the…How Do DiTs Denoise? An Aggregator-Predictor View of Attention and MLPs
How do diffusion transformers (DiTs) represent image content and learn rules for reusing it to generalize?Silent Alignment in Two-Layer Linear Networks: A High-Dimensional Perspective
We study gradient flow in a two-layer linear network in the high-dimensional regime where the input, inner and output dimensions diverge proportionally.Mastering Model Fit: Applications of the InterModel Vigorish for Interpretable Machine Learning
Accuracy alone gives little information about which features or model components improve probabilistic fit, which outcomes benefit, or how fit changes during training.The Price of Current-Effect Inference in Nonstationary Multi-Armed Bandits
Adaptive experiments use bandit algorithms to favor interventions with higher expected rewards.Mitigating Model Collapse through Dreaming Learning
Training generative models on synthetic data causes loss of diversity and a decrease in performance, a phenomenon known as model collapse.The role of locality and weight sharing in convolutional neural networks
The success of convolutional neural networks is often attributed to locality and weight sharing.Geometric Algebra for Neural Networks: A Survey
Geometric algebra (GA) unifies geometric primitives and transformations in a single algebraic framework.Why Constrained Updates Still Forget: A Functional-Path Analysis of Continual SFT
Supervised fine-tuning adapts language models to specialized tasks but can disrupt previously acquired behavior.Differentially Private Synthetic Data via Generalized Moment Matching
We study $(\varepsilon,δ)$-differentially private synthetic data for arbitrary databases of $n$ records in $[-1,1]^d$ under a specified integral probability metric (IPM).AI Mindmeld: A Latent Interconnect Protocol
LLMs can act as scientific agents using tools to consult specialists and run experiments.When Reconstruction Is Not Enough: Doubly Robust Counterfactual and Interventional Mediation Analysis with Learned Multimodal Representations
Learned representations of high-dimensional multimodal health records are increasingly used to adjust for confounding, yet they are usually trained to reconstruct the data rather than preserve the…High-Value Training Data from Noisy, Diverse Sources
Training state-of-the-art models increasingly requires learning from large, heterogeneous datasets whose examples vary in quality, domain relevance, and source of supervision.Flow-based Spectral Kernel Learning for Nonstationary Attention
Kernelized attention provides a useful perspective for understanding self-attention as a similarity-based aggregation mechanism, but existing approaches often rely on fixed or stationary kernels…A Bayesian machine learning approach to direct and spillover effects in cluster-randomized trials with noncompliance
We develop a Bayesian machine learning approach for estimating direct and spillover effects in cluster-randomized trials with treatment noncompliance.Beyond Scalar Leaderboards: Adaptive Sampling for Pareto Frontier Identification in Multi-Objective LLM Arenas
The evaluation of large language models (LLMs) is inherently multi-objective, as different models may offer different tradeoffs across performance dimensions such as math, code, and writing.MiCLA: Mixture-to-Constituent Latent Alignment for Polyphonic Audio Representation Learning
Real-world audio typically contains concurrent sound events, yet standard self-supervised methods treat each clip as an isolated training unit without explicitly modeling mixture--constituent…On the Accuracy of Approximate Leave-One-Out with Blockwise Nonsmooth Regularization in High-dimensions
Approximate leave-one-out (ALO) refers to methods that provide computationally efficient and statistically accurate approximations to leave-one-out risk estimation.What Moved the Alarm Score? Causal Attribution with Exact Computation and Scarce-Incident Estimation
Alarms in industrial systems and scientific instruments often demand immediate diagnosis despite scarce incident data.