AI & Machine Learning · Computer Science & AI
Machine Learning (Statistics)
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1,749 papers, from 4 September 2026 to 5 November 2026: 1,749 arXiv preprints.
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Newest papers, page 83
Hierarchical Clustering Can Jointly Satisfy Richness, Consistency, and Scale Invariance
Despite its ubiquity, clustering lacks a universally accepted definition of what is a cluster.A Hilbert-Valued Functional Decomposition Framework for Explaining Time-Dependent Outputs
Feature-based explanations quantify features' influence on model predictions, but are primarily designed for scalar outputs.Your Model Already Knows Don't Teach It, Learn to Ask It: Soft Prompting for Few-Shot Adaptation of Vision-Language Models
We address few-shot object detection with vision-language models (VLMs) in out-of-domain settings such as aerial, industrial, and medical imagery, using only ten annotated images for supervision.Improving the Sensitivity of Gravitational Wave Detection with Weighted Conformal Prediction
In the last decade, kilometre-scale interferometric gravitational-wave detectors have observed hundreds of compact binary mergers, the majority of which are binary black holes.Generalized Score Matching for Parameter Estimation on Convex Domains
Maximum likelihood (ML) estimation is a principled and statistically efficient approach for learning probabilistic models.Risk-Averse Decision Making with Multi-Level Reliability Guarantees
Many applications in engineering, including wireless broadcasting, require designs that provide performance certificates at different target outage levels.A distribution-free certification framework for trustworthy crash-severity prediction
Crash-severity models inform screening, dispatch and site prioritization, yet are deployed without a finite-sample statement of what one prediction means.Identifiability of Nonnegative Tensor Decompositions via Positive Scattering
Identifiability of tensor decompositions is often established through linear-algebraic conditions on the factor families.RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
Multivariate time series imputation (MTSI) aims to recover missing values in temporal data composed of multiple interdependent variables.Stress-Testing Dynamical and Generative Downscaling Using Subseasonal Extreme Precipitation Forecasts
Coarse spatial resolution limits the ability of subseasonal prediction models to resolve extreme precipitation.Generalization Analysis of Distributed Kernel-based Robust Gradient Descent Algorithms
In this paper, we investigate the generalization performance of distributed gradient descent algorithms in a reproducing kernel Hilbert space under a robust loss function $l_σ$.Sparsity Regularized and Robust Mean Variance Portfolio Selection Under Ellipsoidal Uncertainty
We investigate mean-variance portfolio selection with an $\ell_0$-penalty to promote sparsity in asset allocations.Near-Optimal Reinforcement Learning with Multi-Step Transition Lookahead
We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of $\ell$ actions before deciding its course of…Quantitative Diffusive Limits for Singular Nonlocal Transport
We study the nonlocal continuity equation \[ \partial_tμ_b =\operatorname{div}\!\left( μ_b\nabla\log\bigl((I-b^2Δ)^{-1}μ_b\bigr) \right) \] on a closed connected Riemannian manifold.Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting
Continuous glucose monitoring (CGM) provides high-frequency measurements of glucose dynamics and enables short-term glucose forecasting for diabetes management.Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers.General Quantification of Covariate and Concept Shifts
Generalization under distribution shift remains a core challenge in modern machine learning, yet existing learning bound theory is limited to narrow, idealized settings and is non-estimable from…Learning Interaction Kernels from Collective Steady States
We propose a learning procedure for system identification in interacting particle systems from single-snapshot observations of collective behaviors, unlike existing approaches that rely on…Score-based Outlier Generation via Controlling the Radon-Nikodym Derivative
Outliers are important for stress-testing algorithms and understanding system behaviour under rare conditions.Rank-Efficient LoRA via Joint Tangent-Space Optimization under Isotropic Curvature
Low-Rank Adaptation (LoRA) is an effective approach for adapting large pretrained models by learning low-rank weight updates.