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 12
Integrating multi-source local information for global undirected graph reconstruction via integer programming
Graphical models are widely used to represent conditional independence structures, but learning them is challenging when data are fragmented across sources that measure different subsets of…Metropolized backward sampling for conditional sequential Monte Carlo
Particle Gibbs samplers with backward sampling are widely used for sampling the smoothing distribution of state-space models, but, with few particles, the new trajectory often coincides with the…Pinpointing Pivot Points: The DECIDE Framework for Data Analysis Decision Importances
Human judgment calls made throughout the data science life cycle can change the conclusions drawn from data.Memorization Capacity of Invariant Convolutional Neural Nets
Neural networks are powerful memorizers which are often able to interpolate their training data even when the labels have been randomized.Orthogonal Inference for Conditional Z-Estimation without Cross-Fitting: A Distributional Nearest-Neighbor Approach
Estimating conditional parameters, such as heterogeneous treatment effects, often requires rich covariate adjustment, even when the parameters are reported along only a few dimensions.A Spectral Boundary for Independence Learnability
Learning independence is fundamental to statistical inference and machine learning, yet learning independence between entire time series by a test remains challenging when only a single observation…Modular Bayesian Foundation Models: Bayesian time series encoders for OOD detection, routing and forecasting
Industrial systems continuously generate time series that vary in sampling frequency, scale, dynamics, and operating regime.CausalGrade: Evidence-Guided Causal Graph Scoring with Jev
Evaluating the structural quality of candidate causal graphs when the true causal structure is unknown is an important problem in applying causal discovery results.Second-order Sinkhorn Geometry and $\sqrt{n}$-Inference
The Sinkhorn divergence, a debiased form of entropic optimal transport, has been widely used in various scientific disciplines due to its computational and statistical tractability.Algorithmics for Graphical Separation Criteria
Statistical graphical models are representations of the dependences between the components of a random vector.Wasserstein approximation of measures via orthogonal frames: statistical and computational complexities
Dimension-reduction methods such as PCA identify a subspace capturing most of the variation in the data, but not a distinguished basis within it.Deep Bayesian Filtering for Nonlinear Structural System Identification: A Tutorial on Structural and Physical Inductive Biases
Nonlinear structural system identification remains challenging when governing physics is only partially known and measurements are sparse.PDE-Based Bounds on Optimal Exponents in Active Simple Hypothesis Testing
We present general upper and lower bounds on the optimal error exponent for the Active Simple Hypothesis Testing (ASHT) problem.Conservative Dueling Bandits: Preference Learning under Baseline Constraints
Contextual dueling bandits learn from pairwise comparisons, the same feedback used to fine-tune language models from human preferences.Generative Refinement Learning for Temporal Interpolation in Scientific Machine Learning
Computationally expensive simulations and real-world observations often provide data at a lower temporal resolution than downstream tasks require.LLM knowledge for improved Out-of-Distribution Tabular Prediction - A Cross-Validated Regularisation Approach
Prediction models degrade when the deployment population differs from the one used to train them, and training data cannot say which feature-response relationships will transfer.Joint Distributional Copula Regression for Insurance Claim Frequency and Severity via Boosting
In this paper, we introduce a model-based boosting approach for fitting structured additive distributional copula regression models for bivariate responses, with particular emphasis on jointly…Boundary Geometry Determines Optimal Shallow ReLU Approximation under Weighted Coefficient Constraints
We study how boundary geometry affects $L^q$ approximation by shallow ReLU neural networks on convex bodies $K\subset\mathbb R^d$.Intrinsic Information-Theoretic Analysis of ReLU Nets
Mutual information (MI) is fundamental to representation-learning analysis in deep networks, but in high dimensions MI estimators depend on hyperparameters whose bias often dominates the signal.Online gas classification at the edge
Field applications in chemical sensing, from portable safety monitors to mobile robots, require models that are lightweight, fast to respond, and can be recalibrated from limited labelled data.