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 4
A simple adaptive accelerated optimistic dual averaging method for unconstrained convex optimization in $p$-norm geometry
Efficient preconditioning plays an important role in large-scale first-order optimization.Tight Lower Bounds under Matched High-Order Hölder Smoothness and Uniform Convexity
In this paper, we provide tight lower bounds for minimizing high-order Hölder-smooth and uniformly convex functions when their orders match.Towards Efficient Whole Page Ranking with One-Pass Inference
Whole-page ranking jointly optimizes advertisements and organic content to balance business outcomes and user engagement under complex layout and business constraints.Generalized Hyperrectangular Prediction Sets in Multivariate Conformal Prediction
In multivariate conformal prediction, many nonconformity scores yield prediction sets with complex geometry (e.g., ellipsoids or density level sets) that are difficult to interpret and visualize.A Unified Framework for Training-Dataset Aggregation and Exact Machine Unlearning via Gibbs Probability Measures
In this paper, three operations on Gibbs probability measures are studied: renormalization, normalized log-linear combination, and nesting.Support Vector Machines Directly Minimise KL Divergence
Support vector machines (SVMs) are fundamentally grounded in geometric margin maximisation and currently lack a mainstream probabilistic interpretation.Near-Minimax Sample Complexity for Multi-Objective Reinforcement Learning under Generative Models
In multi-objective reinforcement learning, preferences may be specified only after data collection, requiring one dataset to support a continuum of reward trade-offs.Transport along Local Annealing Paths: Wasserstein Geometry and Multimodal Sampling
Variational approaches to learning transport maps for multimodal sampling can suffer from mode collapse.Exact signal recovery from generative priors with sigmoid output activation
We show exact signal recovery in generative compressed sensing for feedforward neural networks with piecewise-linear activations and a final sigmoid activation, with no assumption of expansivity.From Type to Token: Petri Nets, Individual Token Philosophy, and Individualized Causal Effects
Structural Causal Models and Bayesian Networks are the dominant framework for representing causal relations.Balance-Guided Forest for Causal Effect Estimation
Covariate balancing is widely used for estimating causal effects in observational studies.Generative Modeling for Cascading Extremes via Autoregressive Transformers
Extreme events often occur sequentially, from earthquake-tsunami cascades to successive extreme losses in financial markets.On Stochastic Continuous-Time Linear System Identification for Adaptive Stabilization
Learning to stabilize an unknown continuous-time stochastic linear system is a central problem in control theory and reinforcement learning.Efficient Proper Agnostic Learning of Halfspaces
We give a proper membership-query algorithm for agnostically learning halfspaces under the uniform distribution on $\{\pm1\}^n$.Where Is a Prediction Model Miscalibrated, and for Whom? Local Audits in Outcome-Learned Pockets
Overall and group-level averages, and ranking metrics such as the AUC, can hide sets of observations for which a prediction model systematically over- or under-predicts.The Staged Pandora's Box: Reservation Indices for Multi-Fidelity Bayesian Optimization
A staged search must decide which candidate to advance next and when to stop.Spectral Degeneration of Self-Attention under Isotropic Score Geometry
We study the spectral structure of the self-attention matrix in Transformer models through an idealized spectral model for the pre-softmax score matrix.When Is Tracking Affordable? A Movement-Budget Feasibility Threshold for Dynamic Regret
Dynamic regret measures an online learner against a moving comparator, and standard analyses assume the learner may move freely at every round.Unimodal Augmentation-based SSL Objectives Induce Cross-Modal Alignment
Recent works have observed implicit cross-modal alignment between independently trained unimodal models.Exact Fusion of Softmax Attention Heads Can Require Exponentially Many Atoms
Fusing independently normalized attention heads can increase the number of explicit key-value entries, or atoms, needed to preserve their output.