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Machine Learning
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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 248
AI-Assisted Tumor-Growth Law Discovery from Sparse Data: Refitting and LLM-Based Ranking
Inferring tumor-growth laws from sparse, noisy observations requires more than accurate curve fitting.Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers
Emotions are expressed on a continuum and are naturally described in the valence-arousal (VA) space.Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty
Bayesian Networks (BNs) are established tools for letting agents reason under uncertainty in sequential decision-making.ML Engineering Agents - A Data Management Perspective
Machine learning engineering (MLE) agents are emerging as a new paradigm for automating end-to-end ML pipeline development.Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning
Online motor imagery-based brain-computer interfaces (MI-BCIs) typically decode continuous EEG using fixed-length sliding windows that may be misaligned with neural events.Minimax Optimality of Shampoo for Nonstationary Stochastic Optimization
We develop a comprehensive theoretical analysis of Matrix Shampoo for tracking time-varying optima in nonstationary stochastic optimization.Comparing GenAI to Machine Learning and Manual Review for eDiscovery
Keeling et al. [Keeling2026Generative] report on a case study recently conducted by Redgrave LLP in which they assert that Relativity's Generative AI Review tool (aiR) achieved 88% recall while…Making Tensor Decoupling More Robust and Less Parametric
Tensor decoupling algorithms recover compact representations of multivariate functions by combining linear factor matrices with univariate internal nonlinearities.KNN Implementation Details Can Dramatically Change Performance: An Example from Cover Trees
Nearest neighbor search algorithms are widely used and have a long history, but we find that key implementation details are seldom reviewed in articles about them.Social Priors: Descriptions of AI Users Shape How Language Models Trust and Cooperate
Descriptions of how people use AI are becoming training data for the next generation of models.Learning to Design Convex Relaxations for Nonconvex Optimization
Convex relaxations make nonconvex optimization tractable by replacing hard nonconvex problems with convex surrogates that give valid bounds.SarcasTraps: Effect of Sarcasm on Turkish Hate Speech Detection
Standard hate speech detection datasets often emphasize explicit harmful language, but directed harm can also be expressed indirectly, without clearly hateful wording.DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers
Understanding how many aircraft, and which traffic configurations, can be safely and efficiently managed by air traffic controllers within a given airspace is fundamental to air traffic management.Learning to Design SR Solvers: Harness Optimization for Symbolic Regression
Symbolic regression (SR) seeks explicit equations that explain observed data, and recent methods increasingly employ large language models (LLMs) and agents for equation discovery.PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models
Artificial Intelligence (AI) models are increasingly developed, published and used across scientific fields.Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals
Most prior work on predicting mental health help-seeking treats it as post-hoc classification of users already active in clinical communities, which cannot tell whether someone approaching a…From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning
Offline reinforcement learning aims to learn effective policies from fixed datasets without environment interaction.TabGift: A Context-Efficient Foundation Model for Tabular Data Generation
Tabular data generation aims to synthesize realistic samples from limited observations while faithfully capturing the underlying data distribution.Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation
Bayesian optimisation (BO) is a powerful framework for sample-efficient optimisation of expensive black-box functions, but its application to high-dimensional discrete scientific design remains…Constant-Depth Rational-Weight GNNs and their Connection to Schanuel's Conjecture
A landmark result states that the expressive power of graph neural networks (GNNs) is bounded from above by the 1-dimensional Weisfeiler-Leman (1-WL) test.