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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 124
Cross-Scenario Controller Parameter Transformation from Real-World Driving Data through Flow Matching
Advanced Driver Assistance Systems have become increasingly widespread, aiming to support drivers, reduce workload, and improve traffic safety.PlayerEmbed: Learning Behavioral Fingerprint Representations from Raw Input Sequences
Raw player input sequences in video games contain fine-grained behavior, but are high-dimensional and noisy, which makes large-scale analytics challenging.Internal Discrepancy for Contrastive Learning: Generalization Bounds and Downstream Behaviors
Contrastive learning has emerged as a popular paradigm for training representation models.Strongly Adaptive Online Learning with Time-Varying Movement Costs
We study strongly adaptive regret for Online Convex Optimization (OCO) with time-varying movement costs, a setting that evaluates a learner's performance over arbitrary sub-intervals.On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution
The interest in leveraging physics-based inductive bias in deep learning has resulted in recent development of hybrid deep generative models (hybrid-DGMs) that integrate known physics-based…Sparse Bayesian Neural Networks via Sticky Piecewise Deterministic Markov Processes
There is growing evidence that the performance of a neural network depends largely on a small subset of its weights, which are typically identified by training a dense network and pruning it…ARENA: Active Region attENtion Aggregation for interpretable solar flare prediction
Predicting solar flares is a challenging space-weather forecasting problem with potentially significant impacts on Earth's upper atmosphere, including disruptions to radio communications.Clean-label Poisoning Attack and Defense Game for Federated Learning in Vehicular Networks
This paper studies interactions between clean-label poisoning attack and defense in vehicular networks using federated learning (FL).On-Policy Self-Training: Data-Free Self-Improvement in Large Language Models
On-policy self-distillation enables post-training foundation models without relying on stronger external teachers.Direction-Aware Token Credit Assignment with Reward-Model Gradients
GRPO shares a response-level advantage across tokens, leaving unresolved how strongly each token should contribute to learning.Deep Learning-Based Prediction of HER2 Status from Whole-Brain MRI in Breast Cancer Brain Metastases
Human epidermal growth factor receptor 2 (HER2) status is a key biomarker relevant for prognostication and treatment decision making in metastatic breast cancer.Computational De Novo Design of Miniprotein Candidate Neutralizers Targeting the IL-23 Cytokine Subunit p19
Interleukin-23 (IL-23) drives T-helper-17 inflammation underlying inflammatory bowel disease and psoriasis; anti-p19 antibodies are clinically effective but large, costly, and injectable.Generalization properties of the token perceptron: the role of embedding and vocabulary
Large language models are trained to predict tokens from a finite vocabulary, each represented by a vector in a $d$-dimensional embedding space.CARES: Coverage-Aware Replay Exemplar Selection for Continual Learning
Replay-based Continual Learning mitigates catastrophic forgetting by keeping a buffer of past samples, making buffer composition critical under a fixed memory budget.Multiplicative vs. Additive State Coupling in Learned Optimizer Schedules
We learn schedules for a continuous penalty relaxation of MAX-CUT by backpropagating through an unrolled classical solver.U-NeXt: General-purpose event detection for neonatal EEG
We developed U-NeXt, a general framework for neonatal electroencephalography (EEG) event detection based on semantic segmentation.On the Limits of Sparse Linear Model with Shuffled Differential Privacy
Sparse linear regression is a fundamental problem in high-dimensional statistics, and its private counterparts leave a large gap between the central and the local model of differential privacy.Neural LoFi with Backward Coupling: A Spectral Theory of Cross-Layer Feature Learning
During joint training, backpropagation allows later layers to reshape earlier representations, a feedback mechanism absent from purely layer-wise learning.Near-Optimal List Replicability from Littlestone Dimension
We study the list replicability number in the realizable PAC learning framework and prove that every concept class $H$ over a countable domain with Littlestone dimension $d$ and dual VC dimension…Singular Geometry of Low-Rank Adaptation
Low-rank adaptation (LoRA) is now a standard approach to parameter-efficient fine-tuning of large neural networks.