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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 38
Poisson Super Learning For Survival And Competing Risks On A Discretized Time Scale
We introduce the Poisson Super Learner, a scalable ensemble method for survival and competing risks prediction with right-censored data.Rethinking Fairness Metrics for Multi-Class and Multi-Group Assessment: Not Everything is Binary
Artificial Intelligence, and Machine Learning in general, is becoming pervasive across all domains and disciplines.The Cost of Mismatch: Noise Amplification in Zeroth-Order Reinforcement Learning
Zeroth-order (ZO) optimization has emerged as a promising approach for memory-efficient large language model (LLM) post-training, yet its reliability in reinforcement learning with verifiable…DeepWaveRL: Self-Supervised Full Waveform Inversion via Reinforcement Learning
Full Waveform Inversion (FWI) is a fundamental inverse problem in geophysics to estimate subsurface properties, such as velocity, from seismic measurements.A Human-Verified AI Workflow for Annotating Environmental Sound Data
Large-scale annotation of environmental sound recordings presents significant challenges under time and resource constraints, particularly when sound categories are imbalanced and only part of a…Strictly proper scoring rules for node selection tasks on graphs
In node selection tasks, the goal is to identify a single unknown target node in a graph.ENGINE: Endogenous Variational MultiScale Optimization for Zeroth-Order LLM Fine-Tuning
Zeroth-order (ZO) optimization enables memory-efficient large language model (LLM) fine-tuning, and recent methods incorporate curvature-aware strategies.Inferring Transferable Rewards via Active Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) aims to recover expert rewards that transfer across environments with differing dynamics.An Axiomatic Framework for Reinforcement Learning in Open Multi-Agent Systems
Open multi-agent systems, where only a subset of agents are controllable, are central to real-world domains such as smart grids, yet lack a principled foundation for reinforcement learning algorithm…EventGPT: Next-Event Pretraining on Banking Customer Histories
A model pretrained to predict a customer's next event can estimate the probability of future events without training on their labels and provide a reusable representation for supervised downstream…Calibrated Multiaccuracy: Deterministic and Randomized Sample Complexity
Calibrated multiaccuracy asks that a predictor be globally calibrated and multiaccurate with respect to a bounded test family $G$; that is, its residuals should be uncorrelated with every test…LLMs Depend on Specialist Tools for Out-of-Distribution Generalization in MS/MS Structural Elucidation
A frontier language model was recently reported to solve 1D NMR structural elucidation without tools.The SIMPLE Law for Energy Cost of LLM Inference
Generative AI is billed per token, yet tokens are not equally expensive.FLAMIA: FLAsh MIcroscaling Attention for Fast and Causally Safe Training on Blackwell
Attention remains a substantial computational cost in Transformer training, particularly at long context lengths.NormMoX: A Normative Mixture of Experts for Explainable Autism Classification from Structural and Functional MRI
While structural and functional magnetic resonance imaging (sMRI, fMRI) offer profound potential for researching autism spectrum disorder (ASD) and understanding individual neurodivergence, current…Certifying Graph Neural Networks Against Label and Structure Poisoning
Robust machine learning for graph-structured data has made significant progress against test-time attacks, yet certified robustness to poisoning - where adversaries manipulate the training data -…Neural Actor--Critic Learning with Minimax Critics and Reflected Langevin Sampling
Actor--critic methods rely on critic gradients to improve continuous-action policies, yet finite-sample guarantees must control both value and gradient errors and their propagation through the actor.Multidimensional Suicide Risk Assessment with Machine Learning using Electronic Health Records
Suicide prevention remains a critical public health challenge, and electronic health records (EHRs) provide an opportunity to characterize suicide-related risk indicators using documented clinical…Every Pixel Is an Option: A System One Model for Medical Ultrasound Biometry
**Purpose:** Ultrasound biometry is widely used across various clinical applications, including prenatal and cardiac examinations, to obtain quantitative measurements by placing calipers at…Characterizing When Language Models Take Harmful Actions to Protect Their Assigned Goals
Recent language model (LM) safety failures have largely been studied as disparate phenomena.