News / #paper Tag Research papers 500 articles archived under #paper · RSS Sign in to follow arXiv — Machine Learning research 2d ago Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture arXiv:2606.28623v1 Announce Type: new Abstract: Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be… 37 arXiv — Machine Learning research 2d ago When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling arXiv:2606.28661v1 Announce Type: new Abstract: People overthink; language models over-sample, and the extra effort can talk both into a worse answer. Reasoning systems answer a hard question by sampling it many times (test-time scaling), and the more they draw, the more often a… 22 arXiv — Machine Learning research 2d ago Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks arXiv:2606.28662v1 Announce Type: new Abstract: The flatness hypothesis suggests that flatness of the loss landscape, as measured by the eigenvalues of the loss Hessian, correlates with better neural network generalization. While various algorithms reduce these eigenvalues, most… 18 arXiv — Machine Learning research 2d ago Entropy Regularized Reinforcement Learning for Zero-Sum Stochastic Differential Games in a Regime-Switching Jump-Diffusion Process arXiv:2606.28669v1 Announce Type: new Abstract: To address parameter misspecification and sudden structural environmental changes in conventional stochastic differential game (SDG) frameworks, this paper introduces a distributional control approach that characterizes optimal… 16 arXiv — Machine Learning research 2d ago Entropy-Regularized Reinforcement Learning for Linear-Quadratic Stackelberg Differential Games in Regime-Switching Diffusion Models arXiv:2606.28671v1 Announce Type: new Abstract: Stackelberg differential games (SDGs) provide a powerful framework for hierarchical decision-making in stochastic and continuous-time environments, yet their solution remains computationally challenging due to the complexity of… 13 arXiv — Machine Learning research 2d ago Constrained Tabular Diffusion for Finance arXiv:2606.28674v1 Announce Type: new Abstract: Generative models in finance face the dual challenge of producing realistic data while satisfying strict regulatory and economic objectives, a requirement that standard tabular diffusion models cannot provide. To address this… 14 arXiv — Machine Learning research 2d ago A Path-Space Formulation of Prediction in World Models: From a Single Action to Prediction, Planning, and Irreversibility arXiv:2606.28751v1 Announce Type: new Abstract: We propose a path-space formulation of prediction in AI world models. Rather than sequences of one-step conditional distributions, we argue that a world model implicitly defines a probability measure over future trajectories. In… 13 arXiv — Machine Learning research 2d ago Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization arXiv:2606.28764v1 Announce Type: new Abstract: Hierarchical decision-making frameworks are pivotal for addressing complex control tasks, enabling agents to decompose intricate problems into manageable subgoals. Despite their promise, existing hierarchical policies face critical… 20 arXiv — Machine Learning research 2d ago Generative Learning as a Tool to Improve Perception of Emotional Body Motion Expressions arXiv:2606.28769v1 Announce Type: new Abstract: Emotional body motion expressions are an essential element of non-verbal communication. Effectively conveying these expressions through technology is of utmost importance, for example, with virtual reality avatars and in social… 9 arXiv — Machine Learning research 2d ago On design-unbiased algorithmic Machine Learning arXiv:2606.28795v1 Announce Type: new Abstract: Machine Learning (ML) algorithms, such as k-Nearest Neighbours (kNN) or random forest, eschew the ideal of true data models in favour of predictive performance. However, minimising the MSE or F-score cannot lead to unbiasedness… 24 arXiv — Machine Learning research 2d ago HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression arXiv:2606.28831v1 Announce Type: new Abstract: Long-context LLM inference faces a fundamental conflict: head-adaptive compression algorithms (e.g., Top-$p$ nucleus sampling) offer superior accuracy by dynamically fluctuating memory budgets, yet modern inference engines (e.g.,… 29 arXiv — Machine Learning research 2d ago Active Quantum Kernel Acquisition for Gaussian Process Regression arXiv:2606.28833v1 Announce Type: new Abstract: Quantum kernel estimation on near-term hardware is shot-budgeted: every entry of the kernel Gram matrix is a Bernoulli expectation that must be sampled with a finite number of circuit executions. Recent work on quantum kernel… 36 arXiv — Machine Learning research 2d ago Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning arXiv:2606.28835v1 Announce Type: new Abstract: Federated Learning (FL) emerged as a promising distributed machine learning paradigm. However, extending FL to the class incremental learning scenarios introduces unique challenges: 1) Capacity conflict and catastrophic forgetting… 7 arXiv — Machine Learning research 2d ago The Contagion Tensor: A Framework for Measuring Output-Distribution Coupling in Multi-Agent LLM Systems -- and Auditing the Claims It Enables arXiv:2606.28839v1 Announce Type: new Abstract: We introduce the Contagion Tensor, a measurement framework for quantifying how large language model (LLM) output distributions couple across modalities, agents, and time steps. From the tensor we derive the Coupling Amplification… 38 arXiv — Machine Learning research 2d ago Analysis of Adam Algorithms for Stochastic Dynamic Systems arXiv:2606.28879v1 Announce Type: new Abstract: The adaptive moment estimation algorithm, known as Adam, is widely used in modern machine learning, owing to its low per-iteration complexity and strong empirical performance. Despite its prevalent use, the theoretical foundation… 11 arXiv — Machine Learning research 2d ago An Integrated Machine Learning and Hierarchical Variance Decomposition Pipeline for Student Performance Prediction and Metacognitive Calibration on Multi-Signal Telemetry arXiv:2606.28881v1 Announce Type: new Abstract: Predicting student performance and characterizing metacognitive calibration are essential for personalization in intelligent tutoring systems. Prior research treats performance prediction, calibration error calculation, and… 6 arXiv — Machine Learning research 2d ago MALOQ: Massively Accelerated Learning of Operators for Quantum Transport arXiv:2606.28911v1 Announce Type: new Abstract: Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extending electronic-structure calculations to previously… 19 arXiv — Machine Learning research 2d ago ML-Powered LDAP Reconnaissance Detection using Weak Supervision arXiv:2606.28917v1 Announce Type: new Abstract: Lightweight Directory Access Protocol (LDAP) is a protocol that allows users to query and modify Active Directory (AD) data. By default, all users have read access to all AD data through LDAP, making it a common initial tool for… 14 arXiv — Machine Learning research 2d ago Towards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networks arXiv:2606.28923v1 Announce Type: new Abstract: Detecting security threats in an organization's cloud computing environment has become necessary due to the increased reliance on cloud infrastructure. Logging of all cloud computing events enables investigation into any incidents… 24 arXiv — Machine Learning research 2d ago Multi-Agent Routing as Set-Valued Prediction: A WildChat Benchmark and Cost-Aware Evaluation arXiv:2606.28925v1 Announce Type: new Abstract: Tool and agent routing from natural-language prompts is naturally a set-valued prediction problem: a single query may require multiple agents, while over-selection increases execution cost. The benchmark introduced here is derived… 16 arXiv — Machine Learning research 2d ago DLR: Zero-Inference-Cost Latent Residuals for Low-Rank Pre-Training arXiv:2606.28932v1 Announce Type: new Abstract: Large language models have driven recent progress in language and multimodal AI, yet pre-training them at scale is prohibitively expensive. Low-rank pre-training, which factorizes each weight matrix into a rank-r product to reduce… 35 arXiv — Machine Learning research 2d ago ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies arXiv:2606.28939v1 Announce Type: new Abstract: Behavior-cloned diffusion policies are expressive but remain vulnerable to covariate shift: small deviations from demonstrated states can compound into task failure. Existing methods address this either by expanding the training… 10 arXiv — Machine Learning research 2d ago Machine-learnable Sets arXiv:2606.28947v1 Announce Type: new Abstract: In this study we present a formal definition of large discrete sets having, informally, three properties: their elements are easily recognized, easily generated, and the latter tasks are easily learned from examples. The formalism… 19 arXiv — Machine Learning research 2d ago Modification-Considering Value Learning for Reward Hacking Mitigation in RL arXiv:2606.28955v1 Announce Type: new Abstract: Reinforcement learning agents can exploit misspecified reward signals to achieve high apparent returns while failing on the intended objective, a failure mode known as reward hacking. Existing practical defenses typically constrain… 10 arXiv — Machine Learning research 2d ago RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection arXiv:2606.28970v1 Announce Type: new Abstract: Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient. Classical statistical detectors are often efficient, but they usually rely on a fixed… 32 arXiv — Machine Learning research 2d ago On Surrogate Modeling of Static Response of AM Short-Fiber Thermoplastics Using Graph Neural Networks arXiv:2606.28996v1 Announce Type: new Abstract: Short-fiber thermoplastic (SFT) composites are increasingly employed in lightweight aerospace and automotive structures owing to their favorable strength-to-weight ratio, high production rates, and recyclability. Unlike… 29 arXiv — Machine Learning research 2d ago How Far Can Sharpness and Complexity Jointly Explain Generalization? arXiv:2606.29043v1 Announce Type: new Abstract: Sharpness and complexity are two central factors in the generalization analysis of deep neural networks. Existing quantitative evaluations of generalization measures have largely focused on individual scalar measures, leaving the… 13 arXiv — Machine Learning research 2d ago MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment arXiv:2606.29049v1 Announce Type: new Abstract: Knowledge Tracing (KT) is important for personalized education but traditionally suffers from two key limitations: a reliance on shallow ID-based representations that neglect semantic depth and a restriction to single-granularity… 37 arXiv — Machine Learning research 2d ago A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest arXiv:2606.29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier. In this study, we propose an ensemble classifier called the kernel Fisher discriminant analysis forest (KFDA Forest), which is a tree-based ensemble… 35 arXiv — Machine Learning research 2d ago When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation arXiv:2606.29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and standard heuristic abstention policies miss user-specified risk targets by… 4 arXiv — Machine Learning research 2d ago Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models arXiv:2606.29091v1 Announce Type: new Abstract: Tabular foundation models cannot reason about data produced by running systems without access to the rules that govern them. We make this statement falsifiable. The \emph{Operational Turing Test} (OTT) constructs pairs of legal and… 32 arXiv — Machine Learning research 2d ago Priced Motion Through Optimal Faces: A Normal-Fan Geometry for Non-Stationary Adversarial MDPs arXiv:2606.29092v1 Announce Type: new Abstract: In a changing decision problem, standard dynamic-regret analyses have often equated the cost of non-stationarity to how far loss moves. However, it is simultaneously possible for a loss sequence to travel far and retain the same… 11 arXiv — Machine Learning research 2d ago DiLaServe: High SLO Attainment Serving for Diffusion Language Models arXiv:2606.29094v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as a promising alternative to conventional autoregressive language models. By generating multiple tokens in parallel during each denoising step, they offer higher inference… 36 arXiv — Machine Learning research 2d ago Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps arXiv:2606.29110v1 Announce Type: new Abstract: Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, there is a lack of similar advances in estimating… 32 arXiv — Machine Learning research 2d ago A Novel Latent-Class Attack and its Detection by Class Subspace Orthogonalization arXiv:2606.29112v1 Announce Type: new Abstract: Deep learning, which in general relies on voluminous amounts of training data, is vulnerable to data poisoning attacks, including error-generic attacks and backdoors (Trojans). In this work, we propose a new data poisoning attack… 35 arXiv — Machine Learning research 2d ago How Token Influence Decays with Distance: A Green-Function View of Trained Language Models arXiv:2606.29139v1 Announce Type: new Abstract: We study how the next-token prediction of an autoregressive Transformer language model changes under small perturbations of earlier input token embeddings. Motivated by operator learning and iterative solvers for differential… 27 arXiv — Machine Learning research 2d ago On the Nonlinearity of Learning Rate Scaling for LLM Training arXiv:2606.29158v1 Announce Type: new Abstract: Learning-rate transfer can reduce the cost of training large language models: instead of sweeping learning rates at target scale, practitioners extrapolate from smaller runs. Existing approaches often assume that the optimal… 29 arXiv — Machine Learning research 2d ago GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem arXiv:2606.29161v1 Announce Type: new Abstract: Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical metabolomics, systems biology, and adjacent disciplines. In this work, we revisit… 13 arXiv — Machine Learning research 2d ago Invariant Reasoning Directions in Latent Trajectories of Language Models arXiv:2606.29164v1 Announce Type: new Abstract: Latent reasoning models perform multi-step inference directly in hidden-state space, yet the structure of these latent reasoning trajectories remains poorly understood. We show that contrastive refinement signals between stronger… 25 arXiv — Machine Learning research 2d ago Symbolic Mechanistic Data Attribution: Tracing Training Influence to Learned Behavioral Policies arXiv:2606.29171v1 Announce Type: new Abstract: While existing data attribution methods can identify which training examples build specific mechanistic circuits, they cannot explain how training data shapes the high-level behavioral decisions a model learns to make. To bridge… 31 arXiv — Machine Learning research 2d ago Dead-Direction Conditioners: Gauge-Equivariant Preconditioning for Deep Networks arXiv:2606.29176v1 Announce Type: new Abstract: A deep network's loss is invariant to continuous symmetries of its parameters: the logit shift, the ReLU rescaling, the LayerNorm scale, the per-head attention rotation. Adam's per-coordinate preconditioner drifts along each… 27 arXiv — Machine Learning research 2d ago BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning arXiv:2606.29184v1 Announce Type: new Abstract: While Low-rank adaptation (LoRA) enables highly efficient fine-tuning by constraining task-specific updates to fixed low-rank subspaces, this rigid design limits representational flexibility and often results in overconfident… 15 arXiv — Machine Learning research 2d ago Representational Depth of Evaluation Awareness Shifts With Scale in Open-Weight Language Models arXiv:2606.29196v1 Announce Type: new Abstract: Do language models know when they are being tested? This question matters for AI safety: a model that recognises an evaluation context could alter its behaviour strategically, making downstream benchmarks harder to interpret. Using… 27 arXiv — Machine Learning research 2d ago BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis arXiv:2606.29200v1 Announce Type: new Abstract: Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneity, demographics, and site-specific acquisition protocols. Traditional domain… 14 arXiv — Machine Learning research 2d ago Bayesian Best-Arm Identification with Abstention: A Polynomial-to-Exponential Phase Transition arXiv:2606.29203v1 Announce Type: new Abstract: We study the Bayesian fixed-budget best-arm identification problem in which a learner can abstain from making a terminal recommendation. Subject to an abstention budget $\alpha$, we analyze the probability of undetected error--the… 36 arXiv — Machine Learning research 2d ago Multi-Block Diffusion Language Models arXiv:2606.29215v1 Announce Type: new Abstract: Block Diffusion Language Models (BD-LMs) improve diffusion-based text generation with KV caching and flexible-length generation. A natural next step is to extend them from Single-Block Diffusion (SingleBD) to Multi-Block Diffusion… 10 arXiv — Machine Learning research 2d ago A Linear Matching Bandit Approach to Online Multi-Human Multi-Robot Teaming arXiv:2606.29221v1 Announce Type: new Abstract: We address the problem of online multi-human multi-robot teaming through the lens of a linear matching bandit framework, where a learner assigns robots with unknown features from a fixed pool to distinct sets of human agents over… 15 arXiv — Machine Learning research 2d ago Depth Exploration for LLM Decoding arXiv:2606.29223v1 Announce Type: new Abstract: Autoregressive LLM decoding evaluates every generated token through the full layer stack, even though many tokens become predictable at intermediate depths. Existing lossless depth-adaptive methods exploit this redundancy by… 34 arXiv — Machine Learning research 2d ago On the Policy Gradient Foundations of Group Relative Policy Optimization: Credit Assignment, Gradient Sparsity, and Rank Collapse arXiv:2606.29238v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) eliminates the learned critic in PPO by using the mean reward of grouped rollouts as a baseline. We provide a rigorous derivation of GRPO from first principles of the policy gradient… 22 arXiv — Machine Learning research 2d ago Blackknife: Hard-Label Query-Limited Black-Box Attacks on Heterogeneous Graph Neural Networks arXiv:2606.29240v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have achieved strong performance in modeling complex graph-structured data with multiple node and relation types. However, their robustness under realistic black-box adversarial settings… 19 Page 9 of 10 · 500 articles ← Newer Older →
