Log-Ratio-Based Gaussian Trust Weight

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8 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes
Sep 29, 2026Fred Xu, Thomas Markovich, Florence Regol +1Generalist Graph Anomaly Detection Anomaly Score
University of California, Los Angeles · Block · Mila – Quebec AI Institute
Reference-Tail Trust:Certified Probability Floors for Learned Updates Inside a Deployed Network
Sep 28, 2026Abdolvahab Khalili Sadaghiani, Jose Nunez-YanezLog-Ratio-Based Gaussian Trust Weight Graph Neural Networks
LLM4Trust: Exploring the Capabilities of Large Language Models for Trust Evaluation
Sep 27, 2026Jie Wang, Yanbo Sun, Zheng Yan +2AI Trustworthiness Log-Ratio-Based Gaussian Trust Weight
State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University · Hangzhou Institute of Technology, Xidian University · Department of Computer Science, Purdue University
A Scalable Trust Discovery Architecture for the Internet of Agents
Sep 17, 2026Song Zhang, Jiankang Yao, Hongtao Li +4Log-Ratio-Based Gaussian Trust Weight Protocol
China Internet Network Information Center, Beijing 100070, China · National Engineering Laboratory of Internet Domain Name Management Technology, Beijing 100070, China · Building 4, No.9 West Road, Automobile Museum, Fengtai District, Beijing 100070, China +2
End-to-End Verifiable and Robust Federated Learning
Sep 14, 2026Doryan Lesaignoux, Enrique Mármol Campos, Gabriele Spini +2Federated Learning Zero-Knowledge Proofs
AIT Austrian Institute of Technology · Vienna, Austria · Department of Computer Engineering +2
A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction
Sep 9, 2026Chunxu Zhang, Bo Li, Wenliang Wang +7Vascular Age Federated Learning
PolyU Academy for Artificial Intelligence, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China. · Quantum Life, Hong Kong, China. · Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China. +3
Topological Fraud Detection in Latent Transaction Spaces
Sep 8, 2026Avraham BourlaFraud Detection Log-Ratio-Based Gaussian Trust Weight
A Channel-Boosted Multi-Agent System with Iterative Consultation for Document Sensitivity Classification
Sep 2, 2026Aleesha Zainab, Asifullah Khan, Muhammad Ahmed Khalid +1Large Language Model Classification Log-Ratio-Based Gaussian Trust Weight
Pattern Recognition Lab, Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan. · Deep Learning Lab, Center for Mathematical Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan. · PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan.
Optimizing Byzantine Node Placement in Decentralized Federated Learning
Sep 1, 2026Edoardo Gabrielli, Gabriele TolomeiByzantine Attacks Federated Learning
Dipartimento di Ingegneria Informatica, Automatica e Gestionale Sapienza University of Rome Rome, Italy · Dipartimento di Informatica Sapienza University of Rome Rome, Italy
Feedback-Assisted Trust Propagation over Document Relation Graphs for Retrieval-Augmented Generation
Sep 1, 2026Zhuoheng Li, Ying ChenLog-Ratio-Based Gaussian Trust Weight AI Trustworthiness
College of Information Sciences and Technology, The Pennsylvania State University
Finding the Signal in the Spam: Jointly Learning Rewards and Worker Reliability from Pairwise Comparisons
Aug 10, 2026Kaustubh Shivshankar Shejole, Tanish Agarwal, Arpit Agarwal +1Progress Reward Modeling Log-Ratio-Based Gaussian Trust Weight
IIT Bombay, Mumbai, India – 400076
Robust Reputation-Driven Crowdsourced Federated Learning
Aug 9, 2026Mouhamed Amine Bouchiha, Gregory BlancFederated Learning Log-Ratio-Based Gaussian Trust Weight
SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris
Toward Standardized Cross-Vendor Agent Tool Trust Management in Autonomous Networks
Jul 28, 2026Ravi Kant Sharma, Ashutosh Uttam, Ajay KumarLog-Ratio-Based Gaussian Trust Weight AI Trustworthiness
Ericsson
Agree on the Model, Verify the Inference: GKR Protocols for HND-Based Transformer Inference
Jul 23, 2026Xiaolong Liang, Juanjuan Li, Rui Qin +1Homomorphic Encryption Zero-Knowledge Proofs
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
PREF-Gate: Provenance-Constrained Relational Evidence Fusion with Validation-Gated Selection for Graph Fraud Detection
Jul 13, 2026Liming Liu, Chao Hu, Mingfei Lu +3Fraud Detection Generalist Graph Anomaly Detection
Central South University, Changsha, China · University of Technology Sydney, Australian Artificial Intelligence Institute, Sydney, Australia
Signed-Graph Recommendation as Structural Consistency Maximization
Jul 7, 2026Zifan Wang, Siyu Chen, Wenzhuo SongZero-Shot Link Prediction Log-Ratio-Based Gaussian Trust Weight
Northeast Normal University, Changchun, Jilin, China
Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustness
Jul 1, 2026Thomas Boudou, Batiste Le Bars, Nirupam Gupta +1Generalization Bounds Byzantine Attacks
Inria, Université de Montpellier, INSERM · Montpellier, France · Inria, Université de Lille +3
FoggyTrust: Robust Federated Learning with Hierarchical Trust Networks
Jun 26, 2026Emmanuel Rassou, Tomas GonzalezFederated Learning Log-Ratio-Based Gaussian Trust Weight
School of Engineering and Applied Sciences (SEAS) Harvard Cambridge, MA, USA
TL++: Accuracy and Privacy Preserving Traversal Learning for Distributed Intelligent Systems
Jun 24, 2026Erdenebileg Batbaatar, Young YoonFederated Learning Privacy-Preserving Machine Learning
Neouly, Republic of Korea · Department of Computer Engineering, Hongik University, Republic of Korea
BARD-MARL: Byzantine-Agent Detection for Learned Communication in Multi-Agent Reinforcement Learning
Jun 15, 2026Almond Kiruthu MurimiMulti-Agent Reinforcement Learning Log-Ratio-Based Gaussian Trust Weight
Carnegie Mellon University Department of Electrical and Computer Engineering Pittsburgh, Pennsylvania, United States
TCHG: Tri-Trust Conditioned Heterogeneous Graph Learning for Reliable Dynamic Trust Prediction
Jun 15, 2026Bohao Liao, Boyu Deng, Qipeng Song +2Log-Ratio-Based Gaussian Trust Weight Text-Attributed Graph
School of Telecommunication Engineering, Xidian University, Xi’an, 710071, Shaanxi, China · National Key Laboratory of Multi-domain Data Collaborative Processing and Control, Beijing, 100141, China · School of Cyber Engineering, Xidian University, Xi’an, 710071, Shaanxi, China
The ACUTE Protocol: Operationalizing Language Model Activations for Better Calibration, Utility, and Trust
Jun 5, 2026Nishant Subramani, Palash Goyal, Yiwen Song +4Confidence Estimation AI Trustworthiness
Carnegie Mellon University · Google · Scale AI
Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data
Jun 4, 2026Rebecca Potts, Aiden Durrant, Rick Hackney +1Gas Turbine Combustor Log-Ratio-Based Gaussian Trust Weight
School of Natural and Computing Sciences University of Aberdeen, Aberdeen, UK · School of Computing Sciences University of East Anglia, UK · Siemens Energy Industrial Turbomachinery Ltd. Lincoln, UK +1
Practical Validity Conditions for Byzantine-Tolerant Federated Learning
May 15, 2026Mélanie Cambus, Darya Melnyk, Tijana Milentijević +1Byzantine Attacks Log-Ratio-Based Gaussian Trust Weight
Aalto University, Finland · Technical University Berlin
How Faithful Is Trajectory-Based Data Attribution? Error Sources, Remedies, and Practical Guidelines
May 12, 2026Junwei Deng, Pingbang Hu, Suliang Jin +4Alignment-Aware Selection Log-Ratio-Based Gaussian Trust Weight
University of Illinois Urbana-Champaign · University of Michigan · Princeton University +1
OrScale: Orthogonalised Optimization with Layer-Wise Trust-Ratio Scaling
May 8, 2026Yuxuan Lou, Yang YouSpectral Norm Scaling
National University of Singapore
FedeKD: Energy-Based Gating for Robust Federated Knowledge Distillation under Heterogeneous Settings
May 7, 2026Quang-Huy Nguyen, Jiaqi Wang, Wei-shinn KuKnowledge Distillation Fedavg
Department of Computer Science and Software Engineering Auburn University Auburn, AL 36849
Learned Neighbor Trust for Collaborative Deployment in Model-Agnostic Decentralized Learning
May 6, 2026Michael Lanier, Luise Ge, Sastry Kompella +1Log-Ratio-Based Gaussian Trust Weight Decentralized Learning
Department of Computer Science Washington University in St. Louis and Nexcepta
ITBoost: Information-Theoretic Trust for Robust Boosting
May 6, 2026Ye Su, Longlong Zhao, Diego Garcia-Gil +4Noisy Labels Gradient Boosted Decision Tree
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Department of Software Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada +1
PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discovery
May 3, 2026Xihang Shan, Da ZhouCausal Discovery Methods Causal
School of Mathematical Sciences Xiamen University Fujian, China
GIPO: Gaussian Importance Sampling Policy Optimization
Mar 4, 2026Chengxuan Lu, Zhenquan Zhang, Shukuan Wang +3Frictive Policy Optimization Gaussian Primitives
1IROOTECH TECHNOLOGY · 2Wolf 1069 b Lab, Sany Group
Temperature Scaling Attack Disrupting Model Confidence in Federated Learning
Feb 6, 2026Kichang Lee, Jaeho Jin, JaeYeon Park +2Federated Learning Gradient-Based Attacks
Yonsei University · Dankook University
CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?
Dec 12, 2025Jie Wang, Zheng Yan, Jiahe Lan +2Log-Ratio-Based Gaussian Trust Weight Graph Neural Networks
State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University · Hangzhou Institute of Technology, Xidian University · Department of Computer Science, Purdue University
Tight Stability Bounds for Robust Distributed Learning: Byzantine Failures Hurt Generalization More than Data Poisoning
Jun 22, 2025Thomas Boudou, Batiste Le Bars, Nirupam Gupta +1Byzantine Attacks Learnability
PreMeDICaL team, Inria, Idesp, Inserm, Université de Montpellier, Montpellier, France · Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189, CRIStAL, F-59000 Lille, France · Department of Computer Science, University of Copenhagen, Copenhagen, Danemark
Trust-free Personalized Decentralized Learning
Oct 15, 2024Yawen Li, Yan Li, Junping Du +3Federated Learning Decentralized Learning
Beijing University of Posts and Telecommunications, Beijing, China
FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge
Feb 26, 2024Tianhang Zheng, Yanlu Li, Bohan Deng +1Federated Learning Poisoning
University of Missouri-Kansas City · University of Toronto
DNA: Differentially private Neural Augmentation for contact tracing
Date pendingRob Romijnders, Christos Louizos, Yuki M. Asano +1Covid-19 Δ)$-Differential Privacy
University of Amsterdam · Qualcomm AI research

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Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes

Fred Xu, Thomas Markovich, Florence Regol, Yizhou Sun

University of California, Los Angeles · Block · Mila – Quebec AI Institute

Generalist Graph Anomaly Detection Anomaly Score Graph Anomaly Detection Log-Ratio-Based Gaussian Trust Weight Empirical Bayes Training-Free

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Node-level graph anomaly detection (GAD) identifies nodes whose attributes and interactions deviate from dominant graph regularities. Existing GAD models encode normality and anomaly scoring indirectly through architectures, message passing, reconstruction or contrastive objectives, and tuned score families. This entangles graph trust (how strongly graph structure should define normality), graph-spectral weighting, and anomaly-score choice, yielding scores that are costly, opaque, and unstable across graph regimes. We propose EB-GAD (Empirical-Bayes GAD), a training-free framework that models normality as graph-aware generalized Ornstein-Uhlenbeck (GOU) relaxation toward a graph-filtered template. Empirical Bayes fits the graph precision from the residual-field likelihood; the GOU then turns scoring into a closed-form finite-horizon control energy, the minimum effort to steer a feature-neutral node to its observed endpoint along graph-spectral relaxation. Sweeping relaxation horizon and endpoint tolerance yields a bank of scores that share one fitted prior: equilibrium Mahalanobis scoring is one limit, while finite-horizon control-energy and scale-normalized ratio scores reveal anomalies that static equilibrium scoring can mask. A label-free selector chooses the score family from feature homophily, edge density, and feature dimension, then ranks candidates by fitted-null deviation and rank stability. On 11 benchmarks and without labels at any step, EB-GAD has the best or tied-best AUROC on 9: the four financial fraud networks (up to 3.7M nodes), the YelpChi and Amazon review graphs, Weibo, Reddit and Facebook, with margins of up to 21.7 points. It is second on BlogCatalog and ACM.

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