Graph Regularization

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9 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Controlled Block Proximal Linearized Method for Nonconvex and Nonsmooth Optimization
Oct 25, 2026Weifeng YangGraph Regularization Proximal Optimization
Graph-Regularized Synthetic Control for Stable Counterfactual Estimation under Donor Redundancy
Oct 16, 2026Yanlin Zhang, Sungyong Chung, Alireza TalebpourSynthetic Control Graph Regularization
University of Illinois Urbana-Champaign
Geographically Regularized AUC-Maximizing Personalized Federated Learning
Sep 8, 2026Mayu Hiraishi, Kensuke Tanioka, Toshio ShimokawaGraph Regularization Clinical Risk Prediction
Department of Medicine · Wakayama Medical University · Wakayama, Japan +3
D-CLOT: Double Closed Loop Optimal Transport for Unsupervised Action Segmentation
Aug 6, 2026Elena Bueno-Benito, Mariella DimiccoliTemporal Action Segmentation Graph Regularization
Institut de Rob`otica i Inform`atica Industrial, CSIC-UPC, Barcelona, Spain
Gauge-Invariant, Parameter-Insensitive Regularization for Potential Recovery from Flow on Directed Graphs
Jul 15, 2026Mohammad ForouheshOversmoothing Inverse Problems
Amirkabir University of Technology, Iran
Graph-Guided Universum Learning in Generalized Eigenvalue Proximal SVMs for Alzheimer's Disease Classification
Jun 3, 2026Yogesh Kumar, Vrushank Ahire, Mudasir GanaieAlzheimer's Disease Medical Image Classification
Dept. of Computer Science and Engineering, IIT Ropar, Punjab 140001, India
Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis
May 26, 2026Yamato Suetake, Yuta Kawakami, Shunnosuke Ikeda +1Privacy-Preserving ML Kernel Methods
Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, Japan · Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability
May 8, 2026Dengzhe Hou, Zihao Wu, Lingyu Jiang +3Uncertainty Quantification EEG Decoding
Tohoku University · University of Georgia · Texas A&M University +1
Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization
Oct 8, 2025Kanglei Zhou, Qingyi Pan, Xingxing Zhang +4Continual Learning Graph Regularization
Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China · Department of Statistics and Data Science, Tsinghua University, Beijing 100084, China · Department of Computer Science and Technology, Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University +3

Oct 25, 2026J/K move · Enter open · S save

Controlled Block Proximal Linearized Method for Nonconvex and Nonsmooth Optimization

Weifeng Yang

Graph Regularization Proximal Optimization Tensor Decomposition Non-Negative Matrix Factorization

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We consider multiblock nonconvex and nonsmooth optimization without assuming bounded iterates or coercivity of the original objective. We propose the Controlled Block Proximal Linearized (CBPL) method, which incorporates a control potential into block updates while retaining the original objective, variables, and constraints. Under explicit control design conditions and the KL property, the control yields boundedness, finite length, whole-sequence convergence of the original variables, and convergence of objective values. At the limit, one limiting subgradient of the original objective vanishes on all effective comparison directions. We apply CBPL to graph-regularized nonnegative matrix factorization (NMF), CP decomposition, and independent-core matrix tri-factorization (NMTF) with

\ell_0

norm sparsity constraints. We characterize their reconstruction-preserving scale degeneracies, proving that scale criticality is equivalent to graph collapse and that full criticality forces such collapse. Explicit controls provide compact sublevel sets and a uniform positive lower bound on graph action, including with an independent Tucker core, while retaining genuine reconstruction-changing directions. Each block admits an explicit projection and a computable safe step parameter. For ordinary NMF and CP models, whole-sequence convergence to a full critical point follows. Experiments with direct safe updates demonstrate convergence, sustained graph action, and controlled factor and core scales.