Image de couverture de livre

Structural Learning Theory: A Metric-Topology Factorization Approach

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Li, Xin
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

A Metric Topology of Deep Learning for Data Classification
par: Wu, Jwo-Yuh, et autres
Publié: (2025)

Memory-Amortized Inference: A Topological Unification of Search, Closure, and Structure
par: Li, Xin
Publié: (2025)

Revisiting Bisimulation Metric for Robust Representations in Reinforcement Learning
par: Zhang, Leiji, et autres
Publié: (2025)

Local Urysohn Width: A Topological Complexity Measure for Classification
par: Li, Xin
Publié: (2026)

Concept Factorization via Self-Representation and Adaptive Graph Structure Learning
par: Yang, Zhengqin, et autres
Publié: (2025)

The Speed-up Factor: A Quantitative Multi-Iteration Active Learning Performance Metric
par: Kath, Hannes, et autres
Publié: (2026)

A Riemannian Approach to Ground Metric Learning for Optimal Transport
par: Jawanpuria, Pratik, et autres
Publié: (2024)

Learning Locally Adaptive Metrics that Enhance Structural Representation with $\texttt{LAMINAR}$
par: Kleiber, Christian, et autres
Publié: (2024)

Time-Series Forecasting via Topological Information Supervised Framework with Efficient Topological Feature Learning
par: Lin, ZiXin, et autres
Publié: (2025)

GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models
par: Li, Ziqi
Publié: (2023)

Topological Metric for Unsupervised Embedding Quality Evaluation
par: Shestov, Aleksei, et autres
Publié: (2025)

PRIVET: Privacy Metric Based on Extreme Value Theory
par: Szatkownik, Antoine, et autres
Publié: (2025)

DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
par: Guo, Lianshuai, et autres
Publié: (2025)

RAGFormer: Learning Semantic Attributes and Topological Structure for Fraud Detection
par: Li, Haolin, et autres
Publié: (2024)

Scalable Deep Metric Learning on Attributed Graphs
par: Li, Xiang, et autres
Publié: (2024)

Generative Flow Networks: Theory and Applications to Structure Learning
par: Deleu, Tristan
Publié: (2025)

FedGES: A Federated Learning Approach for BN Structure Learning
par: Torrijos, Pablo, et autres
Publié: (2025)

Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach
par: Hassouna, Mohamed, et autres
Publié: (2025)

A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors
par: Li, Can, et autres
Publié: (2023)

Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis
par: Kong, Dexu, et autres
Publié: (2024)

Manifold Metric: A Loss Landscape Approach for Predicting Model Performance
par: Malviya, Pranshu, et autres
Publié: (2024)

Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
par: Li, Siqi, et autres
Publié: (2023)

Negative Metric Learning for Graphs
par: Zhao, Yiyang, et autres
Publié: (2025)

Unraveling the Localized Latents: Learning Stratified Manifold Structures in LLM Embedding Space with Sparse Mixture-of-Experts
par: Li, Xin, et autres
Publié: (2025)

A Metric-based Principal Curve Approach for Learning One-dimensional Manifold
par: Cuicizion, Eliuvish
Publié: (2024)

Identifying Metric Structures of Deep Latent Variable Models
par: Syrota, Stas, et autres
Publié: (2025)

Revisable by Design: A Theory of Streaming LLM Agent Execution
par: Zhai, Zhiyuan, et autres
Publié: (2026)

A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms
par: Chen, Weiqin, et autres
Publié: (2024)

NeuralFactors: A Novel Factor Learning Approach to Generative Modeling of Equities
par: Gopal, Achintya
Publié: (2024)

A Metric Driven Approach to Mixed Precision Training
par: Rasquinha, Mitchelle, et autres
Publié: (2024)

Cross-Modal Deep Metric Learning for Time Series Anomaly Detection
par: Li, Wei, et autres
Publié: (2025)

A Deep Dive into the Factors Influencing Financial Success: A Machine Learning Approach
par: Zhou, Michael, et autres
Publié: (2024)

Towards Effective Theory of LLMs: A Representation Learning Approach
par: Ustaomeroglu, Muhammed, et autres
Publié: (2026)

Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures
par: Papillon, Mathilde, et autres
Publié: (2024)

Information as Structural Alignment: A Dynamical Theory of Continual Learning
par: Negulescu, Radu
Publié: (2026)

On the use of Statistical Learning Theory for model selection in Structural Health Monitoring
par: Lindley, C. A., et autres
Publié: (2025)

Bridging Theory and Experiment in Materials Discovery: Machine-Learning-Assisted Prediction of Synthesizable Structures
par: Xin, Yu, et autres
Publié: (2025)

Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological Clustering
par: Masuyama, Naoki, et autres
Publié: (2022)

Metric Learning in an RKHS
par: Tatli, Gokcan, et autres
Publié: (2025)

SDSC:A Structure-Aware Metric for Semantic Signal Representation Learning
par: Lee, Jeyoung, et autres
Publié: (2025)