Imagen de Portada

A decision-theoretic model for a principal-agent collaborative learning problem

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Befekadu, Getachew K
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

On characterizing optimal learning trajectories in a class of learning problems
por: Befekadu, Getachew K
Publicado: (2025)

A brief note on learning problem with global perspectives
por: Befekadu, Getachew K.
Publicado: (2026)

A naive aggregation algorithm for improving generalization in a class of learning problems
por: Befekadu, Getachew K
Publicado: (2024)

A successive approximation method in functional spaces for hierarchical optimal control problems and its application to learning
por: Befekadu, Getachew K.
Publicado: (2024)

On improving generalization in a class of learning problems with the method of small parameters for weakly-controlled optimal gradient systems
por: Befekadu, Getachew K.
Publicado: (2024)

Simulation-based Bayesian inference with ameliorative learned summary statistics -- Part I
por: Befekadu, Getachew K.
Publicado: (2026)

Embedding generalization within the learning dynamics: An approach based-on sample path large deviation theory
por: Befekadu, Getachew K.
Publicado: (2024)

A new perspective on the learning dynamics for a class of learning problems via averaged gradient systems coupled with diffusion-transmutation processes
por: Befekadu, Getachew K.
Publicado: (2024)

Further extensions on the successive approximation method for hierarchical optimal control problems and its application to learning
por: Befekadu, Getachew K.
Publicado: (2024)

Fast decision tree learning solves hard coding-theoretic problems
por: Koch, Caleb, et al.
Publicado: (2024)

An active learning method for solving competitive multi-agent decision-making and control problems
por: Fabiani, Filippo, et al.
Publicado: (2022)

AOAD-MAT: Transformer-based multi-agent deep reinforcement learning model considering agents' order of action decisions
por: Takayama, Shota, et al.
Publicado: (2025)

Game-theoretic distributed learning of generative models for heterogeneous data collections
por: Schlesinger, Dmitrij, et al.
Publicado: (2025)

Multi-agent decision making: A Blackwell's informativeness approach
por: Zhang, Zheng, et al.
Publicado: (2026)

A proof of contribution in blockchain using game theoretical deep learning model
por: Wang, Jin
Publicado: (2024)

Joint modeling for learning decision-making dynamics in behavioral experiments
por: Bian, Yuan, et al.
Publicado: (2025)

Robust agents learn causal world models
por: Richens, Jonathan, et al.
Publicado: (2024)

In-context learning agents are asymmetric belief updaters
por: Schubert, Johannes A., et al.
Publicado: (2024)

FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration
por: Imakura, Akira, et al.
Publicado: (2024)

Adaptive collaboration for online personalized distributed learning with heterogeneous clients
por: Philippenko, Constantin, et al.
Publicado: (2025)

Kernel conditional tests from learning-theoretic bounds
por: Massiani, Pierre-François, et al.
Publicado: (2025)

Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification
por: Bench, Ciaran
Publicado: (2026)

LookALike: Human Mimicry based collaborative decision making
por: Karanjai, Rabimba, et al.
Publicado: (2024)

BACON: A fully explainable AI model with graded logic for decision making problems
por: Bai, Haishi, et al.
Publicado: (2025)

On the influence of dependent features in classification problems: a game-theoretic perspective
por: Davila-Pena, Laura, et al.
Publicado: (2024)

Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
por: Lei, Yuan-Zheng, et al.
Publicado: (2025)

Imitation learning for clinical decision support in pediatric ECMO
por: Golivand, Fateme, et al.
Publicado: (2026)

Information theoretic underpinning of self-supervised learning by clustering
por: Kittler, Josef, et al.
Publicado: (2026)

Using causal abstractions to accelerate decision-making in complex bandit problems
por: Dyer, Joel, et al.
Publicado: (2025)

Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks
por: Suttle, Wesley A, et al.
Publicado: (2025)

Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
por: Lee, Jason D., et al.
Publicado: (2024)

A theoretical framework for self-supervised contrastive learning for continuous dependent data
por: Marusov, Alexander, et al.
Publicado: (2025)

Periodic agent-state based Q-learning for POMDPs
por: Sinha, Amit, et al.
Publicado: (2024)

Foreclassing: A new machine learning perspective on human decision making with temporal data
por: Coulson, Daniel Andrew, et al.
Publicado: (2025)

Classification problem in liability insurance using machine learning models: a comparative study
por: Qazvini, Marjan
Publicado: (2024)

Estimation of heterogeneous principal effects under principal ignorability
por: Zhang, Rui, et al.
Publicado: (2026)

Aligning LLM agents with human learning and adjustment behavior: a dual agent approach
por: Liu, Tianming, et al.
Publicado: (2025)

Unified token representations for sequential decision models
por: Tian, Zhuojing, et al.
Publicado: (2025)

The geometry of invariant learning: an information-theoretic analysis of data augmentation and generalization
por: Bouyahia, Abdelali, et al.
Publicado: (2026)

Bayesian learning of the optimal action-value function in a Markov decision process
por: Guo, Jiaqi, et al.
Publicado: (2025)