Image de couverture de livre

A Mathematical Model of the Hidden Feedback Loop Effect in Machine Learning Systems

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Veprikov, Andrey, Afanasiev, Alexander, Khritankov, Anton
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Systems and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning
par: Davydov, Alexander
Publié: (2025)

Efficient Interaction-Aware Interval Analysis of Neural Network Feedback Loops
par: Jafarpour, Saber, et autres
Publié: (2023)

WeightLoRA: Keep Only Necessary Adapters
par: Veprikov, Andrey, et autres
Publié: (2025)

Heat Death of Generative Models in Closed-Loop Learning
par: Marchi, Matteo, et autres
Publié: (2024)

Constraint-Aware Refinement for Safety Verification of Neural Feedback Loops
par: Rober, Nicholas, et autres
Publié: (2024)

Differentiable Filtering for Learning Hidden Markov Models
par: Chen, Reginald Zhiyan, et autres
Publié: (2025)

A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Control
par: Hirt, Sebastian, et autres
Publié: (2025)

Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Control
par: Hirt, Sebastian, et autres
Publié: (2024)

High-Dimensional Surrogate Modeling for Closed-Loop Learning of Neural-Network-Parameterized Model Predictive Control
par: Hirt, Sebastian, et autres
Publié: (2025)

From Big Data to Fast Data: Towards High-Quality Datasets for Machine Learning Applications from Closed-Loop Data Collection
par: Reis, Philipp, et autres
Publié: (2026)

Neural Operators for Predictor Feedback Control of Nonlinear Delay Systems
par: Bhan, Luke, et autres
Publié: (2024)

Modular Distributed Nonconvex Learning with Error Feedback
par: Carnevale, Guido, et autres
Publié: (2025)

Controlling Dynamical Systems into Unseen Target States Using Machine Learning
par: Köglmayr, Daniel, et autres
Publié: (2024)

Machine Learning and System Identification for Estimation in Physical Systems
par: Carlson, Fredrik Bagge
Publié: (2019)

Non-Asymptotic Bounds for Closed-Loop Identification of Unstable Nonlinear Stochastic Systems
par: Siriya, Seth, et autres
Publié: (2024)

Symmetric Linear Dynamical Systems are Learnable from Few Observations
par: Vu, Minh, et autres
Publié: (2025)

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency
par: Zhou, Yuqi, et autres
Publié: (2024)

Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning
par: Wang, Cangqing, et autres
Publié: (2024)

LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning
par: Jian, Pingcheng, et autres
Publié: (2025)

Model-Free Output Feedback Stabilization via Policy Gradient Methods
par: Zhang, Ankang, et autres
Publié: (2026)

A Cyber Manufacturing IoT System for Adaptive Machine Learning Model Deployment by Interactive Causality Enabled Self-Labeling
par: Ren, Yutian, et autres
Publié: (2024)

Dual Filter: A Transformer-like Inference Architecture for Hidden Markov Models
par: Chang, Heng-Sheng, et autres
Publié: (2025)

A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements
par: Ma, Mason, et autres
Publié: (2023)

FedECADO: A Dynamical System Model of Federated Learning
par: Agarwal, Aayushya, et autres
Publié: (2024)

A Systems Theoretic Approach to Online Machine Learning
par: Preez, Anli du, et autres
Publié: (2024)

Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning
par: Forootani, Ali, et autres
Publié: (2024)

Where Does Warm-Up Come From? Adaptive Scheduling for Norm-Constrained Optimizers
par: Riabinin, Artem, et autres
Publié: (2026)

A Supervised Machine-Learning Approach For Turboshaft Engine Dynamic Modeling Under Real Flight Conditions
par: Paniccia, Damiano, et autres
Publié: (2025)

Score Matching Diffusion Based Feedback Control and Planning of Nonlinear Systems
par: Elamvazhuthi, Karthik, et autres
Publié: (2025)

A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
par: Gokhale, Anand, et autres
Publié: (2026)

End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control
par: Mayfrank, Daniel, et autres
Publié: (2023)

Machine Learning and Deep Learning Models for Short Term Electricity Price Forecasting in Australia's National Electricity Market
par: Lu, Wei, et autres
Publié: (2026)

Generalizable Machine Learning Models for Predicting Data Center Server Power, Efficiency, and Throughput
par: Lei, Nuoa, et autres
Publié: (2025)

Closed-Loop Koopman Operator Approximation
par: Dahdah, Steven, et autres
Publié: (2023)

Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification
par: Grigorev, Artur, et autres
Publié: (2024)

The Wisdom of the Crowd: High-Fidelity Classification of Cyber-Attacks and Faults in Power Systems Using Ensemble and Machine Learning
par: Abukhousa, Emad, et autres
Publié: (2025)

Optimal Derivative Feedback Control for an Active Magnetic Levitation System: An Experimental Study on Data-Driven Approaches
par: Omidi, Saber, et autres
Publié: (2026)

Automatic dimensionality reduction of Twin-in-the-Loop Observers
par: Delcaro, Giacomo, et autres
Publié: (2024)

Predictive Maintenance Optimization for Smart Vending Machines Using IoT and Machine Learning
par: Hasan, Md. Nisharul
Publié: (2025)

Stable Linear Subspace Identification: A Machine Learning Approach
par: Di Natale, Loris, et autres
Publié: (2023)