mlsanityData to 14 Oct 2026

Learning Personalized Models with Clustered System Identification

arXiv 2304.01395 ↗First posted 3 Apr 2023math.OC cs.LG cs.SYcited by 11

Not on the map. Why?

Abstract (arXiv)

We address the problem of learning linear system models from observing multiple trajectories from different system dynamics. This framework encompasses a collaborative scenario where several systems seeking to estimate their dynamics are partitioned into clusters according to their system similarity. Thus, the systems within the same cluster can benefit from the observations made by the others. Considering this framework, we present an algorithm where each system alternately estimates its cluster identity and performs an estimation of its dynamics. This is then aggregated to update the model of each cluster. We show that under mild assumptions, our algorithm correctly estimates the cluster identities and achieves an approximate sample complexity that scales inversely with the number of systems in the cluster, thus facilitating a more efficient and personalized system identification process.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 3 Apr 2023.

Builds on

18 of 37 references.

References · 18

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  2. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  3. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  4. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  5. 2017On the Sample Complexity of the Linear Quadratic Regulatorcited by 159
  6. 2018Learning Without Mixing: Towards A Sharp Analysis of Linear System Identificationcited by 158
  7. 2014Statistical guarantees for the EM algorithm: From population to sample-based analysiscited by 133
  8. 2018Near optimal finite time identification of arbitrary linear dynamical systemscited by 89
  9. 2018Non-asymptotic Identification of LTI Systems from a Single Trajectorycited by 78
  10. 2019Learning Linear Dynamical Systems with Semi-Parametric Least Squarescited by 58
  11. 2020Non-asymptotic Identification of Linear Dynamical Systems Using Multiple Trajectoriescited by 38
  12. 2022Multi-Task Imitation Learning for Linear Dynamical Systemscited by 23
6 more
  1. 2022FedSysID: A Federated Approach to Sample-Efficient System Identificationcited by 17
  2. 2017Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identificationcited by 16
  3. 2022Identifying the Dynamics of a System by Leveraging Data from Similar Systemscited by 15
  4. 2023Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systemscited by 8
  5. 2023Learning Dynamical Systems by Leveraging Data from Similar Systemscited by 8
  6. 2022An Improved Algorithm for Clustered Federated Learningcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 5; 2026: 1.

11 papers.

  1. 2024Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQRcited by 17
  2. 2025Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learningcited by 6
  3. 2024Asynchronous Heterogeneous Linear Quadratic Regulator Designcited by 4
  4. 2024Expert with Clustering: Hierarchical Online Preference Learning Frameworkcited by 3
  5. 2024Combining Federated Learning and Control: A Surveycited by 3
  6. 2025Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraftcited by 2
  7. 2025Adversarially Robust Multitask Adaptive Controlcited by 1
  8. 2025Harnessing Data from Clustered LQR Systems: Personalized and Collaborative Policy Optimizationcited by 1
  9. 2024The Nah Bandit: Modeling User Non-compliance in Recommendation Systemscited by 0
  10. 2025A State Alignment-Centric Approach to Federated System Identification: The FedAlign Frameworkcited by 0
  11. 2026Eigenspace-Based Clustering for Personalized System Identificationcited by 0