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Regret Analysis of Multi-task Representation Learning for
  Linear-Quadratic Adaptive Control

Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control

8 July 2024
Bruce D. Lee
Leonardo F. Toso
Thomas T. Zhang
James Anderson
Nikolai Matni
ArXivPDFHTML

Papers citing "Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control"

7 / 7 papers shown
Title
Learning Stabilizing Policies via an Unstable Subspace Representation
Learning Stabilizing Policies via an Unstable Subspace Representation
Leonardo F. Toso
Lintao Ye
James Anderson
19
0
0
02 May 2025
Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
Donglin Zhan
Leonardo F. Toso
James Anderson
88
1
0
04 Feb 2025
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a
  Handful of Trials
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials
Aviral Kumar
Anika Singh
F. Ebert
Mitsuhiko Nakamoto
Yanlai Yang
Chelsea Finn
Sergey Levine
OffRL
OnRL
120
64
0
11 Oct 2022
Minimal Expected Regret in Linear Quadratic Control
Minimal Expected Regret in Linear Quadratic Control
Yassir Jedra
Alexandre Proutière
OffRL
19
17
0
29 Sep 2021
Robust Online Control with Model Misspecification
Robust Online Control with Model Misspecification
Xinyi Chen
Udaya Ghai
Elad Hazan
Alexandre Megretski
13
3
0
16 Jul 2021
FedProto: Federated Prototype Learning across Heterogeneous Clients
FedProto: Federated Prototype Learning across Heterogeneous Clients
Yue Tan
Guodong Long
Lu Liu
Tianyi Zhou
Qinghua Lu
Jing Jiang
Chengqi Zhang
FedML
138
287
0
01 May 2021
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
237
11,568
0
09 Mar 2017
1