NeurIPS · 2024
Disentangling Linear Quadratic Control with Untrusted ML Predictions
Advances in Neural Information Processing Systems
Can a controller learn which components of a forecast to trust?
DISC learns confidence in predictions of latent disturbance components. Competitive analysis covers linear and more general mixing functions, showing how online confidence adaptation can exploit accurate forecasts while maintaining protection against large errors.
Research themes
Connections
From prediction error to control performance
Trust adaptation in linear quadratic control and perturbation bounds for MPC connect the quality of a forecast to the cost of acting on it. Nonlinear certification adds stability to this picture.
Open questions
Prediction quality as a decision resource
Which prediction errors actually matter for the decision being made?
Explore open directionsCite this paper
Tongxin Li, Hao Liu, Yisong Yue. Disentangling Linear Quadratic Control with Untrusted ML Predictions. Advances in Neural Information Processing Systems, 2024. https://papers.nips.cc/paper_files/paper/2024/hash/9dff3b83d463fab213941bfee23341ba-Abstract-Conference.html
BibTeX
@inproceedings{tongxin-disc,
title = {{Disentangling Linear Quadratic Control with Untrusted ML Predictions}},
author = {Tongxin Li and Hao Liu and Yisong Yue},
year = {2024},
booktitle = {Advances in Neural Information Processing Systems},
url = {https://papers.nips.cc/paper\_files/paper/2024/hash/9dff3b83d463fab213941bfee23341ba-Abstract-Conference.html}
}
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