Search Results for author: Thomas T. C. K. Zhang

Found 4 papers, 1 papers with code

TaSIL: Taylor Series Imitation Learning

1 code implementation30 May 2022 Daniel Pfrommer, Thomas T. C. K. Zhang, Stephen Tu, Nikolai Matni

We propose Taylor Series Imitation Learning (TaSIL), a simple augmentation to standard behavior cloning losses in the context of continuous control.

Continuous Control Imitation Learning

Performance-Robustness Tradeoffs in Adversarially Robust Linear-Quadratic Control

no code implementations21 Mar 2022 Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni

Though this fundamental tradeoff between nominal performance and robustness is known to exist, it is not well-characterized in quantitative terms.

Adversarially Robust Stability Certificates can be Sample-Efficient

no code implementations20 Dec 2021 Thomas T. C. K. Zhang, Stephen Tu, Nicholas M. Boffi, Jean-Jacques E. Slotine, Nikolai Matni

Motivated by bridging the simulation to reality gap in the context of safety-critical systems, we consider learning adversarially robust stability certificates for unknown nonlinear dynamical systems.

Adversarial Tradeoffs in Linear Inverse Problems and Robust State Estimation

no code implementations17 Nov 2021 Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni

Adversarially robust training has been shown to reduce the susceptibility of learned models to targeted input data perturbations.

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