Search Results for author:
Found 6 papers, 3 papers with code
Multiscale Sensor Fusion and Continuous Control with Neural CDEs
Though robot learning is often formulated in terms of discrete-time Markov decision processes (MDPs), physical robots require near-continuous multiscale feedback control.
Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization
We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics.
Improving Robustness of Machine Translation with Synthetic Noise
Modern Machine Translation (MT) systems perform consistently well on clean, in-domain text.
Learning Stabilizable Dynamical Systems via Control Contraction Metrics
We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics.
Language Informed Modeling of Code-Switched Text
Code-switching (CS), the practice of alternating between two or more languages in conversations, is pervasive in most multi-lingual communities.
Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods
The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order to minimize the expected value of a cost function, i. e., that humans are risk neutral.
