Ding Zhao

Functional Optimal Transport: Mapping Estimation and Domain Adaptation for Functional data.

2021 · arXiv (Cornell University)

We introduce a formulation of optimal transport problem for distributions on function spaces, where the stochastic map between functional domains can be partially represented in terms of an (infinite-dimensional) Hilbert-Schmidt operator mapping a Hilbert space …

Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training

2022 · arXiv (Cornell University)

Robust Reinforcement Learning (RL) focuses on improving performances under model errors or adversarial attacks, which facilitates the real-life deployment of RL agents. Robust Adversarial Reinforcement Learning (RARL) is one of the most popular frameworks for …

Federated Pruning: Improving Neural Network Efficiency with Federated Learning

2022 · arXiv (Cornell University)

Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns. Federated learning has been widely used and is considered to be an …

Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation

2023 · arXiv (Cornell University)

Robustness has been extensively studied in reinforcement learning (RL) to handle various forms of uncertainty such as random perturbations, rare events, and malicious attacks. In this work, we consider one critical type of robustness against …