Search Results for author: Kry Yik Chau Lui

Found 5 papers, 1 papers with code

Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets

no code implementations16 Jul 2021 Yue Gao, Kry Yik Chau Lui, Pablo Hernandez-Leal

Trading markets represent a real-world financial application to deploy reinforcement learning agents, however, they carry hard fundamental challenges such as high variance and costly exploration.

reinforcement-learning Reinforcement Learning (RL)

On the Sensitivity of Adversarial Robustness to Input Data Distributions

no code implementations ICLR 2019 Gavin Weiguang Ding, Kry Yik Chau Lui, Xiaomeng Jin, Luyu Wang, Ruitong Huang

Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution.

Adversarial Robustness

MMA Training: Direct Input Space Margin Maximization through Adversarial Training

1 code implementation ICLR 2020 Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, Ruitong Huang

We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary.

Adversarial Defense Adversarial Robustness

Implicit Manifold Learning on Generative Adversarial Networks

no code implementations30 Oct 2017 Kry Yik Chau Lui, Yanshuai Cao, Maxime Gazeau, Kelvin Shuangjian Zhang

This paper raises an implicit manifold learning perspective in Generative Adversarial Networks (GANs), by studying how the support of the learned distribution, modelled as a submanifold $\mathcal{M}_{\theta}$, perfectly match with $\mathcal{M}_{r}$, the support of the real data distribution.

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