255 citations · 455 across the 11 of their papers we have counts for
11 papers
Optimistic and Adaptive Lagrangian Hedging
Ryan D'Orazio, Ruitong Huang
In online learning an algorithm plays against an environment with losses possibly picked by an adversary at each round. The generality of this framework includes problems that are…
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
Zihan Ding, Pablo Hernandez-Leal, Gavin Weiguang Ding +2
Deep Reinforcement Learning (DRL) has recently achieved significant advances in various domains. However, explaining the policy of RL agents still remains an open problem due to se…
On the Sensitivity of Adversarial Robustness to Input Data Distributions
Gavin Weiguang Ding, Kry Yik Chau Lui, Xiaomeng Jin +2
Neural networks are vulnerable to small adversarial perturbations. Existing literature largely focused on understanding and mitigating the vulnerability of learned models. In this…
Few-Shot Self Reminder to Overcome Catastrophic Forgetting
Junfeng Wen, Yanshuai Cao, Ruitong Huang
Deep neural networks are known to suffer the catastrophic forgetting problem, where they tend to forget the knowledge from the previous tasks when sequentially learning new tasks.…
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui +1
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 boundar…
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds
Kry Yik Chau Lui, Gavin Weiguang Ding, Ruitong Huang +1
In this paper, we investigate Dimensionality reduction (DR) maps in an information retrieval setting from a quantitative topology point of view. In particular, we show that no DR m…
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui +1
We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs). The motivation is that when the discriminator D spreads out its model capacity in…
Structured Best Arm Identification with Fixed Confidence
Ruitong Huang, Mohammad M. Ajallooeian, Csaba Szepesvári +1
We study the problem of identifying the best action among a set of possible options when the value of each action is given by a mapping from a number of noisy micro-observables in…
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities
Ruitong Huang, Tor Lattimore, András György +1
The follow the leader (FTL) algorithm, perhaps the simplest of all online learning algorithms, is known to perform well when the loss functions it is used on are convex and positiv…
Revise Saturated Activation Functions
Bing Xu, Ruitong Huang, Mu Li
In this paper, we revise two commonly used saturated functions, the logistic sigmoid and the hyperbolic tangent (tanh). We point out that, besides the well-known non-zero centered…
Learning with a Strong Adversary
Ruitong Huang, Bing Xu, Dale Schuurmans +1
The robustness of neural networks to intended perturbations has recently attracted significant attention. In this paper, we propose a new method, \emph{learning with a strong adver…