no code implementations • 23 Jan 2021 • Ryan D'Orazio, Ruitong Huang
The generality of this framework includes problems that are not adversarial, for example offline optimization, or saddle point problems (i. e. min max optimization).
1 code implementation • 15 Nov 2020 • Zihan Ding, Pablo Hernandez-Leal, Gavin Weiguang Ding, Changjian Li, Ruitong Huang
As a second contribution our study reveals limitations of explaining black-box policies via imitation learning with tree-based explainable models, due to its inherent instability.
no code implementations • NeurIPS 2019 • Chenjun Xiao, Ruitong Huang, Jincheng Mei, Dale Schuurmans, Martin Müller
We then extend this approach to general sequential decision making by developing a general MCTS algorithm, Maximum Entropy for Tree Search (MENTS).
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.
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.
no code implementations • 3 Dec 2018 • Junfeng Wen, Yanshuai Cao, Ruitong Huang
We demonstrate the superiority of our method to the previous ones in two different continual learning settings on popular benchmarks, as well as a new continual learning problem where tasks are designed to be more dissimilar.
no code implementations • NeurIPS 2018 • Kry Yik Chau Lui, Gavin Weiguang Ding, Ruitong Huang, Robert J. McCann
In this paper, we investigate Dimensionality reduction (DR) maps in an information retrieval setting from a quantitative topology point of view.
1 code implementation • ICLR 2018 • Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui, Ruitong Huang
We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs).
no code implementations • 16 Jun 2017 • Ruitong Huang, Mohammad M. Ajallooeian, Csaba Szepesvári, Martin Müller
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 the so-called fixed confidence setting.
no code implementations • NeurIPS 2016 • Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
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 positively curved.
no code implementations • 18 Feb 2016 • Bing Xu, Ruitong Huang, Mu Li
In this paper, we revise two commonly used saturated functions, the logistic sigmoid and the hyperbolic tangent (tanh).
1 code implementation • 10 Nov 2015 • Ruitong Huang, Bing Xu, Dale Schuurmans, Csaba Szepesvari
The robustness of neural networks to intended perturbations has recently attracted significant attention.