60 citations · 97 across the 12 of their papers we have counts for
12 papers
A Review on Machine Theory of Mind
Yuanyuan Mao, Shuang Liu, Pengshuai Zhao +3
Theory of Mind (ToM) is the ability to attribute mental states to others, the basis of human cognition. At present, there has been growing interest in the AI with cognitive abiliti…
Achieve Optimal Adversarial Accuracy for Adversarial Deep Learning using Stackelberg Game
Xiao-Shan Gao, Shuang Liu, Lijia Yu
Adversarial deep learning is to train robust DNNs against adversarial attacks, which is one of the major research focuses of deep learning. Game theory has been used to answer some…
Improving Policy Optimization with Generalist-Specialist Learning
Zhiwei Jia, Xuanlin Li, Zhan Ling +3
Generalization in deep reinforcement learning over unseen environment variations usually requires policy learning over a large set of diverse training variations. We empirically ob…
Provably Efficient Kernelized Q-Learning
Shuang Liu, Hao Su
We propose and analyze a kernelized version of Q-learning. Although a kernel space is typically infinite-dimensional, extensive study has shown that generalization is only affected…
Task Guided Compositional Representation Learning for ZDA
Shuang Liu, Mete Ozay
Zero-shot domain adaptation (ZDA) methods aim to transfer knowledge about a task learned in a source domain to a target domain, while data from target domain are not available. In…
GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback
Minghui Huang, Dong Wang, Shuang Liu +1
Query expansion with pseudo-relevance feedback (PRF) is a powerful approach to enhance the effectiveness in information retrieval. Recently, with the rapid advance of deep learning…
Regret Bounds for Discounted MDPs
Shuang Liu, Hao Su
Reinforcement learning (RL) has traditionally been understood from an episodic perspective; the concept of non-episodic RL, where there is no restart and therefore no reliable reco…
Multi-task Batch Reinforcement Learning with Metric Learning
Jiachen Li, Quan Vuong, Shuang Liu +5
We tackle the Multi-task Batch Reinforcement Learning problem. Given multiple datasets collected from different tasks, we train a multi-task policy to perform well in unseen tasks…
The Inductive Bias of Restricted f-GANs
Shuang Liu, Kamalika Chaudhuri
Generative adversarial networks are a novel method for statistical inference that have achieved much empirical success; however, the factors contributing to this success remain ill…
Approximation and Convergence Properties of Generative Adversarial Learning
Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri
Generative adversarial networks (GAN) approximate a target data distribution by jointly optimizing an objective function through a "two-player game" between a generator and a discr…
A Parallel algorithm for -Armed bandits
Cheng Chen, Shuang Liu, Zhihua Zhang +1
The target of -armed bandit problem is to find the global maximum of an unknown stochastic function , given a finite budget of evaluations. Recently, $\mathcal{…
Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and Beyond
Shuang Liu, Cheng Chen, Zhihua Zhang
In this paper, we consider the distributed stochastic multi-armed bandit problem, where a global arm set can be accessed by multiple players independently. The players are allowed…