64 citations · 154 across the 12 of their papers we have counts for
12 papers
Toward Exact Convergence in Byzantine-Robust Decentralized Learning: A Statistical Identification Approach
Siyuan Zhang, Chengde Qian, Xin Liu +1
To defend against Byzantine attacks in decentralized learning, most existing methods rely on robust aggregation rules to mitigate the influence of malicious machines. However, thes…
Opportunistic Episodic Reinforcement Learning
Xiaoxiao Wang, Nader Bouacida, Xueying Guo +1
In this paper, we propose and study opportunistic reinforcement learning - a new variant of reinforcement learning problems where the regret of selecting a suboptimal action varies…
Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance
Xiaoxiao Wang, Fanyu Meng, Xin Liu +2
Explainability plays an increasingly important role in machine learning. Furthermore, humans view the world through a causal lens and thus prefer causal explanations over associati…
CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing
Yongshuai Liu, Jiaxin Ding, Zhi-Li Zhang +1
As mobile networks proliferate, we are experiencing a strong diversification of services, which requires greater flexibility from the existing network. Network slicing is proposed…
FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity
Yangfan Zhou, Kaizhu Huang, Cheng Cheng +3
AdaBelief, one of the current best optimizers, demonstrates superior generalization ability compared to the popular Adam algorithm by viewing the exponential moving average of obse…
An Opportunistic Bandit Approach for User Interface Experimentation
Nader Bouacida, Amit Pande, Xin Liu
Facing growing competition from online rivals, the retail industry is increasingly investing in their online shopping platforms to win the high-stake battle of customer' loyalty. U…
Contextual Bandits with Side-Observations
Rahul Singh, Fang Liu, Xin Liu +1
We investigate contextual bandits in the presence of side-observations across arms in order to design recommendation algorithms for users connected via social networks. Users in so…
IPO: Interior-point Policy Optimization under Constraints
Yongshuai Liu, Jiaxin Ding, Xin Liu
In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cum…
AdaLinUCB: Opportunistic Learning for Contextual Bandits
Xueying Guo, Xiaoxiao Wang, Xin Liu
In this paper, we propose and study opportunistic contextual bandits - a special case of contextual bandits where the exploration cost varies under different environmental conditio…
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits
Huasen Wu, Xueying Guo, Xin Liu
In this paper, we propose and study opportunistic bandits - a new variant of bandits where the regret of pulling a suboptimal arm varies under different environmental conditions, s…
Double Thompson Sampling for Dueling Bandits
Huasen Wu, Xin Liu
In this paper, we propose a Double Thompson Sampling (D-TS) algorithm for dueling bandit problems. As indicated by its name, D-TS selects both the first and the second candidates a…
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits
Huasen Wu, R. Srikant, Xin Liu +1
We study contextual bandits with budget and time constraints, referred to as constrained contextual bandits.The time and budget constraints significantly complicate the exploration…