64 citations · 166 across the 10 of their papers we have counts for
10 papers
A Simple yet Effective Self-Debiasing Framework for Transformer Models
Xiaoyue Wang, Lijie Wang, Xin Liu +3
Current Transformer-based natural language understanding (NLU) models heavily rely on dataset biases, while failing to handle real-world out-of-distribution (OOD) instances. Many m…
Should I send this notification? Optimizing push notifications decision making by modeling the future
Conor O'Brien, Huasen Wu, Shaodan Zhai +3
Most recommender systems are myopic, that is they optimize based on the immediate response of the user. This may be misaligned with the true objective, such as creating long term u…
Learning to Rank For Push Notifications Using Pairwise Expected Regret
Yuguang Yue, Yuanpu Xie, Huasen Wu +4
Listwise ranking losses have been widely studied in recommender systems. However, new paradigms of content consumption present new challenges for ranking methods. In this work we c…
Waiting but not Aging: Optimizing Information Freshness Under the Pull Model
Fengjiao Li, Yu Sang, Zhongdong Liu +3
The Age-of-Information is an important metric for investigating the timeliness performance in information-update systems. In this paper, we study the AoI minimization problem under…
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…
Laxity-Based Opportunistic Scheduling with Flow-Level Dynamics and Deadlines
Huasen Wu, Youguang Zhang, Xin Liu
Many data applications in the next generation cellular networks, such as content precaching and video progressive downloading, require flow-level quality of service (QoS) guarantee…
Fast Adaptive S-ALOHA Scheme for Event-driven M2M Communications (Journal version)
Huasen Wu, Chenxi Zhu, Richard J. La +2
Supporting massive device transmission is challenging in Machine-to-Machine (M2M) communications. Particularly, in event-driven M2M communications, a large number of devices activa…
Fast Adaptive S-ALOHA Scheme for Event-driven Machine-to-Machine Communications
Huasen Wu, Chenxi Zhu, Richard J. La +2
Machine-to-Machine (M2M) communication is now playing a market-changing role in a wide range of business world. However, in event-driven M2M communications, a large number of devic…