activity
20172023
most citedAuxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing

23 citations · 45 across the 6 of their papers we have counts for

collaborators

9 papers

cs.CL2022

A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS

Wei Shen, Xiaonan He, Chuheng Zhang +2

Recently, spoken dialogue systems have been widely deployed in a variety of applications, serving a huge number of end-users. A common issue is that the errors resulting from noisy…

stat.ML2022

TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets

Yuanying Cai, Chuheng Zhang, Li Zhao +6

We consider an offline reinforcement learning (RL) setting where the agent need to learn from a dataset collected by rolling out multiple behavior policies. There are two challenge…

cs.LG2022

Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation

Guogang Liao, Xiaowen Shi, Ze Wang +5

A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem. Meanwhile, modeling user…

cs.LG202118 cited

Return-Based Contrastive Representation Learning for Reinforcement Learning

Guoqing Liu, Chuheng Zhang, Li Zhao +5

Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxi…

cs.LG2020

DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis

Chuheng Zhang, Yuanqi Li, Xi Chen +3

Modern machine learning models (such as deep neural networks and boosting decision tree models) have become increasingly popular in financial market prediction, due to their superi…

cs.LG202023 cited

Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing

Wei Shen, Xiaonan He, Chuheng Zhang +3

In mobile crowdsourcing (MCS), the platform selects participants to complete location-aware tasks from the recruiters aiming to achieve multiple goals (e.g., profit maximization, e…