24 citations · 85 across the 22 of their papers we have counts for
11 papers · 1 filter
Multiple Instance Learning for Uplift Modeling
Yao Zhao, Haipeng Zhang, Shiwei Lyu +3
Uplift modeling is widely used in performance marketing to estimate effects of promotion campaigns (e.g., increase of customer retention rate). Since it is impossible to observe ou…
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
Tianchi Cai, Jiyan Jiang, Wenpeng Zhang +7
We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing b…
Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems
Tianchi Cai, Shenliao Bao, Jiyan Jiang +5
Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards. However, most…
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting
Dan Yang, Binbin Hu, Xiaoyan Yang +4
With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users th…
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning
Weifan Wang, Binbin Hu, Zhicheng Peng +5
Recently, the growth of service platforms brings great convenience to both users and merchants, where the service search engine plays a vital role in improving the user experience…
Edge-cloud Collaborative Learning with Federated and Centralized Features
Zexi Li, Qunwei Li, Yi Zhou +3
Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud serve…
A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Method
Yu Xiang, Guangbo Zhang, Liwei Hu +2
Geometrical shape of airfoils, together with the corresponding flight conditions, are crucial factors for aerodynamic performances prediction. The obtained airfoils geometrical fea…
Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximation
Yuankai Teng, Zhu Wang, Lili Ju +2
Due to the curse of dimensionality and the limitation on training data, approximating high-dimensional functions is a very challenging task even for powerful deep neural networks.…
PI3NN: Out-of-distribution-aware prediction intervals from three neural networks
Siyan Liu, Pei Zhang, Dan Lu +1
We propose a novel prediction interval (PI) method for uncertainty quantification, which addresses three major issues with the state-of-the-art PI methods. First, existing PI metho…
AdaDGS: An adaptive black-box optimization method with a nonlocal directional Gaussian smoothing gradient
Hoang Tran, Guannan Zhang
The local gradient points to the direction of the steepest slope in an infinitesimal neighborhood. An optimizer guided by the local gradient is often trapped in local optima when t…
Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy
Jiaxing Zhang, Hoang Tran, Guannan Zhang
Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks, rivaling other state-of-the-art deep RL methods. Yet, there are two limi…