13 citations · 51 across the 17 of their papers we have counts for
9 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…
ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference
Tianchi Cai, Xierui Song, Jiyan Jiang +3
Aligning language models to human expectations, e.g., being helpful and harmless, has become a pressing challenge for large language models. A typical alignment procedure consists…
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…
Adversarial Learning for Incentive Optimization in Mobile Payment Marketing
Xuanying Chen, Zhining Liu, Li Yu +5
Many payment platforms hold large-scale marketing campaigns, which allocate incentives to encourage users to pay through their applications. To maximize the return on investment, i…
A framework for massive scale personalized promotion
Yitao Shen, Yue Wang, Xingyu Lu +6
Technology companies building consumer-facing platforms may have access to massive-scale user population. In recent years, promotion with quantifiable incentive has become a popula…
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning
Tianchi Cai, Wenpeng Zhang, Lihong Gu +2
Although well-established in general reinforcement learning (RL), value-based methods are rarely explored in constrained RL (CRL) for their incapability of finding policies that ca…
Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction
Yun Yue, Yongchao Liu, Suo Tong +7
We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, Ada…