Search Results for author: Chuan Luo

Found 12 papers, 5 papers with code

Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing

1 code implementation4 Feb 2024 Yinqiu Huang, Shuli Wang, Min Gao, Xue Wei, Changhao Li, Chuan Luo, Yinhua Zhu, Xiong Xiao, Yi Luo

ECUP consists of two primary components: 1) the Entire Chain-Enhanced Network, which utilizes user behavior patterns to estimate ITE throughout the entire chain space, models the various impacts of treatments on each task, and integrates task prior information to enhance context awareness across all stages, capturing the impact of treatment on different tasks, and 2) the Treatment-Enhanced Network, which facilitates fine-grained treatment modeling through bit-level feature interactions, thereby enabling adaptive feature adjustment.

Marketing

Contrastive Learning with Negative Sampling Correction

no code implementations13 Jan 2024 Lu Wang, Chao Du, Pu Zhao, Chuan Luo, Zhangchi Zhu, Bo Qiao, Wei zhang, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

To correct the negative sampling bias, we propose a novel contrastive learning method named Positive-Unlabeled Contrastive Learning (PUCL).

Contrastive Learning Data Augmentation +2

HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction

1 code implementation10 Mar 2023 Jie zhou, Xianshuai Cao, Wenhao Li, Lin Bo, Kun Zhang, Chuan Luo, Qian Yu

Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out multi-scenario transfer learning on the basis of the Mixture-of-Expert (MoE) architecture.

Multi-Task Learning Recommendation Systems

Feature Decomposition for Reducing Negative Transfer: A Novel Multi-task Learning Method for Recommender System

1 code implementation10 Feb 2023 Jie zhou, Qian Yu, Chuan Luo, Jing Zhang

In recent years, thanks to the rapid development of deep learning (DL), DL-based multi-task learning (MTL) has made significant progress, and it has been successfully applied to recommendation systems (RS).

Multi-Task Learning Recommendation Systems

Distributed Evolution Strategies for Black-box Stochastic Optimization

no code implementations9 Apr 2022 Xiaoyu He, Zibin Zheng, Chuan Chen, Yuren Zhou, Chuan Luo, QIngwei Lin

This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms.

Evolutionary Algorithms

A Surrogate Objective Framework for Prediction+Programming with Soft Constraints

no code implementations NeurIPS 2021 Kai Yan, Jie Yan, Chuan Luo, Liting Chen, QIngwei Lin, Dongmei Zhang

Prediction+optimization is a common real-world paradigm where we have to predict problem parameters before solving the optimization problem.

Portfolio Optimization

A Surrogate Objective Framework for Prediction+Optimization with Soft Constraints

1 code implementation22 Nov 2021 Kai Yan, Jie Yan, Chuan Luo, Liting Chen, QIngwei Lin, Dongmei Zhang

Prediction+optimization is a common real-world paradigm where we have to predict problem parameters before solving the optimization problem.

Portfolio Optimization

Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property

no code implementations ICLR 2022 Boshi Wang, Jialin Yi, Hang Dong, Bo Qiao, Chuan Luo, QIngwei Lin

Combinatorial optimization problems with parameters to be predicted from side information are commonly seen in a variety of problems during the paradigm shift from reactive decision making to proactive decision making.

Combinatorial Optimization Decision Making

COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution

no code implementations23 Apr 2020 Qingsen Yan, Bo wang, Dong Gong, Chuan Luo, Wei Zhao, Jianhu Shen, Qinfeng Shi, Shuo Jin, Liang Zhang, Zheng You

Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection.

Computed Tomography (CT) Image Segmentation +3

Improving the Performance of Stochastic Local Search for Maximum Vertex Weight Clique Problem Using Programming by Optimization

no code implementations27 Feb 2020 Yi Chu, Chuan Luo, Holger H. Hoos, QIngwei Lin, Haihang You

The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications.

NuMVC: An Efficient Local Search Algorithm for Minimum Vertex Cover

no code implementations4 Feb 2014 Shaowei Cai, Kaile Su, Chuan Luo, Abdul Sattar

These two strategies are used in designing a new MVC local search algorithm, which is referred to as NuMVC.

Combinatorial Optimization

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