Search Results for author: QIngwei Lin

Found 6 papers, 1 papers with code

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.

Stochastic Optimization

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

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.

Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning

no code implementations20 May 2019 Xu Zhang, Yang Yao, Baile Xu, Lekun Mao, Furao Shen, Jian Zhao, QIngwei Lin

In this paper, it is the first time to discuss the difficulty without support of old classes in class incremental learning, which is called as softmax suppression problem.

class-incremental learning Incremental Learning +1

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