Two papers accepted to ICML 2020


Two papers on the application of multi-objective optimization techniques in supervised learning and reinforcement learning have been accepted to ICML 2020: Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control from Jie Xu, Yunsheng Tian, and Pingchuan Ma proposed an efficient evolutionary learning algorithm to find the Pareto set approximation for continuous robot control problems, and Efficient Continuous Pareto Exploration in Multi-Task Learning from Pingchuan Ma and Tao Du presented a novel, efficient method that generates locally continuous Pareto sets and Pareto fronts for multi-task learning problems. Details about the two papers will be available soon.


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