Best paper award for a novel approach to training neural networks

A paper on multi-objective (MO) training of neural networks, written by researchers from CWI and Leiden University Medical Center, has received the best paper award during the international EMO 2023 conference.

Publication date
28 Mar 2023

The paper discusses a problem called MO optimization, which involves finding solutions that balance multiple, often conflicting objectives. For example, in e-commerce, a recommendation system may need to balance between maximizing revenue and customer satisfaction. The paper presents a new method for training neural networks that enables a posteriori MO decision-making and demonstrates its effectiveness in various real-world problems.

Novel approach

The authors propose a novel approach to training neural networks for multi-objective decision-making, where the neural networks generate multiple solutions that span and uniformly cover the Pareto front, which is the set of optimal trade-off solutions. Unlike traditional approaches that require the user to specify the trade-off vectors beforehand, the proposed approach does not require this information and enables a posteriori MO decision-making. The approach uses maximization of hypervolume (HV), which is a metric that measures the quality and diversity of a set of solutions, to train the neural networks.

Well-spread outputs

Experiments conducted on different multi-objective problems show that the proposed approach returns well-spread outputs across different trade-offs on the approximated Pareto front. Especially in cases that are challenging and complex because of an asymmetric Pareto front (when one objective's improvement comes at a high cost to the improvement of other objectives).

EMO 2023 is the 12th Edition of the biannual International Conference Series on Evolutionary Multi-Criterion Optimization (EMO). It focuses on solving real-world problems in government, business, and industry.

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