We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learning such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. Moreover, these objectives are often imperfect evaluations of some underlying property of interest, making it important to generate diverse candidates to have multiple options for expensive downstream evaluations. We propose Multi-Objective GFlowNets (MOGFNs), a novel method for generating diverse Pareto optimal solutions, based on GFlowNets. We introduce two variants of MOGFNs: MOGFN-PC, which models a family of independent sub-problems defined by a scalarization function, with reward-conditional GFlowNets, and MOGFN-AL, which solves a sequence of sub-problems defined by an acquisition function in an active learning loop. Our experiments on wide variety of synthetic and benchmark tasks demonstrate advantages of the proposed methods in terms of the Pareto performance and importantly, improved candidate diversity, which is the main contribution of this work.

翻译：我们研究了多目标优化背景下生成多样化候选解的问题。在机器学习的许多应用（如药物发现和材料设计）中，目标是生成能够同时优化一组潜在冲突目标的候选解。此外，这些目标往往是对感兴趣的潜在特性的不完美评估，因此生成多样化的候选解至关重要，以便为昂贵的下游评估提供多种选择。我们提出了多目标GFlowNets（MOGFNs），一种基于GFlowNets生成多样化Pareto最优解的新方法。我们介绍了MOGFNs的两种变体：MOGFN-PC，它通过标量化函数建模一系列独立子问题，并使用奖励条件GFlowNets；以及MOGFN-AL，它在主动学习循环中通过采集函数求解一系列子问题。我们在多种合成任务和基准任务上的实验表明，所提出的方法在Pareto性能上具有优势，更重要的是，改善了候选解的多样性，这也是本研究的主要贡献。