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 …

Many real world scientific and industrial applications require optimizing multiple competing
black-box objectives. When the objectives are expensive-to-evaluate, multi-objective …

Expensive multi-objective optimization problems can be found in many real-world
applications, where their objective function evaluations involve expensive computations or …

Deep neural network (DNN) models typically have many hyperparameters that can be
configured to achieve optimal performance on a particular dataset. Practitioners usually tune …

Optimizing expensive-to-evaluate black-box functions of discrete (and potentially
continuous) design parameters is a ubiquitous problem in scientific and engineering …

Recently, there has been a growing surge of interest in enabling machine learning systems
to generalize well to Out-of-Distribution (OOD) data. Most efforts are devoted to advancing …

This paper presents a novel approach to multiobjective algorithms aimed at modeling the
Pareto set using neural networks. Whereas previous methods mainly focused on identifying …

Many real-world problems can be phrased as a multi-objective optimization problem, where
the goal is to identify the best set of compromises between the competing objectives. Multi …

Finding diverse and representative Pareto solutions from the Pareto front is a key challenge
in multi-objective optimization (MOO). In this work, we propose a novel gradient-based …

Machine learning problems with multiple objectives appear either i) in learning with multiple
criteria where learning has to make a trade-off between multiple performance metrics such …