Linear scalarization, ie, combining all loss functions by a weighted sum, has been the
default choice in the literature of multi-task learning (MTL) since its inception. In recent years …

Multi-task learning, which optimizes performance across multiple tasks, is inherently a multi-
objective optimization problem. Various algorithms are developed to provide discrete trade …

Recent advancements in the realm of deep generative models focus on generating samples
that satisfy multiple desired properties. However, prevalent approaches optimize these …

The Federated Learning paradigm facilitates effective distributed machine learning in
settings where training data is decentralized across multiple clients. As the popularity of the …

One of the key challenges of learning an online recommendation model is the temporal
domain shift, which causes the mismatch between the training and testing data distribution …

Fruit counting is a fundamental task for fruit yield estimation. Though semi-supervised
counting methods have received increased attention in recent years, due to the high data …

In multi-objective optimization, a single decision vector must balance the trade-offs between
many objectives. Solutions achieving an optimal trade-off are said to be Pareto optimal …

Multiobjective optimization (MOO) is prevalent in numerous applications, in which a Pareto
front (PF) is constructed to display optima under various preferences. Previous methods …

Session-based recommendation (SBR) predicts potential items of interest by analyzing user
behavior within sessions. In this work, we explore the continual learning for SBR task, a …

Abstract Pareto Front Learning (PFL) has been one of the effective means to resolve multi-
objective optimization problems through exploring all optimal solutions to learn the entire …