Tasks in multi-task learning often correlate, conflict, or even compete with each other. As a
result, a single solution that is optimal for all tasks rarely exists. Recent papers introduced …

In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them.
Multi-task learning is inherently a multi-objective problem because different tasks may …

Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously.
However, it is often impossible to find one single solution to optimize all the tasks, since …

Abstract Multi-Task Learning (MTL) is a well established paradigm for jointly learning
models for multiple correlated tasks. Often the tasks conflict, requiring trade-offs between …

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 …

Machine learning is inherently a multiobjective task. Traditionally, however, either only one
of the objectives is adopted as the cost function or multiple objectives are aggregated to a …

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

Multi-objective optimization (MOO) and multi-task learning (MTL) have gained much
popularity with prevalent use cases such as production model development of regression …

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 …

A multi-task learning (MTL) system aims at solving multiple related tasks at the same time.
With a fixed model capacity, the tasks would be conflicted with each other, and the system …