Revisiting scalarization in multi-task learning: A theoretical perspective
Y Hu, R ** for multi-constraint safe reinforcement learning
Online safe reinforcement learning (RL) involves training a policy that maximizes task
efficiency while satisfying constraints via interacting with the environments. In this paper, our …
efficiency while satisfying constraints via interacting with the environments. In this paper, our …
Min-max multi-objective bilevel optimization with applications in robust machine learning
We consider a generic min-max multi-objective bilevel optimization problem with
applications in robust machine learning such as representation learning and …
applications in robust machine learning such as representation learning and …
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Multi-objective optimization problems can be found in many real-world applications, where
the objectives often conflict each other and cannot be optimized by a single solution. In the …
the objectives often conflict each other and cannot be optimized by a single solution. In the …
Fair Resource Allocation in Multi-Task Learning
By jointly learning multiple tasks, multi-task learning (MTL) can leverage the shared
knowledge across tasks, resulting in improved data efficiency and generalization …
knowledge across tasks, resulting in improved data efficiency and generalization …
Challenging Common Assumptions in Multi-task Learning
While multi-task learning (MTL) has gained significant attention in recent years, its
underlying mechanisms remain poorly understood. Recent methods did not yield consistent …
underlying mechanisms remain poorly understood. Recent methods did not yield consistent …
PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization
Multi-objective optimization (MOO) lies at the core of many machine learning (ML)
applications that involve multiple, potentially conflicting objectives (eg, multi-task learning …
applications that involve multiple, potentially conflicting objectives (eg, multi-task learning …