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cs.LG2026
Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control
Chentong Huang, Lisha Chen
Multi-objective learning (MOL) aims to optimize multiple objectives simultaneously. The multi-gradient descent algorithm (MGDA) is a workhorse that iteratively updates along a comm…
cs.LG2026
SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front
Liuyuan Jiang, Chentong Huang, Lisha Chen
Scalarization is widely used in multi-objective optimization owing to its simplicity and scalability. In many applications, the goal is to generate solutions that represent diverse…
cs.LG2024
FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning
Lisha Chen, AFM Saif, Yanning Shen +1
Finding specific preference-guided Pareto solutions that represent different trade-offs among multiple objectives is critical yet challenging in multi-objective problems. Existing…