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cs.LG2026
A Unified Framework for Gradient Aggregation in Multi-Objective Optimization
Zeou Hu, Kelvin Ho, Yaoliang Yu
Many machine learning problems involve multiple inherent trade-offs that are best addressed by gradient-based multi-objective optimization (MOO) algorithms. Existing methods are of…
cs.LG2026
Efficient Bilevel Optimization with KFAC-Based Hypergradients
Disen Liao, Felix Dangel, Yaoliang Yu
Bilevel optimization (BO) is widely applicable to many machine learning problems. Scaling BO, however, requires repeatedly computing hypergradients, which involves solving inverse…
cs.LG2024
A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD
Ruinan Jin, Xiao Li, Yaoliang Yu +1
Adaptive Moment Estimation (Adam) is a cornerstone optimization algorithm in deep learning, widely recognized for its flexibility with adaptive learning rates and efficiency in han…