9 papers
Improved Convergence Rate for Stochastic Multi-Gradient Descent: A Proof Discovered with AI
Lisha Chen
For smooth nonconvex stochastic multi-objective problems, stochastic multi-gradient descent (SMG) computes an approximate steepest common descent direction of the objectives from s…
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…
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…
Efficient Penalty-Based Bilevel Methods: Improved Analysis, Novel Updates, and Flatness Condition
Liuyuan Jiang, Quan Xiao, Lisha Chen +1
Penalty-based methods have become popular for solving bilevel optimization (BLO) problems, thanks to their effective first-order nature. However, they often require inner-loop iter…
BiRQ: Bi-Level Self-Labeling Random Quantization for Self-Supervised Speech Recognition
Liuyuan Jiang, Xiaodong Cui, Brian Kingsbury +2
Speech is a rich signal, and labeled audio-text pairs are costly, making self-supervised learning essential for scalable representation learning. A core challenge in speech SSL is…
Objective Soups: Multilingual Multi-Task Modeling for Speech Processing
A F M Saif, Lisha Chen, Xiaodong Cui +3
Training a single model for multilingual, multi-task speech processing (MSP) is severely hampered by conflicting objectives between tasks like speech recognition and translation. W…