13 citations · 33 across the 6 of their papers we have counts for
6 papers
Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance
Lisha Chen, Heshan Fernando, Yiming Ying +1
Multi-objective learning (MOL) problems often arise in emerging machine learning problems when there are multiple learning criteria, data modalities, or learning tasks. Different f…
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems
Lisha Chen, Sharu Theresa Jose, Ivana Nikoloska +3
Deep learning has achieved remarkable success in many machine learning tasks such as image classification, speech recognition, and game playing. However, these breakthroughs are of…
Understanding Benign Overfitting in Gradient-Based Meta Learning
Lisha Chen, Songtao Lu, Tianyi Chen
Meta learning has demonstrated tremendous success in few-shot learning with limited supervised data. In those settings, the meta model is usually overparameterized. While the conve…
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
Momin Abbas, Quan Xiao, Lisha Chen +2
Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML can be challenging…
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?
Lisha Chen, Tianyi Chen
Meta learning aims at learning a model that can quickly adapt to unseen tasks. Widely used meta learning methods include model agnostic meta learning (MAML), implicit MAML, Bayesia…
Deep Structured Prediction for Facial Landmark Detection
Lisha Chen, Hui Su, Qiang Ji
Existing deep learning based facial landmark detection methods have achieved excellent performance. These methods, however, do not explicitly embed the structural dependencies amon…