3 citations · 6 across the 5 of their papers we have counts for
6 papers
MODAIC: Coordinated Asynchrony for Decentralized Multimodal Learning
Yanhang Shi, Xiaoyu Wang, Houwei Cao +2
Multimodal decentralized learning enables collaboration across heterogeneous edge devices without sharing raw data, but differing modality sets create coupled computation and commu…
When Does -Boosting Overfit Benignly? High-Dimensional Risk Asymptotics and the Implicit Bias
Ye Su, Jian Li, Yong Liu
Benign overfitting is well-characterized in geometries, but its behavior under the implicit bias of greedy ensembles remains challenging. The analytical barrier s…
PID-Guided Partial Alignment for Multimodal Decentralized Federated Learning
Yanhang Shi, Xiaoyu Wang, Houwei Cao +2
Multimodal decentralized federated learning (DFL) must support collaboration among agents that hold different modality subsets and often different model components, while operating…
Operation-level Progressive Differentiable Architecture Search
Xunyu Zhu, Jian Li, Yong Liu +1
Differentiable Neural Architecture Search (DARTS) is becoming more and more popular among Neural Architecture Search (NAS) methods because of its high search efficiency and low com…
Towards Sharper Utility Bounds for Differentially Private Pairwise Learning
Yilin Kang, Yong Liu, Jian Li +1
Pairwise learning focuses on learning tasks with pairwise loss functions, depends on pairs of training instances, and naturally fits for modeling relationships between pairs of sam…
Semi-supervised Vector-valued Learning: Improved Bounds and Algorithms
Jian Li, Yong Liu, Weiping Wang
Vector-valued learning, where the output space admits a vector-valued structure, is an important problem that covers a broad family of important domains, e.g. multi-task learning a…