3 papers
cs.LG2026
The Maximum von Neumann Entropy Principle: Theory and Applications in Machine Learning
Youqi Wu, Farzan Farnia
Von Neumann entropy (VNE) is a fundamental quantity in quantum information theory and has recently been adopted in machine learning as a spectral measure of diversity for kernel ma…
cs.LG2025
When Kernels Multiply, Clusters Unify: Fusing Embeddings with the Kronecker Product
Youqi Wu, Jingwei Zhang, Farzan Farnia
State-of-the-art embeddings often capture distinct yet complementary discriminative features: For instance, one image embedding model may excel at distinguishing fine-grained textu…
cs.LG2025
Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning
Chih Wei Ling, Chun Hei Michael Shiu, Youqi Wu +4
Training machine learning models on decentralized private data via federated learning (FL) poses two key challenges: communication efficiency and privacy protection. In this work,…