5 citations · 5 across the 6 of their papers we have counts for
6 papers
On the Fragility of Data Attribution When Learning Is Distributed
Xian Gao, Bo Hui, Min-Te Sun +1
Data attribution has become an important component of pricing, auditing, and governance in machine learning pipelines, yet most attribution methods implicitly assume that attributi…
FedeKD: Energy-Based Gating for Robust Federated Knowledge Distillation under Heterogeneous Settings
Quang-Huy Nguyen, Jiaqi Wang, Wei-shinn Ku
Federated learning (FL) operates in heterogeneous environments, where variations in data distributions and asymmetric model design often result in negative transfer. While federate…
Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneity
Quang-Huy Nguyen, Jiaqi Wang, Wei-Shinn Ku
Federated learning (FL) faces challenges in uncertainty quantification (UQ). Without reliable UQ, FL systems risk deploying overconfident models at under-resourced agents, leading…
scBatchProx: Federated-Inspired Refinement for Stable Cell-Type Discriminability under Heterogeneous Batch Compositions
Quang-Huy Nguyen, Jiaqi Wang, Wei-Shinn Ku
Single-cell integration workflows often construct low-dimensional cell embeddings and then refine them with post-hoc methods to reduce batch effects. This refinement process can be…
Optimized Local Updates in Federated Learning via Reinforcement Learning
Ali Murad, Bo Hui, Wei-Shinn Ku
Federated Learning (FL) is a distributed framework for collaborative model training over large-scale distributed data, enabling higher performance while maintaining client data pri…
A Survey of Lottery Ticket Hypothesis
Bohan Liu, Zijie Zhang, Peixiong He +6
The Lottery Ticket Hypothesis (LTH) states that a dense neural network model contains a highly sparse subnetwork (i.e., winning tickets) that can achieve even better performance th…