12 citations · 15 across the 11 of their papers we have counts for
4 papers · 1 filter
Adaptive Token-Weighted Differential Privacy for LLMs: Not All Tokens Require Equal Protection
Manjiang Yu, Priyanka Singh, Xue Li +1
Large language models (LLMs) frequently memorize sensitive or personal information, raising significant privacy concerns. Existing variants of differential privacy stochastic gradi…
Random Client Selection on Contrastive Federated Learning for Tabular Data
Achmad Ginanjar, Xue Li, Priyanka Singh +1
Vertical Federated Learning (VFL) has revolutionised collaborative machine learning by enabling privacy-preserving model training across multiple parties. However, it remains vulne…
Continual Contrastive Learning on Tabular Data with Out of Distribution
Achmad Ginanjar, Xue Li, Priyanka Singh +1
Out-of-distribution (OOD) prediction remains a significant challenge in machine learning, particularly for tabular data where traditional methods often fail to generalize beyond th…
Representation Learning on Out of Distribution in Tabular Data
Achmad Ginanjar, Xue Li, Priyanka Singh +1
The open-world assumption in model development suggests that a model might lack sufficient information to adequately handle data that is entirely distinct or out of distribution (O…