Generalization remains a central challenge in machine learning. In this work, we propose
Learning from Teaching (LoT), a novel regularization technique for deep neural networks to …

Abstract The growth of Graph Convolution Network (GCN) model sizes has revolutionized
numerous applications, surpassing human performance in areas such as personal …

Secure multi-party computation (MPC) enables computation directly on encrypted data and
protects both data and model privacy in deep learning inference. However, existing neural …

Deep neural network (DNN) inference based on secure 2-party computation (2PC) can offer
cryptographically-secure privacy protection but suffers from orders of magnitude latency …

In the acceleration of deep neural network training, the graphics processing unit (GPU) has
become the mainstream platform. GPUs face substantial challenges on Graph Neural …

Secure two-party neural network (2P-NN) inference allows the server with a neural network
model and the client with inputs to perform neural network inference without revealing their …

Many inference services based on large language models (LLMs) pose a privacy concern,
either revealing user prompts to the service or the proprietary weights to the user. Secure …

This paper explores the integration of strategic optimization methods in search advertising,
focusing on ad ranking and bidding mechanisms within E-commerce platforms. By …

Security has always been a critical issue in machine learning (ML) applications. Due to the
high cost of model training–such as collecting relevant samples, labeling data, and …

Homomorphic Encryption (HE) is a promising technology to protect clients' data privacy for
Machine Learning as a Service (MLaaS) on public clouds. However, HE operations can be …