5 papers
LakeMLB: Data Lake Machine Learning Benchmark
Feiyu Pan, Tianbin Zhang, Aoqian Zhang +5
Data lakes have become a fundamental platform for large-scale machine learning by enabling flexible management of heterogeneous data. Despite their growing importance, standardized…
StegaVAR: Privacy-Preserving Video Action Recognition via Steganographic Domain Analysis
Lixin Chen, Chaomeng Chen, Jiale Zhou +2
Despite the rapid progress of deep learning in video action recognition (VAR) in recent years, privacy leakage in videos remains a critical concern. Current state-of-the-art privac…
SMEC: Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression
Biao Zhang, Lixin Chen, Tong Liu +1
Large language models (LLMs) generate high-dimensional embeddings that capture rich semantic and syntactic information. However, high-dimensional embeddings exacerbate computationa…
How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?
Wenjun Ding, Ying An, Lixing Chen +3
Federated Adversarial Learning (FAL) is a robust framework for resisting adversarial attacks on federated learning. Although some FAL studies have developed efficient algorithms, t…
Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to -Level Stochastic Optimizations
Xiaokang Pan, Xingyu Li, Jin Liu +4
STOchastic Recursive Momentum (STORM)-based algorithms have been widely developed to solve one to -level () stochastic optimization problems. Specifically, they use es…