6 citations
7 papers
Non-Trivial Zero-Knowledge Implies One-Way Functions
Suvradip Chakraborty, James Hulett, Dakshita Khurana +1
A recent breakthrough [Hirahara and Nanashima, STOC'2024] established that if , the existence of zero-knowledge with negligible errors…
Fine-grained Graph Rationalization
Zhe Xu, Menghai Pan, Yuzhong Chen +4
Rationale discovery is defined as finding a subset of the input data that maximally supports the prediction of downstream tasks. In the context of graph machine learning, graph rat…
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding
Yan Zheng, Junpeng Wang, Chin-Chia Michael Yeh +4
Embedding learning transforms discrete data entities into continuous numerical representations, encoding features/properties of the entities. Despite the outstanding performance re…
Beating Attackers At Their Own Games: Adversarial Example Detection Using Adversarial Gradient Directions
Yuhang Wu, Sunpreet S. Arora, Yanhong Wu +1
Adversarial examples are input examples that are specifically crafted to deceive machine learning classifiers. State-of-the-art adversarial example detection methods characterize a…
Merchant Category Identification Using Credit Card Transactions
Chin-Chia Michael Yeh, Zhongfang Zhuang, Yan Zheng +3
Digital payment volume has proliferated in recent years with the rapid growth of small businesses and online shops. When processing these digital transactions, recognizing each mer…
Towards a Flexible Embedding Learning Framework
Chin-Chia Michael Yeh, Dhruv Gelda, Zhongfang Zhuang +3
Representation learning is a fundamental building block for analyzing entities in a database. While the existing embedding learning methods are effective in various data mining pro…