We propose a general method to watermark and probabilistically identify the structured outputs of machine learning algorithms. Our method is robust to local editing operations and...
Ashish Venugopal, Jakob Uszkoreit, David Talbot, F...
We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic pro...
Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, T...
Modeling a user’s click-through behavior in click logs is a challenging task due to the well-known position bias problem. Recent advances in click models have adopted the examin...
Botao Hu, Yuchen Zhang, Weizhu Chen, Gang Wang, Qi...
A large and growing number of web pages display contextual advertising based on keywords automatically extracted from the text of the page, and this is a substantial source of rev...
Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and simila...
Xiaohong Wang, Aaron M. Smalter, Jun Huan, Gerald ...