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Found 8 papers, 3 papers with code
Randomized algorithms for precise measurement of differentially-private, personalized recommendations
Personalized recommendations form an important part of today's internet ecosystem, helping artists and creators to reach interested users, and helping users to discover new and engaging content.
An Efficient and Robust System for Vertically Federated Random Forest
As there is a growing interest in utilizing data across multiple resources to build better machine learning models, many vertically federated learning algorithms have been proposed to preserve the data privacy of the participating organizations.
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space.
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms.
Recognizing Descriptive Wikipedia Categories for Historical Figures
Wikipedia is a useful knowledge source that benefits many applications in language processing and knowledge representation.
False-Friend Detection and Entity Matching via Unsupervised Transliteration
Transliterations play an important role in multilingual entity reference resolution, because proper names increasingly travel between languages in news and social media.
The Expressive Power of Word Embeddings
We seek to better understand the difference in quality of the several publicly released embeddings.
