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Found 6 papers, 0 papers with code
Certain and Approximately Certain Models for Statistical Learning
Real-world data is often incomplete and contains missing values.
Towards Consistent Language Models Using Declarative Constraints
Large language models have shown unprecedented abilities in generating linguistically coherent and syntactically correct natural language output.
Learning Over Dirty Data Without Cleaning
We propose DLearn, a novel relational learning system that learns directly over dirty databases effectively and efficiently without any preprocessing.
Usable & Scalable Learning Over Relational Data With Automatic Language Bias
Relational databases are valuable resources for learning novel and interesting relations and concepts.
A Signaling Game Approach to Databases Querying and Interaction
Using a real-world interaction workload, we show that users learn and modify how to express their information needs during their interactions with the DBMS and their learning is accurately modeled by a well-known reinforcement learning mechanism.
Schema Independent Relational Learning
In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations.
