Search Results for author:
Found 5 papers, 2 papers with code
Guiding Catalogue Enrichment with User Queries
Techniques for knowledge graph (KGs) enrichment have been increasingly crucial for commercial applications that rely on evolving product catalogues.
AutoML in Heavily Constrained Applications
In this paper, we propose CAML, which uses meta-learning to automatically adapt its own AutoML parameters, such as the search strategy, the validation strategy, and the search space, for a task at hand.
Learning Action Embeddings for Off-Policy Evaluation
Off-policy evaluation (OPE) methods allow us to compute the expected reward of a policy by using the logged data collected by a different policy.
iPTR: Learning a representation for interactive program translation retrieval
iPTR uses a novel code representation technique that encodes structural characteristics of a program and a predictive transformation technique to transform the representation into the target programming language.
ED2: Two-stage Active Learning for Error Detection -- Technical Report
The challenges for such an approach are twofold: (1) to represent the data in a way that enables a classification model to identify various kinds of data errors, and (2) to pick the most promising data values for learning.
