Search Results for author:
Found 4 papers, 2 papers with code
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.
