pith. machine review for the scientific record. sign in

arxiv: 1804.09238 · v2 · submitted 2018-04-24 · 💻 cs.LG · cs.AI· stat.ML

Recognition: unknown

Semi-Supervised Learning with Declaratively Specified Entropy Constraints

Authors on Pith no claims yet
classification 💻 cs.LG cs.AIstat.ML
keywords heuristicsconstraintslearningsemi-superviseddeclarativelyusedadditionagreement
0
0 comments X
read the original abstract

We propose a technique for declaratively specifying strategies for semi-supervised learning (SSL). The proposed method can be used to specify ensembles of semi-supervised learning, as well as agreement constraints and entropic regularization constraints between these learners, and can be used to model both well-known heuristics such as co-training and novel domain-specific heuristics. In addition to representing individual SSL heuristics, we show that multiple heuristics can also be automatically combined using Bayesian optimization methods. We show consistent improvements on a suite of well-studied SSL benchmarks, including a new state-of-the-art result on a difficult relation extraction task.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.