Learning small predictors

Vikas K. Garg, Ofer Dekel, Lin Xiao

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


We introduce a new framework for learning in severely resource-constrained settings. Our technique delicately amalgamates the representational richness of multiple linear predictors with the sparsity of Boolean relaxations, and thereby yields classifiers that are compact, interpretable, and accurate. We provide a rigorous formalism of the learning problem, and establish fast convergence of the ensuing algorithm via relaxation to a minimax saddle point objective. We supplement the theoretical foundations of our work with an extensive empirical evaluation.

Original languageEnglish
Pages (from-to)9125-9135
Number of pages11
JournalAdvances in Neural Information Processing Systems
StatePublished - 2018
Externally publishedYes
Event32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
Duration: Dec 2 2018Dec 8 2018


Dive into the research topics of 'Learning small predictors'. Together they form a unique fingerprint.

Cite this