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Showing 2019 · cs.LGShow all
3 papers · 2 filters
cs.LG2019★ 14 cited
Deep Active Learning: Unified and Principled Method for Query and Training
Changjian Shui, Fan Zhou, Christian Gagné +1
In this paper, we are proposing a unified and principled method for both the querying and training processes in deep batch active learning. We are providing theoretical insights fr…
cs.LG2019★ 20 cited
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback
Mingrui Zhang, Lin Chen, Hamed Hassani +1
In this paper, we propose three online algorithms for submodular maximisation. The first one, Mono-Frank-Wolfe, reduces the number of per-function gradient evaluations from $T^{1/2…
cs.LG2019★ 5 cited
Learning Neurosymbolic Generative Models via Program Synthesis
Halley Young, Osbert Bastani, Mayur Naik
Significant strides have been made toward designing better generative models in recent years. Despite this progress, however, state-of-the-art approaches are still largely unable t…