262 citations · 1.8k across the 75 of their papers we have counts for
6 papers · 2 filters
Submodularity in Batch Active Learning and Survey Problems on Gaussian Random Fields
Yifei Ma, Roman Garnett, Jeff Schneider
Many real-world datasets can be represented in the form of a graph whose edge weights designate similarities between instances. A discrete Gaussian random field (GRF) model is a fi…
Maximum Margin Output Coding
Yi Zhang, Jeff Schneider
In this paper we study output coding for multi-label prediction. For a multi-label output coding to be discriminative, it is important that codewords for different label vectors ar…
Bayesian Optimal Active Search and Surveying
Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong +2
We consider two active binary-classification problems with atypical objectives. In the first, active search, our goal is to actively uncover as many members of a given class as pos…
Copula-based Kernel Dependency Measures
Barnabas Poczos, Zoubin Ghahramani, Jeff Schneider
The paper presents a new copula based method for measuring dependence between random variables. Our approach extends the Maximum Mean Discrepancy to the copula of the joint distrib…
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions
Barnabas Poczos, Liang Xiong, Jeff Schneider
Low-dimensional embedding, manifold learning, clustering, classification, and anomaly detection are among the most important problems in machine learning. The existing methods usua…
Kernels on Sample Sets via Nonparametric Divergence Estimates
Danica J. Sutherland, Liang Xiong, Barnabás Póczos +1
Most machine learning algorithms, such as classification or regression, treat the individual data point as the object of interest. Here we consider extending machine learning algor…