Bayesian Active Learning for Posterior Estimation Kirthevasan Kandasamy, Jeff Schneider, Barnab´as P´oczos Carnegie Mellon University, Pittsburgh, PA, USA {kandasamy, schneide, bapoczos}@cs.cmu.edu Abstract

Source URL: www.cs.cmu.edu

Inside the mind of a learner - Sampling experiences to enhance learning process INTRODUCTION

Source URL: www.claned.com

• Educational psychology
• Educational technology
• Self-regulated learning
• Learning
• Active learning
• Situated learning

Low-Cost Learning via Active Data Procurement JACOB ABERNETHY, University of Michigan YILING CHEN, Harvard University CHIEN-JU HO, UCLA BO WAGGONER, Harvard University

Source URL: yiling.seas.harvard.edu

Adv Physiol Educ 30: 159 –167, 2006; doi:advanHow We Learn Where’s the evidence that active learning works?

Source URL: www2.ucsc.edu

The True Sample Complexity of Active Learning Maria-Florina Balcan Computer Science Department Carnegie Mellon University

Source URL: www.jennwv.com

Active Learning of Model Evidence Using Bayesian Quadrature Michael A. Osborne University of Oxford

Source URL: www.probabilistic-numerics.org

JMLR: Workshop and Conference Proceedings vol 40:1–20, 2015 S2 : An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification Gautam Dasarathy

Source URL: jmlr.org

• Graph connectivity
• Graph theory
• Edsger W. Dijkstra
• Network theory
• Shortest path problem
• Bridge
• Connected component
• Connectivity
• Graph
• Routing algorithms
• Planar separator theorem
• Minimum spanning tree

EigenSense: Saving User Effort with Active Metric Learning Eli T Brown Remco Chang

Source URL: poloclub.gatech.edu

• Machine learning
• Multivariate statistics
• K-nearest neighbors algorithm
• Active learning
• Eigenvalues and eigenvectors
• Cluster analysis
• Large margin nearest neighbor
• Recommender system
• K-means clustering
• Support vector machine
• Markov chain
• Multidimensional scaling

multi-bandit_techreport.dvi

Source URL: victorgabillon.nfshost.com

• Machine learning
• Multi-armed bandit
• Stochastic optimization
• Markov models
• Active learning
• Dynamic programming
• Reinforcement learning
• Variance

Combining Active Learning and Partial Annotation for Domain Adaptation of a Japanese Dependency Parser Daniel Flannery∗ Vitei Inc. Kankoboko-cho 79 Shimogyo-ku, Kyoto, Japan

Source URL: www.ar.media.kyoto-u.ac.jp