Topic Model Based Multi-Label Classification from the Crowd

no code implementations • 4 Apr 2016 • Divya Padmanabhan, Satyanath Bhat, Shirish Shevade, Y. Narahari

Multi-label classification is a common supervised machine learning problem where each instance is associated with multiple classes.

Classification General Classification +1

A Truthful Mechanism with Biparameter Learning for Online Crowdsourcing

no code implementations • 12 Feb 2016 • Satyanath Bhat, Divya Padmanabhan, Shweta Jain, Y. Narahari

The time to failure of a worker depends on the duration of the task handled by the worker.

A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds

no code implementations • 25 Jan 2016 • Divya Padmanabhan, Satyanath Bhat, Dinesh Garg, Shirish Shevade, Y. Narahari

We study the problem of training an accurate linear regression model by procuring labels from multiple noisy crowd annotators, under a budget constraint.

Active Learning regression +1

An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance

no code implementations • 27 Jun 2014 • Shweta Jain, Sujit Gujar, Satyanath Bhat, Onno Zoeter, Y. Narahari

First, we propose a framework, Assured Accuracy Bandit (AAB), which leads to an MAB algorithm, Constrained Confidence Bound for a Non Strategic setting (CCB-NS).