Divya Padmanabhan
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Multi-Label Classification from Multiple Noisy Sources Using Topic Models
D Padmanabhan, S Bhat, S Shevade, Y Narahari
Information 8 (2), 52, 2017
Topic model based multi-label classification
D Padmanabhan, S Bhat, S Shevade, Y Narahari
2016 IEEE 28th International Conference on Tools with Artificial …, 2016
System and method to implement sharing of paper documents using virtual currency
S Roy, D Padmanabhan, J Williamowski, A Grasso, Y Hoppenot
US Patent 8,848,242, 2014
Mechanisms with learning for stochastic multi-armed bandit problems
S Jain, S Bhat, G Ghalme, D Padmanabhan, Y Narahari
Indian Journal of Pure and Applied Mathematics 47 (2), 229-272, 2016
A robust UCB scheme for active learning in regression from strategic crowds
D Padmanabhan, S Bhat, D Garg, S Shevade, Y Narahari
2016 International Joint Conference on Neural Networks (IJCNN), 2212-2219, 2016
A truthful mechanism with biparameter learning for online crowdsourcing
S Bhat, D Padmanabhan, S Jain, Y Narahari
arXiv preprint arXiv:1602.04032, 2016
Exploiting partial correlations in distributionally robust optimization
D Padmanabhan, K Natarajan, K Murthy
Mathematical Programming, 1-47, 2019
A dominant strategy truthful, deterministic multi-armed bandit mechanism with logarithmic regret
D Padmanabhan, S Bhat, S Shevade, Y Narahari
arXiv preprint arXiv:1703.00632, 2017
Topic Model Based Multi-Label Classification from the Crowd
D Padmanabhan, S Bhat, S Shevade, Y Narahari
arXiv preprint arXiv:1604.00783, 2016
Tree Bounds for Sums of Bernoulli Random Variables: A Linear Optimization Approach
D Padmanabhan, K Natarajan
arXiv preprint arXiv:1910.06321, 2019
Theoretical Models for Learning from Multiple, Heterogenous and Strategic Agents
D Padmanabhan
Proceedings of the 16th Conference on Autonomous Agents and MultiAgent …, 2017
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