Narayanan U Edakunni
Narayanan U Edakunni
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Beyond Fano's inequality: Bounds on the optimal F-score, BER, and cost-sensitive risk and their implications
MJ Zhao, N Edakunni, A Pocock, G Brown
The Journal of Machine Learning Research 14 (1), 1033-1090, 2013
Cost-sensitive boosting algorithms: Do we really need them?
N Nikolaou, N Edakunni, M Kull, P Flach, G Brown
Machine Learning 104, 359-384, 2016
Predicting arrival times of vehicles based upon observed schedule adherence
A Tripathi, V Rajan, NU Edakunni
US Patent 9,159,032, 2015
Kernel carpentry for online regression using randomly varying coefficient model
NU Edakunni, S Schaal, S Vijayakumar
Method and system for recommending one or more vehicles for one or more requestors
S Jat, K Mukherjee, NU Edakunni, P Manohar
US Patent 9,978,111, 2018
Methods and systems for analyzing customer care data
G Manjunath, A Sharma, NU Edakunni, D Gupta, M Gupta, S Kunde, ...
US Patent App. 15/064,642, 2017
Boosting as a Product of Experts
NU Edakunni, G Brown, T Kovacs
Uncertainty in Artificial Intelligence, 187-194, 2011
Modeling UCS as a mixture of experts
NU Edakunni, T Kovacs, G Brown, JAR Marshall
Proceedings of the 11th Annual conference on Genetic and Evolutionary …, 2009
Fairxgboost: Fairness-aware classification in xgboost
S Ravichandran, D Khurana, B Venkatesh, NU Edakunni
arXiv preprint arXiv:2009.01442, 2020
Efficient online classification using an ensemble of bayesian linear logistic regressors
NU Edakunni, S Vijayakumar
International Workshop on Multiple Classifier Systems, 102-111, 2009
Method and system to predict a communication channel for communication with a customer service
NU Edakunni, S Galhotra
US Patent App. 15/077,085, 2017
Systems and methods for real-time scheduling in a transportation system based upon a user criteria
NU Edakunni, K Baruah
US Patent 11,127,100, 2021
Online, GA based mixture of experts: a probabilistic model of UCS
NU Edakunni, G Brown, T Kovacs
Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011
Use of gps signals from multiple vehicles for robust vehicle tracking
A Sengupta, NU Edakunni
US Patent App. 15/443,295, 2018
Method and system for real-time prediction of crowdedness in vehicles in transit
A Sengupta, K Baruah, S Sankhya, NU Edakunni
US Patent App. 15/271,249, 2018
Probabilistic Dependency Networks for Prediction and Diagnostics
NU Edakunni, A Raghunathan, A Tripathi, J Handley, F Roulland
Transportation Research Board 94th Annual Meeting, 2015
Bayesian locally weighted online learning
NU Edakunni
The University of Edinburgh, 2010
Accurate and Intuitive Contextual Explanations using Linear Model Trees
A Lahiri, NU Edakunni
arXiv preprint arXiv:2009.05322, 2020
Accuracy exponentiation in UCS and its effect on voting margins
T Kovacs, N Edakunni, G Brown
Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011
Simple is better: Making Decision Trees faster using random sampling
VN Kumar, NU Edakunni
arXiv preprint arXiv:2108.08790, 2021
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