| 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 | 83 | 2016 |
| 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 | 81 | 2013 |
| Predicting arrival times of vehicles based upon observed schedule adherence A Tripathi, V Rajan, NU Edakunni US Patent 9,159,032, 2015 | 59 | 2015 |
| 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 | 18 | 2017 |
| 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 | 16 | 2018 |
| Kernel carpentry for online regression using randomly varying coefficient model NU Edakunni, S Schaal, S Vijayakumar | 15 | 2006 |
| Fairxgboost: Fairness-aware classification in xgboost S Ravichandran, D Khurana, B Venkatesh, NU Edakunni arXiv preprint arXiv:2009.01442, 2020 | 13 | 2020 |
| Boosting as a Product of Experts NU Edakunni, G Brown, T Kovacs Uncertainty in Artificial Intelligence, 187-194, 2011 | 12 | 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 | 12 | 2009 |
| Method and system for scheduling vehicles along routes in a transportation system K Mukherjee, A Kumar, P Manohar, NU Edakunni, S Jat US Patent 9,746,332, 2017 | 11 | 2017 |
| 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 | 8 | 2017 |
| Efficient online classification using an ensemble of bayesian linear logistic regressors NU Edakunni, S Vijayakumar International Workshop on Multiple Classifier Systems, 102-111, 2009 | 7 | 2009 |
| 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 | 6 | 2021 |
| 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 | 4 | 2018 |
| 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 | 4 | 2011 |
| Accurate and Intuitive Contextual Explanations using Linear Model Trees A Lahiri, NU Edakunni arXiv preprint arXiv:2009.05322, 2020 | 3 | 2020 |
| Use of gps signals from multiple vehicles for robust vehicle tracking A Sengupta, NU Edakunni US Patent App. 15/443,295, 2018 | 3 | 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 | 2 | 2015 |
| Bayesian locally weighted online learning NU Edakunni The University of Edinburgh, 2010 | 2 | 2010 |
| 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 | 1 | 2011 |
Gavin BrownProfessor of Machine Learning, Dept of Computer Science, University of ManchesterVerified email at manchester.ac.uk
