| On the global solution of linear programs with linear complementarity constraints J Hu, JE Mitchell, JS Pang, KP Bennett, G Kunapuli SIAM Journal on Optimization 19 (1), 445-471, 2008 | 148 | 2008 |
| Multi-agent inverse reinforcement learning S Natarajan, G Kunapuli, K Judah, P Tadepalli, K Kersting, J Shavlik 2010 ninth international conference on machine learning and applications …, 2010 | 137 | 2010 |
| Classification model selection via bilevel programming G Kunapuli, KP Bennett, J Hu, JS Pang Optimization Methods & Software 23 (4), 475-489, 2008 | 124 | 2008 |
| Model selection via bilevel optimization KP Bennett, J Hu, X Ji, G Kunapuli, JS Pang The 2006 IEEE International Joint Conference on Neural Network Proceedings …, 2006 | 111 | 2006 |
| Bilevel optimization and machine learning KP Bennett, G Kunapuli, J Hu, JS Pang IEEE world congress on computational intelligence, 25-47, 2008 | 105 | 2008 |
| A decision-support tool for renal mass classification G Kunapuli, BA Varghese, P Ganapathy, B Desai, S Cen, M Aron, I Gill, ... Journal of Digital Imaging 31 (6), 929-939, 2018 | 68 | 2018 |
| Classification of burn injury using Raman spectroscopy and optical coherence tomography: An ex-vivo study on porcine skin LP Rangaraju, G Kunapuli, D Every, OD Ayala, P Ganapathy, ... Burns 45 (3), 659-670, 2019 | 65 | 2019 |
| Bilevel Model Selection for Support Vector Machines Gautam Kunapuli, Kristin P. Bennett, Jing Hu, and Jong-Shi Pang G Kunapuli Data mining and mathematical programming 45, 129, 2008 | 62 | 2008 |
| Guiding autonomous agents to better behaviors through human advice G Kunapuli, P Odom, JW Shavlik, S Natarajan 2013 IEEE 13th international conference on data mining, 409-418, 2013 | 55 | 2013 |
| Mirror descent for metric learning: A unified approach G Kunapuli, J Shavlik Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012 | 53 | 2012 |
| Online knowledge-based support vector machines G Kunapuli, KP Bennett, A Shabbeer, R Maclin, J Shavlik Joint European Conference on Machine Learning and Knowledge Discovery in …, 2010 | 44 | 2010 |
| Learning from imbalanced data in relational domains: A soft margin approach S Yang, T Khot, K Kersting, G Kunapuli, K Hauser, S Natarajan 2014 IEEE International Conference on Data Mining, 1085-1090, 2014 | 41 | 2014 |
| Drug‐drug interaction discovery: kernel learning from heterogeneous similarities DS Dhami, G Kunapuli, M Das, D Page, S Natarajan Smart Health 9, 88-100, 2018 | 33 | 2018 |
| Structure learning for relational logistic regression: An ensemble approach N Ramanan, G Kunapuli, T Khot, B Fatemi, SM Kazemi, D Poole, ... Data Mining and Knowledge Discovery 35 (5), 2089-2111, 2021 | 21 | 2021 |
| Relational restricted boltzmann machines: A probabilistic logic learning approach N Kaur, G Kunapuli, T Khot, K Kersting, W Cohen, S Natarajan International Conference on Inductive Logic Programming, 94-111, 2017 | 21 | 2017 |
| Fast relational probabilistic inference and learning: Approximate counting via hypergraphs M Das, DS Dhami, G Kunapuli, K Kersting, S Natarajan Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 7816-7824, 2019 | 20 | 2019 |
| A bilevel optimization approach to machine learning G Kunapuli Rensselaer Polytechnic Institute, 2008 | 20 | 2008 |
| Neural networks for relational data N Kaur, G Kunapuli, S Joshi, K Kersting, S Natarajan International Conference on Inductive Logic Programming, 62-71, 2019 | 18 | 2019 |
| On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach. S Natarajan, S Das, N Ramanan, G Kunapuli, P Radivojac IJCAI, 3498-3505, 2018 | 17 | 2018 |
| Docinfer: Document-level natural language inference using optimal evidence selection P Mathur, G Kunapuli, R Bhat, M Shrivastava, D Manocha, M Singh Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 16 | 2022 |
Sriraam NatarajanUniversity of Texas at DallasVerified email at utdallas.edu
