| An online actor–critic algorithm with function approximation for constrained markov decision processes S Bhatnagar, K Lakshmanan Journal of Optimization Theory and Applications 153, 688-708, 2012 | 293 | 2012 |
| Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer A Kumar, SK Singh, S Saxena, K Lakshmanan, AK Sangaiah, H Chauhan, ... Information Sciences 508, 405-421, 2020 | 233 | 2020 |
| CoMHisP: A novel feature extractor for histopathological image classification based on fuzzy SVM with within-class relative density A Kumar, SK Singh, S Saxena, AK Singh, S Shrivastava, K Lakshmanan, ... IEEE Transactions on Fuzzy Systems 29 (1), 103-117, 2020 | 47 | 2020 |
| Improved regret bounds for undiscounted continuous reinforcement learning K Lakshmanan, R Ortner, D Ryabko International conference on machine learning, 524-532, 2015 | 44 | 2015 |
| PILHNB: Popularity, interests, location used hidden Naive Bayesian-based model for link prediction in dynamic social networks AK Singh, K Lakshmanan Neurocomputing 461, 562-576, 2021 | 22 | 2021 |
| Link prediction-based influence maximization in online social networks AK Singh, L Kailasam Neurocomputing 453, 151-163, 2021 | 20 | 2021 |
| A game-theoretic approach for increasing resource utilization in edge computing enabled internet of things S Kumar, R Gupta, K Lakshmanan, V Maurya IEEE Access 10, 57974-57989, 2022 | 15 | 2022 |
| A novel cloud-assisted secure deep feature classification framework for cancer histopathology images A Kumar, SK Singh, K Lakshmanan, S Saxena, S Shrivastava ACM Transactions on Internet Technology (TOIT) 21 (2), 1-22, 2021 | 15 | 2021 |
| Beam alignment for mmWave using non-stationary bandits R Gupta, K Lakshmanan, AK Sah IEEE Communications Letters 24 (11), 2619-2622, 2020 | 15 | 2020 |
| An unsupervised software fault prediction approach using threshold derivation R Kumar, A Chaturvedi, L Kailasam IEEE Transactions on Reliability 71 (2), 911-932, 2022 | 13 | 2022 |
| A novel Q-learning algorithm with function approximation for constrained Markov decision processes K Lakshmanan, S Bhatnagar 2012 50th Annual Allerton Conference on Communication, Control, and …, 2012 | 13 | 2012 |
| Multiscale Q-learning with linear function approximation S Bhatnagar, K Lakshmanan Discrete Event Dynamic Systems 26, 477-509, 2016 | 9 | 2016 |
| Comment on “federated learning with differential privacy: Algorithms and performance analysis” K Rajkumar, A Goswami, K Lakshmanan, R Gupta IEEE Transactions on Information Forensics and Security 17, 3922-3924, 2022 | 7 | 2022 |
| Transition based discount factor for model free algorithms in reinforcement learning A Sharma, R Gupta, K Lakshmanan, A Gupta Symmetry 13 (7), 1197, 2021 | 6 | 2021 |
| A parameter-free affinity based clustering B Mukhoty, R Gupta, L K, M Kumar Applied Intelligence 50 (12), 4543-4556, 2020 | 6 | 2020 |
| A deep actor critic reinforcement learning framework for learning to rank V Padhye, K Lakshmanan Neurocomputing 547, 126314, 2023 | 5 | 2023 |
| Proximal policy optimization based hybrid recommender systems for large scale recommendations V Padhye, K Lakshmanan, A Chaturvedi Multimedia Tools and Applications 82 (13), 20079-20100, 2023 | 4 | 2023 |
| Quasi-Newton smoothed functional algorithms for unconstrained and constrained simulation optimization K Lakshmanan, S Bhatnagar Computational Optimization and Applications 66, 533-556, 2017 | 4 | 2017 |
| Smoothed functional and quasi-Newton algorithms for routing in multi-stage queueing network with constraints K Lakshmanan, S Bhatnagar International Conference on Distributed Computing and Internet Technology …, 2011 | 4 | 2011 |
| Accelerated Reinforcement Learning K Lakshmanan 2017 14th IEEE India Council International Conference (INDICON), 1-4, 2017 | 3 | 2017 |
