| Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems AS Leong, A Ramaswamy, DE Quevedo, H Karl, L Shi Automatica 113, 108759, 2020 | 157 | 2020 |
| DeepCAS: A deep reinforcement learning algorithm for control-aware scheduling B Demirel, A Ramaswamy, DE Quevedo, H Karl IEEE Control Systems Letters 2 (4), 737-742, 2018 | 68 | 2018 |
| Rainbow connection number and radius M Basavaraju, LS Chandran, D Rajendraprasad, A Ramaswamy Graphs and Combinatorics 30, 275-285, 2014 | 67 | 2014 |
| A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions A Ramaswamy, S Bhatnagar Mathematics of Operations Research 42 (3), 648-661, 2017 | 38 | 2017 |
| Rainbow connection number of graph power and graph products M Basavaraju, LS Chandran, D Rajendraprasad, A Ramaswamy Graphs and Combinatorics 30, 1363-1382, 2014 | 36 | 2014 |
| Deep Q-learning: Theoretical insights from an asymptotic analysis A Ramaswamy, E Hüllermeier IEEE Transactions on Artificial Intelligence 3 (2), 139-151, 2021 | 28* | 2021 |
| Stability of stochastic approximations with “controlled markov” noise and temporal difference learning A Ramaswamy, S Bhatnagar IEEE Transactions on Automatic Control 64 (6), 2614-2620, 2018 | 28 | 2018 |
| Analysis of gradient descent methods with nondiminishing bounded errors A Ramaswamy, S Bhatnagar IEEE Transactions on Automatic Control 63 (5), 1465-1471, 2017 | 24 | 2017 |
| Multi-stage reinforcement learning for object detection J König, S Malberg, M Martens, S Niehaus, A Krohn-Grimberghe, ... Advances in Computer Vision: Proceedings of the 2019 Computer Vision …, 2020 | 23 | 2020 |
| Deep reinforcement learning for scheduling in large-scale networked control systems A Redder, A Ramaswamy, DE Quevedo IFAC-PapersOnLine 52 (20), 333-338, 2019 | 22 | 2019 |
| Stochastic recursive inclusion in two timescales with an application to the Lagrangian dual problem A Ramaswamy, S Bhatnagar Stochastics 88 (8), 1173-1187, 2016 | 13 | 2016 |
| Asymptotic convergence of deep multi-agent actor-critic algorithms A Redder, A Ramaswamy, H Karl arXiv: 2201.00570, 2022 | 12 | 2022 |
| Asynchronous stochastic approximations with asymptotically biased errors and deep multiagent learning A Ramaswamy, S Bhatnagar, DE Quevedo IEEE Transactions on Automatic Control 66 (9), 3969-3983, 2020 | 11 | 2020 |
| Optimization over time-varying networks and unbounded information delays A Ramaswamy, A Redder, DE Quevedo IEEE Transactions on Automatic Control 67 (8), 4131-4137, 2021 | 8 | 2021 |
| Automated detection of side channels in cryptographic protocols: DROWN the ROBOTs! JP Drees, P Gupta, E Hüllermeier, T Jager, A Konze, C Priesterjahn, ... Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security …, 2021 | 7 | 2021 |
| Reinforcement learning for autonomous vehicle movements in wireless sensor networks H Afifi, A Ramaswamy, H Karl ICC 2021-IEEE International Conference on Communications, 1-6, 2021 | 7 | 2021 |
| Age of information process under strongly mixing communication-moment bound, mixing rate and strong law A Redder, A Ramaswamy, H Karl 2022 58th Annual Allerton Conference on Communication, Control, and …, 2022 | 6 | 2022 |
| Analyzing approximate value iteration algorithms A Ramaswamy, S Bhatnagar Mathematics of Operations Research 47 (3), 2138-2159, 2022 | 6 | 2022 |
| 3DPG: Distributed deep deterministic policy gradient algorithms for networked multi-agent systems A Redder, A Ramaswamy, H Karl arXiv preprint arXiv:2201.00570, 2022 | 5 | 2022 |
| Practical network conditions for the convergence of distributed optimization A Redder, A Ramaswamy, H Karl IFAC-PapersOnLine 55 (13), 133-138, 2022 | 5 | 2022 |
Holger KarlHasso-Plattner-Institute, Potsdam UniversityVerified email at hpi.de
