Analyzing Approximate Value Iteration Algorithms
A. Ramaswamy, S. Bhatnagar, Mathematics of Operations Research (to Appear) (2021).

Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs!
J.P. Drees, P. Gupta, E. Hüllermeier, T. Jager, A. Konze, C. Priesterjahn, A. Ramaswamy, J. Somorovsky, 14th ACM Workshop on Artificial Intelligence and Security (2021).

Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis
A. Ramaswamy, E. Hüllermeier, IEEE Transactions on Artificial Intelligence (to Appear) (2021).

Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks
H. Afifi, A. Ramaswamy, H. Karl, in: 2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC’21 - IoTSN Symposium), Montreal, Canada, 2021.

A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks
H. Afifi, A. Ramaswamy, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications \'26; Networking Conference (CCNC) (CCNC 2021), 2021.

Distributed optimization over time-varying networks with stochastic information delays
A. Ramaswamy, A. Redder, D.E. Quevedo, IEEE Transactions on Automatic Control (2021) 1–1.

DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme
A. Ramaswamy, in: 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2020, pp. 54–62.

Constrained Multi-Agent Optimization with Unbounded Information Delay
S.H. Heid, A. Ramaswamy, E. Hüllermeier, in: Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27. November 2020, 2020, p. 247.

Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning
A. Ramaswamy, S. Bhatnagar, D.E. Quevedo, IEEE Transactions on Automatic Control (2020).

Multi-stage reinforcement learning for object detection
J. König, S. Malberg, M. Martens, S. Niehaus, A. Krohn-Grimberghe, A. Ramaswamy, in: Science and Information Conference, 2019, pp. 178–191.

Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems
A.S. Leong, A. Ramaswamy, D.E. Quevedo, H. Karl, L. Shi, Automatica (2019).

Deep reinforcement learning for scheduling in large-scale networked control systems
A. Redder, A. Ramaswamy, D. Quevedo, in: Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems, 2019.

Stability of stochastic approximations with ccontrolled markovd noise and temporal difference learning
A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018) 2614–2620.

Deepcas: A deep reinforcement learning algorithm for control-aware scheduling
B. Demirel, A. Ramaswamy, D.E. Quevedo, H. Karl, IEEE Control Systems Letters 2 (2018) 737–742.

Analysis of gradient descent methods with nondiminishing bounded errors
A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 63 (2017) 1465–1471.

A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions
A. Ramaswamy, S. Bhatnagar, Mathematics of Operations Research 42 (2017) 648–661.

Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem
A. Ramaswamy, S. Bhatnagar, Stochastics 88 (2016) 1173–1187.

Rainbow connection number of graph power and graph products
M. Basavaraju, L.S. Chandran, D. Rajendraprasad, A. Ramaswamy, Graphs and Combinatorics 30 (2014) 1363–1382.

Rainbow connection number and radius
M. Basavaraju, L.S. Chandran, D. Rajendraprasad, A. Ramaswamy, Graphs and Combinatorics 30 (2014) 275–285.