Rainbow Connection Number and Radius. - Manu Basavaraju; L. Sunil Chandran; Deepak Rajendraprasad; Arunselvan Ramaswamy (Graphs Comb., 2014)

Rainbow Connection Number of Graph Power and Graph Products. - Manu Basavaraju; L. Sunil Chandran; Deepak Rajendraprasad; Arunselvan Ramaswamy (Graphs Comb., 2014)

Stochastic recursive inclusions with two timescales. - Arunselvan Ramaswamy; Shalabh Bhatnagar (CoRR, 2015)

Gradient-based learning algorithms with constant-error estimators: stability and convergence. - Arunselvan Ramaswamy; Shalabh Bhatnagar (CoRR, 2016)

Conditions for Stability and Convergence of Set-Valued Stochastic Approximations: Applications to Approximate Value and Fixed point Iterations with Noise. - Arunselvan Ramaswamy; Shalabh Bhatnagar (CoRR, 2017)

A Generalization of the Borkar-Meyn Theorem for Stochastic Recursive Inclusions. - Arunselvan Ramaswamy; Shalabh Bhatnagar (Math. Oper. Res., 2017)

DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling. - Burak Demirel; Arunselvan Ramaswamy; Daniel E. Quevedo; Holger Karl (IEEE Control. Syst. Lett., 2018)

Analysis of Gradient Descent Methods With Nondiminishing Bounded Errors. - Arunselvan Ramaswamy; Shalabh Bhatnagar (IEEE Trans. Autom. Control., 2018)

Multi-stage Reinforcement Learning for Object Detection. - Jonas König; Simon Malberg; Martin Martens; Sebastian Niehaus; Artus Krohn-Grimberghe; Arunselvan Ramaswamy (CVC (1), 2019)

Deep reinforcement learning for scheduling in large-scale networked control systems. - Adrian Redder; Arunselvan Ramaswamy; Daniel E. Quevedo (CoRR, 2019)

Stability of Stochastic Approximations With "Controlled Markov" Noise and Temporal Difference Learning. - Arunselvan Ramaswamy; Shalabh Bhatnagar (IEEE Trans. Autom. Control., 2019)

DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme. - Arunselvan Ramaswamy (PDP, 2020)

Deep reinforcement learning for wireless sensor scheduling in cyber-physical systems. - Alex S. Leong; Arunselvan Ramaswamy; Daniel E. Quevedo; Holger Karl; Ling Shi (Autom., 2020)

Theory of Deep Q-Learning: A Dynamical Systems Perspective. - Arunselvan Ramaswamy (CoRR, 2020)

A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks. - Haitham Afifi; Arunselvan Ramaswamy; Holger Karl (CCNC, 2021)

Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks. - Haitham Afifi; Arunselvan Ramaswamy; Holger Karl (ICC, 2021)

Practical sufficient conditions for convergence of distributed optimisation algorithms over communication networks with interference. - Adrian Redder; Arunselvan Ramaswamy; Holger Karl (CoRR, 2021)

Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! - Jan Peter Drees; Pritha Gupta; Eyke Hüllermeier; Tibor Jager; Alexander Konze; Claudia Priesterjahn; Arunselvan Ramaswamy; Juraj Somorovsky (IACR Cryptol. ePrint Arch., 2021)

Asynchronous Stochastic Approximations With Asymptotically Biased Errors and Deep Multiagent Learning. - Arunselvan Ramaswamy; Shalabh Bhatnagar; Daniel E. Quevedo (IEEE Trans. Autom. Control., 2021)

Age of Information Process under Strongly Mixing Communication - Moment Bound, Mixing Rate and Strong Law. - Adrian Redder; Arunselvan Ramaswamy; Holger Karl (Allerton, 2022)

Automated Information Leakage Detection: A New Method Combining Machine Learning and Hypothesis Testing with an Application to Side-channel Detection in Cryptographic Protocols. - Pritha Gupta; Arunselvan Ramaswamy; Jan Peter Drees; Eyke Hüllermeier; Claudia Priesterjahn; Tibor Jager (ICAART (2), 2022)

Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication. - Adrian Redder; Arunselvan Ramaswamy; Holger Karl (ICAART (1), 2022)

Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms. - Adrian Redder; Arunselvan Ramaswamy; Holger Karl (CoRR, 2022)

Distributed gradient-based optimization in the presence of dependent aperiodic communication. - Adrian Redder; Arunselvan Ramaswamy; Holger Karl (CoRR, 2022)

Analyzing Approximate Value Iteration Algorithms. - Arunselvan Ramaswamy; Shalabh Bhatnagar (Math. Oper. Res., 2022)

Optimization Over Time-Varying Networks and Unbounded Information Delays. - Arunselvan Ramaswamy; Adrian Redder; Daniel E. Quevedo (IEEE Trans. Autom. Control., 2022)

Deep Q-Learning: Theoretical Insights From an Asymptotic Analysis. - Arunselvan Ramaswamy; Eyke Hüllermeier (IEEE Trans. Artif. Intell., 2022)

Gradient Clipping in Deep Learning: A Dynamical Systems Perspective. - Arunselvan Ramaswamy (ICPRAM, 2023)

Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays. - Adrian Redder; Arunselvan Ramaswamy; Holger Karl (CoRR, 2023)

A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks. - Arunselvan Ramaswamy; Shalabh Bhatnagar; Naman Saxena (CoRR, 2023)

Reinforcement learning for autonomous vehicle movements in wireless multimedia applications. - Haitham Afifi; Arunselvan Ramaswamy; Holger Karl (Pervasive Mob. Comput., 2023)

Modeling Batch Tasks Using Recurrent Neural Networks in Co-Located Alibaba Workloads. - Hifza Khalid; Arunselvan Ramaswamy; Simone Ferlin; Alva L. Couch (ICPRAM, 2024)

Information Theoretic Deductions Using Machine Learning with an Application in Sociology. - Arunselvan Ramaswamy; Yunpeng Ma; Stefan Alfredsson; Fran Collyer; Anna Brunström (ICPRAM, 2024)

An Online Simulated Annealing-Based Task Offloading Strategy for a Mobile Edge Architecture. - Ayeh Mahjoubi; Arunselvan Ramaswamy; Karl-Johan Grinnemo (IEEE Access, 2024)

Approximate Probabilistic Inference for Time-Series Data: A Robust Latent Gaussian Model with Temporal Awareness. - Anton Johansson; Arunselvan Ramaswamy (ICAART (2), 2025)

Pre-Training Deep Q-Networks Eliminates the Need for Target Networks: An Empirical Study. - Alexander Lindström; Arunselvan Ramaswamy; Karl-Johan Grinnemo (ICPRAM, 2025)