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ORCIDAdrian Redder
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2020 – today
- 2024
[b1]Adrian Redder:
Distributed asynchronous stochastic approximation algorithms with unbounded stochastic information delays - theory and applications (Verteilte asynchrone stochastische Approximationsalgorithmen mit unbeschränkten stochastischen Informationsdelays - Theorie und Anwendungen). Paderborn University, Germany, 2024- [j4]Haitham Afifi
, Sabrina Pochaba, Andreas Boltres, Dominic Laniewski, Janek Haberer, Leonard Paeleke, Reza Poorzare, Daniel Stolpmann, Nikolas Wehner, Adrian Redder, Eric Samikwa, Michael Seufert:
Machine Learning With Computer Networks: Techniques, Datasets, and Models. IEEE Access 12: 54673-54720 (2024) - 2023
- [i5]Adrian Redder, Arunselvan Ramaswamy, Holger Karl:
Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays. CoRR abs/2305.07091 (2023) - 2022
- [j3]Arunselvan Ramaswamy, Adrian Redder, Daniel E. Quevedo:
Optimization Over Time-Varying Networks and Unbounded Information Delays. IEEE Trans. Autom. Control. 67(8): 4131-4137 (2022) - [c4]Adrian Redder, Arunselvan Ramaswamy, Holger Karl:
Age of Information Process under Strongly Mixing Communication - Moment Bound, Mixing Rate and Strong Law. Allerton 2022: 1-8 - [c3]Adrian Redder, Arunselvan Ramaswamy, Holger Karl:
Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication. ICAART (1) 2022: 282-289 - [i4]Adrian Redder, Arunselvan Ramaswamy, Holger Karl:
Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms. CoRR abs/2201.00570 (2022) - [i3]Adrian Redder, Arunselvan Ramaswamy, Holger Karl:
Distributed gradient-based optimization in the presence of dependent aperiodic communication. CoRR abs/2201.11343 (2022) - 2021
- [i2]Adrian Redder, Arunselvan Ramaswamy, Holger Karl:
Practical sufficient conditions for convergence of distributed optimisation algorithms over communication networks with interference. CoRR abs/2105.04230 (2021)
2010 – 2019
- 2019
- [j2]Moritz Schulze Darup, Adrian Redder, Daniel E. Quevedo:
Encrypted Cooperative Control Based on Structured Feedback. IEEE Control. Syst. Lett. 3(1): 37-42 (2019) - [i1]Adrian Redder, Arunselvan Ramaswamy, Daniel E. Quevedo:
Deep reinforcement learning for scheduling in large-scale networked control systems. CoRR abs/1905.05992 (2019) - 2018
- [j1]Moritz Schulze Darup, Adrian Redder, Iman Shames, Farhad Farokhi, Daniel E. Quevedo:
Towards Encrypted MPC for Linear Constrained Systems. IEEE Control. Syst. Lett. 2(2): 195-200 (2018) - [c2]Moritz Schulze Darup, Adrian Redder, Daniel E. Quevedo:
A fixed-point implementation of explicit MPC laws. ACC 2018: 749-755 - [c1]Alex S. Leong, Adrian Redder, Daniel E. Quevedo, Subhrakanti Dey:
On the Use of Artificial Noise for Secure State Estimation in the Presence of Eavesdroppers. ECC 2018: 325-330
Coauthor Index
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