Welcome to the lab, Jonathan and Alex!
Starting June 1 Jonathan and Alex will join MLPS lab. Jonathan works on representation learning for wireless channels and Alex works on Constrained Reinforcement Learning.
We start our lab!
Chris has been appointed full professor of Machine Learning and Positioning Systems at the University of Technology Nuremberg (UTN).
Michael Girstl: Safe Reinforcement learning using Successor representations
Reinforcement Learning (RL) has achieved remarkable success in various domains, from game playing to robotics. However, in safety-critical applications such as autonomous driving or medical treatment, traditional RL algorithms may…
Haris Asif: Quantum Circuit Optimization via Hierarchical Reinforcement Learning (FAU Erlangen-Nürnberg, 2024)
Motivation / Related Work Optimizing quantum circuits is essential for various applications, such as chemical simulations, due to the inherent complexity of quantum systems and the current limitations of quantum…
Thorsten Bescher: Schedule-Net and Gumbel-Alpha-Zero Play-to-Plan: a Hybrid Approach for the Job-Shop Scheduling Problem (FAU Erlangen-Nürnberg, 2024)
Motivation / Related Work Recently, reinforcement learning (RL) is applied frequently for combinatorial optimization problems [1] with real world applications, e.g., the job-shop scheduling problem (JSSP) or the travelling salesman…
Stephan Geisler: Designing and training foundation models for positioning applications (FAU Erlangen-Nürnberg, 2024)
Foundation models (like OpenAI’s GPT-4/ChatGPT [1] or Meta’s Llama 2 [2]) have been a disruptive technology that within just few months has started to transform the modus operandi of several industries. There have…
Nicolas Kolbenschlag: Automated Generation of Block-Encodings for Quantum Policy Iteration (FAU Erlangen-Nürnberg, 2024)
Motivation The rise of quantum computing has offered new opportunities to achieve computational tasks that classical computers cannot. However, to achieve quantum advantage over classical algorithms, it is necessary to…
Alexander Mattick: Reinforcement Learning for Node Selection in Branch-and-Bound (FAU Erlangen-Nürnberg, 2023)
1 Introduction The branch-and-bound algorithm is a fundamental solver for linear mixed integer programming problems. It is used to solve optimization problems with a combination of continuous and integer variables.…
Yongxu Ren: Variational Quantum Compiling with (Deep) Reinforcement Learning (FAU Erlangen-Nürnberg, 2023)
MotivationQuantum computing promises to revolutionize many areas that are hard or impossible to approach with tra- ditional computers. However, due to rigid hardware restrictions and noise sensitivity of currently available…
Alexander Mattick: Beam Tracking as a Time-Varying Reinforcement Learning Problem
The envisioned transition to 5G and 6G technologies have started to transform the properties of established communication networks. In the core of this transformation lies the capability of Base Stations…