Follow
Thomas Schmied
Thomas Schmied
PhD Student, Institute for Machine Learning, Johannes Kepler University Linz
Verified email at ml.jku.at
Title
Cited by
Cited by
Year
Towards a general framework for ml-based self-tuning databases
T Schmied, D Didona, A Döring, T Parnell, N Ioannou
Proceedings of the 1st Workshop on Machine Learning and Systems, 24-30, 2021
112021
Reactive exploration to cope with non-stationarity in lifelong reinforcement learning
CA Steinparz, T Schmied, F Paischer, MC Dinu, VP Patil, A Bitto-Nemling, ...
Conference on Lifelong Learning Agents, 441-469, 2022
82022
Fast and data-efficient training of rainbow: an experimental study on atari
D Schmidt, T Schmied
Deep Reinforcement Learning Workshop NeurIPS 2021, 2021
62021
Learning to Modulate pre-trained Models in RL
T Schmied, M Hofmarcher, F Paischer, R Pascanu, S Hochreiter
Advances in Neural Information Processing Systems 36, 2024
32024
InfODist: Online distillation with Informative rewards improves generalization in Curriculum Learning
R Siripurapu, VP Patil, K Schweighofer, MC Dinu, T Schmied, LEF Diez, ...
Deep Reinforcement Learning Workshop NeurIPS 2022, 2022
12022
Self-supervision, data augmentation and online fine-tuning for offline RL
T Schmied
Wien, 2022
12022
Controllable Network Data Balancing with GANs
F Meghdouri, T Schmied, T Gärtner, T Zseby
NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021
2021
Efficient Reinforcement Learning via Self-supervised learning and Model-based methods
T Schmied, M Thiessen
Challenges of Real-World Reinforcement Learning NeurIPS 2020 Workshop, 2020
2020
The system can't perform the operation now. Try again later.
Articles 1–8