Towards a General Framework for ML-based Self-tuning Databases. - Thomas Schmied; Diego Didona; Andreas C. Döring; Thomas P. Parnell; Nikolas Ioannou (EuroMLSys@EuroSys, 2021)

Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari. - Dominik Schmidt; Thomas Schmied (CoRR, 2021)

Reactive Exploration to Cope With Non-Stationarity in Lifelong Reinforcement Learning. - Christian Alexander Steinparz; Thomas Schmied; Fabian Paischer; Marius-Constantin Dinu; Vihang Prakash Patil; Angela Bitto-Nemling; Hamid Eghbal-zadeh; Sepp Hochreiter (CoLLAs, 2022)

Learning to Modulate pre-trained Models in RL. - Thomas Schmied; Markus Hofmarcher; Fabian Paischer; Razvan Pascanu; Sepp Hochreiter (NeurIPS, 2023)

Retrieval-Augmented Decision Transformer: External Memory for In-context RL. - Thomas Schmied; Fabian Paischer; Vihang Patil; Markus Hofmarcher; Razvan Pascanu; Sepp Hochreiter (CoRR, 2024)

One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptation. - Fabian Paischer; Lukas Hauzenberger; Thomas Schmied; Benedikt Alkin; Marc Peter Deisenroth; Sepp Hochreiter (CoRR, 2024)

A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks. - Thomas Schmied; Thomas Adler; Vihang Patil; Maximilian Beck; Korbinian Pöppel; Johannes Brandstetter; Günter Klambauer; Razvan Pascanu; Sepp Hochreiter (CoRR, 2024)

LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities. - Thomas Schmied; Jörg Bornschein; Jordi Grau-Moya; Markus Wulfmeier; Razvan Pascanu (CoRR, 2025)

pLSTM: parallelizable Linear Source Transition Mark networks. - Korbinian Pöppel; Richard Freinschlag; Thomas Schmied; Wei Lin 019; Sepp Hochreiter (CoRR, 2025)