Automatic Checking of Component Protocols in Component-Based Systems. - Wolf Zimmermann; Michael Schaarschmidt (SC@ETAPS, 2006)

Towards a Scalable and Unified REST API for Cloud Data Stores. - Felix Gessert; Steffen Friedrich; Wolfram Wingerath; Michael Schaarschmidt; Norbert Ritter (GI-Jahrestagung, 2014)

The Cache Sketch: Revisiting Expiration-based Caching in the Age of Cloud Data Management. - Felix Gessert; Michael Schaarschmidt; Wolfram Wingerath; Steffen Friedrich; Norbert Ritter (BTW, 2015)

Towards Automated Polyglot Persistence. - Michael Schaarschmidt; Felix Gessert; Norbert Ritter (BTW, 2015)

Tuning the Scheduling of Distributed Stochastic Gradient Descent with Bayesian Optimization. - Valentin Dalibard; Michael Schaarschmidt; Eiko Yoneki (CoRR, 2016)

Learning Runtime Parameters in Computer Systems with Delayed Experience Injection. - Michael Schaarschmidt; Felix Gessert; Valentin Dalibard; Eiko Yoneki (CoRR, 2016)

BOAT: Building Auto-Tuners with Structured Bayesian Optimization. - Valentin Dalibard; Michael Schaarschmidt; Eiko Yoneki (WWW, 2017)

Quaestor: Query Web Caching for Database-as-a-Service Providers. - Felix Gessert; Michael Schaarschmidt; Wolfram Wingerath; Erik Witt; Eiko Yoneki; Norbert Ritter (Proc. VLDB Endow., 2017)

LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations. - Michael Schaarschmidt; Alexander Kuhnle; Ben Ellis; Kai Fricke; Felix Gessert; Eiko Yoneki (CoRR, 2018)

RLgraph: Flexible Computation Graphs for Deep Reinforcement Learning. - Michael Schaarschmidt; Sven Mika; Kai Fricke; Eiko Yoneki (CoRR, 2018)

RLgraph: Modular Computation Graphs for Deep Reinforcement Learning. - Michael Schaarschmidt; Sven Mika; Kai Fricke; Eiko Yoneki (SysML, 2019)

Wield: Systematic Reinforcement Learning With Progressive Randomization. - Michael Schaarschmidt; Kai Fricke; Eiko Yoneki (CoRR, 2019)

Learning Index Selection with Structured Action Spaces. - Jeremy Welborn; Michael Schaarschmidt; Eiko Yoneki (CoRR, 2019)

Reinforcement Learning for the Adaptive Scheduling of Educational Activities. - Jonathan Bassen; Bharathan Balaji; Michael Schaarschmidt; Candace Thille; Jay Painter; Dawn Zimmaro; Alex Games; Ethan Fast; John C. Mitchell (CHI, 2020)

End-to-end deep reinforcement learning in computer systems. - Michael Schaarschmidt (2020)

Very Deep Graph Neural Networks Via Noise Regularisation. - Jonathan Godwin; Michael Schaarschmidt; Alexander L. Gaunt; Alvaro Sanchez-Gonzalez; Yulia Rubanova; Petar Velickovic; James Kirkpatrick; Peter W. Battaglia (CoRR, 2021)

Automap: Towards Ergonomic Automated Parallelism for ML Models. - Michael Schaarschmidt; Dominik Grewe; Dimitrios Vytiniotis; Adam Paszke; Georg Stefan Schmid; Tamara Norman; James Molloy; Jonathan Godwin; Norman Alexander Rink; Vinod Nair; Dan Belov (CoRR, 2021)

Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond. - Jonathan Godwin; Michael Schaarschmidt; Alexander L. Gaunt; Alvaro Sanchez-Gonzalez; Yulia Rubanova; Petar Velickovic; James Kirkpatrick; Peter W. Battaglia (ICLR, 2022)

Learned Force Fields Are Ready For Ground State Catalyst Discovery. - Michael Schaarschmidt; Morgane Riviere; Alex M. Ganose; James S. Spencer; Alexander L. Gaunt; James Kirkpatrick; Simon Axelrod; Peter W. Battaglia; Jonathan Godwin (CoRR, 2022)

Automatic Discovery of Composite SPMD Partitioning Strategies in PartIR. - Sami Alabed; Dominik Grewe; Juliana Franco; Bart Chrzaszcz; Tom Natan; Tamara Norman; Norman A. Rink; Dimitrios Vytiniotis; Michael Schaarschmidt (CoRR, 2022)

Pre-training via Denoising for Molecular Property Prediction. - Sheheryar Zaidi; Michael Schaarschmidt; James Martens; Hyunjik Kim; Yee Whye Teh; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Razvan Pascanu; Jonathan Godwin (ICLR, 2023)

PartIR: Composing SPMD Partitioning Strategies for Machine Learning. - Sami Alabed; Bart Chrzaszcz; Juliana Franco; Dominik Grewe; Dougal Maclaurin; James Molloy; Tom Natan; Tamara Norman; Xiaoyue Pan; Adam Paszke; Norman A. Rink; Michael Schaarschmidt; et al. (CoRR, 2024)

PartIR: Composing SPMD Partitioning Strategies for Machine Learning. - Sami Alabed; Daniel Belov; Bart Chrzaszcz; Juliana Franco; Dominik Grewe; Dougal Maclaurin; James Molloy; Tom Natan; Tamara Norman; Xiaoyue Pan; Adam Paszke; Norman A. Rink; et al. (ASPLOS (1), 2025)