Michael Schaarschmidt
Michael Schaarschmidt
Research Scientist, Isomorphic Labs
Verified email at
Cited by
Cited by
Tensorforce: A tensorflow library for applied reinforcement learning
M Schaarschmidt, A Kuhnle, K Fricke
Web page, 2017
BOAT: Building auto-tuners with structured Bayesian optimization
V Dalibard, M Schaarschmidt, E Yoneki
Proceedings of the 26th International Conference on World Wide Web, 479-488, 2017
Simple gnn regularisation for 3d molecular property prediction and beyond
J Godwin, M Schaarschmidt, AL Gaunt, A Sanchez-Gonzalez, ...
International conference on learning representations, 2021
Reinforcement learning for the adaptive scheduling of educational activities
J Bassen, B Balaji, M Schaarschmidt, C Thille, J Painter, D Zimmaro, ...
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems …, 2020
Pre-training via denoising for molecular property prediction
S Zaidi, M Schaarschmidt, J Martens, H Kim, YW Teh, ...
arXiv preprint arXiv:2206.00133, 2022
Towards automated polyglot persistence
M Schaarschmidt, F Gessert, N Ritter
Datenbanksysteme für Business, Technologie und Web (BTW 2015), 2015
LIFT: Reinforcement learning in computer systems by learning from demonstrations
M Schaarschmidt, A Kuhnle, B Ellis, K Fricke, F Gessert, E Yoneki
arXiv preprint arXiv:1808.07903, 2018
Quaestor: Query web caching for database-as-a-service providers
F Gessert, M Schaarschmidt, W Wingerath, E Witt, E Yoneki, N Ritter
Proceedings of the VLDB Endowment 10 (12), 1670-1681, 2017
Towards a Scalable and Unified REST API for Cloud Data Stores.
F Gessert, S Friedrich, W Wingerath, M Schaarschmidt, N Ritter
GI-Jahrestagung, 723-734, 2014
Rlgraph: modular computation graphs for deep reinforcement learning
M Schaarschmidt, S Mika, K Fricke, E Yoneki
Proceedings of Machine Learning and Systems 1, 65-80, 2019
Very deep graph neural networks via noise regularisation
J Godwin, M Schaarschmidt, A Gaunt, A Sanchez-Gonzalez, Y Rubanova, ...
arXiv preprint arXiv:2106.07971, 2021
The cache sketch: Revisiting expiration-based caching in the age of cloud data management
F Gessert, M Schaarschmidt, W Wingerath, S Friedrich, N Ritter
Gesellschaft für Informatik eV, 2015
Automap: Towards ergonomic automated parallelism for ml models
M Schaarschmidt, D Grewe, D Vytiniotis, A Paszke, GS Schmid, T Norman, ...
arXiv preprint arXiv:2112.02958, 2021
Learning runtime parameters in computer systems with delayed experience injection
M Schaarschmidt, F Gessert, V Dalibard, E Yoneki
arXiv preprint arXiv:1610.09903, 2016
Learning index selection with structured action spaces
J Welborn, M Schaarschmidt, E Yoneki
arXiv preprint arXiv:1909.07440, 2019
Learned force fields are ready for ground state catalyst discovery
M Schaarschmidt, M Riviere, AM Ganose, JS Spencer, AL Gaunt, ...
arXiv preprint arXiv:2209.12466, 2022
Wield: Systematic reinforcement learning with progressive randomization
M Schaarschmidt, K Fricke, E Yoneki
arXiv preprint arXiv:1909.06844, 2019
Tuning the scheduling of distributed stochastic gradient descent with Bayesian optimization
V Dalibard, M Schaarschmidt, E Yoneki
arXiv preprint arXiv:1612.00383, 2016
Towards latency: An online learning mechanism for caching dynamic query content
M Schaarschmidt
Master’s thesis, University of Cambridge Computer Laboratory, 6 2015, 2015
How to train your learners: Reinforcement learning for the scheduling of online learning activities
J Bassen, B Balaji, M Schaarschmidt, C Thille, J Painter, D Zimmaro, ...
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