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Michael Schaarschmidt
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Automatic Discovery of Composite SPMD Partitioning Strategies in PartIR


Oct 07, 2022
Sami Alabed, Dominik Grewe, Juliana Franco, Bart Chrzaszcz, Tom Natan, Tamara Norman, Norman A. Rink, Dimitrios Vytiniotis, Michael Schaarschmidt

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Learned Force Fields Are Ready For Ground State Catalyst Discovery


Sep 26, 2022
Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose, James S. Spencer, Alexander L. Gaunt, James Kirkpatrick, Simon Axelrod, Peter W. Battaglia, Jonathan Godwin

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Pre-training via Denoising for Molecular Property Prediction


May 31, 2022
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, Jonathan Godwin

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Automap: Towards Ergonomic Automated Parallelism for ML Models


Dec 06, 2021
Michael Schaarschmidt, Dominik Grewe, Dimitrios Vytiniotis, Adam Paszke, Georg Stefan Schmid, Tamara Norman, James Molloy, Jonathan Godwin, Norman Alexander Rink, Vinod Nair, Dan Belov

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* Workshop on ML for Systems at NeurIPS 2021 
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Very Deep Graph Neural Networks Via Noise Regularisation


Jun 15, 2021
Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar VeličkoviΔ‡, James Kirkpatrick, Peter Battaglia

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Learning Index Selection with Structured Action Spaces


Sep 16, 2019
Jeremy Welborn, Michael Schaarschmidt, Eiko Yoneki

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Wield: Systematic Reinforcement Learning With Progressive Randomization


Sep 15, 2019
Michael Schaarschmidt, Kai Fricke, Eiko Yoneki

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* 10 pages, draft paper 
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RLgraph: Flexible Computation Graphs for Deep Reinforcement Learning


Oct 21, 2018
Michael Schaarschmidt, Sven Mika, Kai Fricke, Eiko Yoneki

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LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations


Aug 23, 2018
Michael Schaarschmidt, Alexander Kuhnle, Ben Ellis, Kai Fricke, Felix Gessert, Eiko Yoneki

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Tuning the Scheduling of Distributed Stochastic Gradient Descent with Bayesian Optimization


Dec 01, 2016
Valentin Dalibard, Michael Schaarschmidt, Eiko Yoneki

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