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ORCIDRML@ICLR 2019: New Orleans, Louisiana, United States
- Reproducibility in Machine Learning, ICLR 2019 Workshop, New Orleans, Louisiana, United States, May 6, 2019. OpenReview.net 2019
Accepted Papers
- Thomas Boquet, Laure Delisle, Denis Kochetkov, Nathan Schucher, Boris N. Oreshkin, Julien Cornebise:
Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning. - Arnout Devos, Sylvain Chatel, Matthias Grossglauser:
Reproducing Meta-learning with differentiable closed-form solvers. - Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. - Alexandru Mara, Jefrey Lijffijt, Tijl De Bie:
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction. - Brian Lee, Andrew Jackson, Tom Madams, Seth Troisi, Derek Jones:
Minigo: A Case Study in Reproducing Reinforcement Learning Research. - Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini:
Reproducibility in Machine Learning for Health. - David Abel:
simple_rl: Reproducible Reinforcement Learning in Python. - Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer:
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms.
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