RML@ICLR

Publications

2019

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