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ML Retrospectives, Surveys & Meta-Analyses (ML-RSA)
Chhavi Yadav · Prabhu Pradhan · Jesse Dodge · Mayoore Jaiswal · Peter Henderson · Abhishek Gupta · Ryan Lowe · Jessica Forde · Joelle Pineau

Fri Dec 11 08:30 AM -- 09:00 PM (PST) @
Event URL: https://ml-retrospectives.github.io/neurips2020/ »

The exponential growth of AI research has led to several papers floating on arxiv, making it difficult to review existing literature. Despite the huge demand, the proportion of survey & analyses papers published is very low due to reasons like lack of a venue and incentives. Our Workshop, ML-RSA provides a platform and incentivizes writing such types of papers. It meets the need of taking a step back, looking at the sub-field as a whole and evaluating actual progress. We will accept 3 types of papers: broad survey papers, meta-analyses, and retrospectives. Survey papers will mention and cluster different types of approaches, provide pros and cons, highlight good source code implementations, applications and emphasize impactful literature. We expect this type of paper to provide a detailed investigation of the techniques and link together themes across multiple works. The main aim of these will be to organize techniques and lower the barrier to entry for newcomers. Meta-Analyses, on the other hand, are forward-looking, aimed at providing critical insights on the current state-of-affairs of a sub-field and propose new directions based on them. These are expected to be more than just an ablation study -- though an empirical analysis is encouraged as it can provide for a stronger narrative. Ideally, they will seek to showcase trends that are not possible to be seen when looking at individual papers. Finally, retrospectives seek to provide further insights ex post by the authors of a paper: these could be technical, insights into the research process, or other helpful information that isn’t apparent from the original work.

Author Information

Chhavi Yadav (NYU, Walmart Labs)
Prabhu Pradhan (Max Planck Institute for Intelligent Systems (MPI-IS))

[Prabhu](https://prabhupradhan.github.io) is a Research Assistant at MPI-IS Tübingen, working on Robustness and Confounding in Machine Learning.

Jesse Dodge (Allen Institute for AI)
Mayoore Jaiswal (IBM)
Peter Henderson (Stanford University)
Abhishek Gupta (Montreal AI Ethics Institute, Microsoft, and McGill University)
Ryan Lowe (McGill)
Jessica Forde (Brown University)
Joelle Pineau (McGill University)

Joelle Pineau is an Associate Professor and William Dawson Scholar at McGill University where she co-directs the Reasoning and Learning Lab. She also leads the Facebook AI Research lab in Montreal, Canada. She holds a BASc in Engineering from the University of Waterloo, and an MSc and PhD in Robotics from Carnegie Mellon University. Dr. Pineau's research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains. She also works on applying these algorithms to complex problems in robotics, health care, games and conversational agents. She serves on the editorial board of the Journal of Artificial Intelligence Research and the Journal of Machine Learning Research and is currently President of the International Machine Learning Society. She is a recipient of NSERC's E.W.R. Steacie Memorial Fellowship (2018), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Senior Fellow of the Canadian Institute for Advanced Research (CIFAR) and in 2016 was named a member of the College of New Scholars, Artists and Scientists by the Royal Society of Canada.

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