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Popular machine learning conference NeurIPS 2019 (Conference on Neural Information Processing Systems) will be held on Sunday, December 8 through Saturday, December 14 at the Vancouver Convention Center. The conference invites papers tutorials, and submissions on cross-disciplinary research where machine learning methods are being used in other fields, as well as methods and ideas from other fields being applied to ML. 

NeurIPS 2019 accepted papers

Yesterday, the conference published the list of their accepted papers. A total of 1429 papers have been selected. Submissions opened from May 1 on a variety of topics such as Algorithms, Applications, Data implementations, Neuroscience, and Cognitive Science, Optimization, Probabilistic Methods, Reinforcement Learning and Planning, and Theory. (The full list of Subject Areas are available here.)

This year at NeurIPS 2019, authors of accepted submissions were mandatorily required to prepare either a 3-minute video or a PDF of slides summarizing the paper or prepare a PDF of the poster used at the conference. This was done to make NeurIPS content accessible to those unable to attend the conference. NeurIPS 2019 also introduced a mandatory abstract submission deadline, a week before final submissions are due. Only a submission with a full abstract was allowed to have the full paper uploaded. The authors were also asked to answer questions from the Reproducibility Checklist during the submission process.

NuerIPS 2019 tutorial program

NeurIPS also invites experts to present tutorials that feature topics that are of interest to a sizable portion of the NeurIPS community and are different from the ones already presented at other ML conferences like ICML or ICLR. They looked for tutorial speakers that cover topics beyond their own research in a comprehensive manner that encompasses multiple perspectives. 


The tutorial chairs for NeurIPS 2019 are Danielle Belgrave and Alice Oh. They initially compiled a list based on the last few years’ publications, workshops, and tutorials presented at NeurIPS and at related venues. They asked colleagues for recommendations and conducted independent research. In reviewing the potential candidates, the chair read papers to understand their expertise and watch their videos to appreciate their style of delivery. The list of candidates was emailed to the General Chair, Diversity & Inclusion Chairs, and the rest of the Organizing Committee for their comments on this shortlist. Following a few adjustments based on their input, the potential speakers were selected.

A total of 9 tutorials have been selected for NeurIPS 2019:

  • Deep Learning with Bayesian Principles – Emtiyaz Khan
  • Efficient Processing of Deep Neural Network: from Algorithms to Hardware Architectures – Vivienne Sze
  • Human Behavior Modeling with Machine Learning: Opportunities and Challenges – Nuria Oliver, Albert Ali Salah
  • Interpretable Comparison of Distributions and Models – Wittawat Jitkrittum, Dougal Sutherland, Arthur Gretton
  • Language Generation: Neural Modeling and Imitation Learning –  Kyunghyun Cho, Hal Daume III
  • Machine Learning for Computational Biology and Health – Anna Goldenberg, Barbara Engelhardt
  • Reinforcement Learning: Past, Present, and Future Perspectives – Katja Hofmann
  • Representation Learning and Fairness – Moustapha Cisse, Sanmi Koyejo
  • Synthetic Control – Alberto Abadie, Vishal Misra, Devavrat Shah

NeurIPS 2019 Workshops

NeurIPS Workshops are primarily used for discussion of work in progress and future directions. This time the number of Workshop Chairs doubled, from two to four; selected chairs are Jenn Wortman Vaughan, Marzyeh Ghassemi, Shakir Mohamed, and Bob Williamson. However, the number of workshop submissions went down from 140 in 2018 to 111 in 2019. Of these 111 submissions, 51 workshops were selected. The full list of selected Workshops is available here

The NeurIPS 2019 chair committee introduced new guidelines, expectations, and selection criteria for the Workshops. This time workshops had an important focus on the nature of the problem, intellectual excitement of the topic, diversity, and inclusion, quality of proposed invited speakers, organizational experience and ability of the team and more. 

The Workshop Program Committee consisted of 37 reviewers with each workshop proposal assigned to two reviewers. The reviewer committee included more senior researchers who have been involved with the NeurIPS community. Reviewers were asked to provide a summary and overall rating for each workshop, a detailed list of pros and cons, and specific ratings for each of the new criteria. After all reviews were submitted, each proposal was assigned to two of the four chair committee members. The chair members looked through assigned proposals and their reviews to form an educated assessment of the pros and cons of each. Finally, the entire chair held a meeting to discuss every submitted proposal to make decisions. 

You can check more details about the conference on the NeurIPS website. As always keep checking this space for more content about the conference. In the meanwhile, you can read our previous year coverage:

NeurIPS Invited Talk: Reproducible, Reusable, and Robust Reinforcement Learning

NeurIPS 2018: How machine learning experts can work with policymakers to make good tech decisions [Invited Talk]

NeurIPS 2018: Rethinking transparency and accountability in machine learning

NeurIPS 2018: Developments in machine learning through the lens of Counterfactual Inference [Tutorial]

Accountability and algorithmic bias: Why diversity and inclusion matters [NeurIPS Invited Talk]

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