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S2D-OLAD: From shallow to deep, overcoming limited and adverse data
Workshop held in conjunction with the Ninth International Conference on Learning Representations (ICLR 2021) 
https://s2d-olad.github.io
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ICLR is the premier gathering of professionals dedicated to the advancement of representation learning, which is generally referred to as deep learning. It will be held virtually from May 4th through May 8th, with the workshop taking place on the final day.

We invite submissions of papers on all topics pertinent to deep representation learning in the context of adverse and limited data sets. The workshop will explore challenges and solutions to overcome limited and adverse data. This includes limited and sparse datasets, noisy data, imbalanced classes, heterogenous, non-stationary data, and other related topics. Whilst learning from complex and limited datasets is scientifically and technically important, it is clear that models trained and deployed under such settings can have significant unintended implications when deployed into the real-world. The workshop, therefore, also invites participants to consider the social and ethical implications of AI applied in this context. 

Questions of interest include (but are not limited to):
- What are the challenges and risks associated with deep representation learning from limited and adverse data?
- How do the challenges and required solutions overlap and diverge in deep and shallow representation learning? Can old insights be repurposed for the deep world? 
- What are the most pertinent questions related to deep representation learning from data with adverse properties? Questions to consider are: is it possible to generalize few-shot learning across domains? 
- What are the relative advantages of few-shot learning over fine-tuned transfer learning? What are the impacts of, and solutions to, deep representation learning from long-tailed data and data with imbalanced class priors? Moreover, we welcome, and in fact, encourage other questions.
- What are the moral and social issues related to the applications of models trained on limited and adverse data? Can these be mitigated with new technical solutions?

Key dates:
Submission date:  26 February 2021 (AOE)
Final decisions: 26 March 2021 (AOE)
Workshop: 8 May 2021

Author and Style Instructions:
Please format your papers using the standard ICLR 2021 style files. The page limit is 4 pages (excluding references). 
Papers should be submitted via cmt3: 
https://cmt3.research.microsoft.com/S2DOLAD2021

In addition to papers describing clear research advances, we encourage the submission of short papers that discuss work in progress, new challenges and limitations, and future directions for representations learning to overcome limited and adverse data, along with socially relevant problems, ethical AI, and AI safety. 

Selection Criteria:
All submissions will undergo peer review by the workshop’s program committee. Accepted papers will be chosen based on technical merit, empirical validation, novelty, and suitability to the workshop’s goals.
The workshop aims to provide an engaging platform for dialog that will push the state-of-the-art in representation learning from limited and adverse data. To this end, selected papers will be works in progress and propose novel topics and future directions. Work that has already appeared or is scheduled to appear in a journal, workshop, or conference (including ICLR 2021) must be significantly extended to be eligible for workshop submission. Work that is currently under review at another venue may be submitted. 

Presentation Details:
All accepted abstracts will be presented in the form of a virtual poster. A small number of submissions will be invited to present 15-minute virtual talks. Accepted papers will be made available on the workshop website. However, this does not constitute an archival publication and no formal workshop proceedings will be made available, meaning contributors are free to publish their work in archival journals or conferences.

Code of conduct:
All participants of the workshop must abide by the ICLR code of conduct. We empower and encourage you to report any behavior that makes you or others feel uncomfortable by contacting the ICLR Diversity and Inclusion co-chairs. You can also contact the organizing committee by email.

Workshop Organizers:
Colin Bellinger, colin.bellinger@nrc-cnrc.gc.ca  (National Research Council of Canada) 
Roberto Corizzo, rcorizzo@american.edu (American University) 
Vincent Dumoulin, vdumoulin@google.com (Google Research)
Nathalie Japkowicz, japkowic@american.edu (American University)

Contact Information for ICLR organizers:
https://iclr.cc/Help/Contact?select=OpenReview