Coresets
Determinantal Point Processes
Submodular functions and their optimization
Information-Theoretic Approaches
Applications of Subset Selection
Compute efficient training (training time and energy efficiency)
Active Learning and selecting subsets of unlabelled data for labelling
Human assisted learning
Feature selection and dimensionality reduction
Cost-sensitive feature selection
Model compression
Rule augmentation and Data programming
Image segmentation, image correspondence, and MAP inference in graphical models.
Data Summarization (e.g. video, image collection, document, news summarization)
Peptide Matching, Proteomics, etc.
Learning of neural set functions
The above are just a few of the potential applications and theoretical directions. If you are working on anything related to subset selection in ML, AI, and deep learning, please consider submitting to and attending our workshop!
Submissions in the form of extended abstracts must be at most 6 pages long (not including references and an unlimited number of pages for supplemental material, which reviewers are not required to take into account) and adhere to the ICML format. We accept submissions of work recently published or currently under review. Submissions should be anonymized as described in the submission instructions and should be submitted through: The workshop will not have formal proceedings, but authors of accepted abstracts can choose to have either a link to an Arxiv version of their paper or a pdf published on the workshop webpage. If the authors give us an Arxiv link, we will link it here from the list of accepted papers on this webpage.
Important dates:
Submission deadline: Sunday, June 6th, 23:59 AOE
Author notification: Wednesday, June 16th
Camera-ready deadline and videos for selected talks: June 25th
Workshop date: Saturday, 24th July 2021