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Negative dependence and submodularity

Theory and applications in machine learning

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Call for papers

We invite submissions of papers on any topic related to negative dependence and submodularity in machine learning, including (but not limited to):

  • Submodular optimization
  • Determinantal point processes
  • Volume sampling
  • Recommender systems
  • Experimental design
  • Variance-reduction methods
  • Exploitation/exploration trade-offs (reinforcement, Bayesian Optimization, etc.)
  • Batched active learning
  • Strongly Rayleigh measures
  • Monte Carlo integration
  • Diversity constraints in experimental validation
  • Log-concave polynomials
  • Randomized numerical linear algebra

Please submit your papers via the CMT web site; the submissions should be in PDF format and a maximum of 4 pages long, excluding references and appendices. Submissions must use the anonymized ICML 2020 style file. The submission deadline is Monday, June 15th, 2020, 11:59 PM Anywhere on Earth.

If the research has previously appeared in a journal, workshop, or conference (including ICML 2020), the workshop submission should extend that previous work. Parallel submissions (such as to other conferences) are permitted.

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Useful links

Virtual ICML workshop link

CMT

ICML 2020

NeurIPS 2018 tutorial on negative dependence

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