Data-driven Discovery of Models (D3M)
Call for Papers
This workshop is about data-driven approaches to model discovery. These approaches aim at developing systems that enable users with subject matter expertise but no data science background to create empirical models of real, complex processes. As a result, 1) subject matter experts are empowered to create empirical models without the need for data scientists, and 2) expert data scientists experience increased productivity through automation.
Call for Papers
Contributions describing work in progress as well as position papers are invited. All contributions must focus on data-driven approaches to the design and deployment of ML/DM models. Of particular interest are methods and proposals that address the following issues:
Presentations of beta versions of tools for automated or guided use of methods or algorithms with respect to performance or run stability are also welcome.
- Ensemble learning
- Learning with privileged information
- Learning with missing data
- Learning from heterogeneous data
- Hyperparameter optimization
- Non-parametric, model-free, and zero-knowledge learning
- Non-parametric causal inference
- Spectral graph embedding and inference
- Automatic feature generation
- Weak supervision
- Workflows with multiple learning methods
- Planning and optimizing learning workflows
Papers must be formatted and written according to the Submission Guidelines on the ICDM 2017 conference web site.
Papers must be submitted electronically via CyberChair.
Submitted papers will be reviewed by at least two independent referees from the Program Committee.
- Paper submission deadline: August 7th, 2017
- Paper notification: September 7th, 2017
- Camera-ready version of accepted papers: September 15th, 2017
- Workshop date: November 18th, 2017
- Ishanu Chattopadhyay, University of Chicago
- Christophe Giraud-Carrier, Brigham Young University
- Madeleine Udell, Cornell University
Please direct questions to Christophe Giraud-Carrier
- Rauf Izmailov, Vencore Labs
- Avi Pfeffer, Charles River Analytics Inc.
- Carey Priebe, Johns Hopkins University
- Juliana Freire, New York University
- Michael Mahoney, University of California Berkeley
- Stephen Bach, Stanford University
- Scott Langevin, Unchartered Software
- Mukesh Dalal, Charles River Analytics
- Hod Lipson, Columbia University
- Artur Dubrawski, Carnegie Mellon University
- Eric Nyberg, Carnegie Mellon University
- Kyle Miller, Carnegie Mellon University
- Barnabas Poczos, Carnegie Mellon University
- William Cleveland, Purdue University
- Mayank Kejriwal, University of Southern California, ISI