Co-organised by: Royal Holloway, University of London (UK) and Karolinska Institutet, Sweden
Conference website: http://clrc.rhul.ac.uk/copa2017/
Quantifying the uncertainty of the predictions produced by classification and regression techniques is an important problem in the field of Machine Learning. Conformal Prediction is a framework for complementing the predictions of Machine Learning algorithms with reliable measures of confidence. The methods developed based on this framework produce well-calibrated confidence measures for individual examples without assuming anything more than that the data are generated independently by the same probability distribution (i.i.d.). The aim of this symposium is to serve as a forum for the presentation of new and ongoing work and the exchange of ideas between researchers on any aspect of Conformal Prediction and its applications.
Prof. Vladimir Vapnik, Data Science Institute, Columbia University, NY
Dr. Andreas Bender, Centre for Molecular Informatics, Department of Chemistry, Cambridge University, UK
Special Session on Novel Directions of Applying Machine Learning in Cheminformatics
There has been a renewed interest in novel machine learning techniques in drug discovery during the last years, and in this session we will mainly focus on the cheminformatics aspects. The speakers will describe the current state of the art, bottlenecks and future directions covering topics like de novo design of novel molecules, improve accuracy in activity prediction, and confidence estimation. The presentations will be followed by round table discussions focusing on current challenges and future opportunities.
Call for papers
Authors are invited to submit original, English-language research contributions or experience reports. Submitted papers will be refereed for quality, correctness, originality, and relevance. Notification and reviews will be communicated via email. All accepted papers will be presented at the conference and published by JMLR Workshop and Conference Proceedings (volume 60). Accepted papers in the special session “Novel Directions of Applying Machine Learning in Cheminformatics” will be invited to submit an extended version of their manuscripts to a special issue in Journal of Cheminformatics. Paper submission deadline: March 31st, 2017.
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