Overview

Machine learning (ML) has seen a tremendous amount of recent success and has been applied in a variety of applications. However, it comes with several drawbacks, such as the need for large amounts of training data and the lack of explainability and verifiability of the results. In many domains, there is structured knowledge (e.g., from electronic health records, laws, clinical guidelines, or common sense knowledge) which can be leveraged for reasoning in an informed way (i.e., including the information encoded in the knowledge representation itself) in order to obtain high quality answers. Symbolic approaches for knowledge representation and reasoning (KRR) are less prominent today - mainly due to their lack of scalability - but their strength lies in the verifiable and interpretable reasoning that can be accomplished. This workshop aims at the intersection of these two sub-fields of AI, and hopes to shine a light on the synergies that exist between KRR and ML. The 4th KR2ML workshop will be a virtual workshop at NeurIPS 2020, Dec. 11. Our goal is to advance the general discussion of the topic by highlighting contributions proposing innovative approaches integrating KRR and ML.


Speakers

Yoshua Bengio
Yoshua Bengio
Mila, Université de Montréal
Oren Etzioni
Oren Etzioni
Allen Institute for Artificial Intelligence (AI2)
Heng Ji
Heng Ji
University of Illinois at Urbana-Champaign
Jure Leskovec
Jure Leskovec
Stanford University
Victoria Lin
Victoria Lin
Salesforce Research
Tim Rocktäschel
Tim Rocktäschel
Facebook AI Research
Jiajun Wu
Jiajun Wu
Stanford University
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Organizers

Veronika Thost
Veronika Thost
MIT-IBM Watson AI Lab
Kartik Talamadupula
Kartik Talamadupula
IBM Research
Vivek Srikumar
Vivek Srikumar
University of Utah
Chenwei Zhang
Chenwei Zhang
Amazon
Joshua Tenenbaum
Joshua Tenenbaum
Massachusetts Institute of Technology

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Contact: kr2ml.ws@gmail.com.