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KR2ML - Knowledge Representation and Reasoning Meets Machine Learning
Veronika Thost · Kartik Talamadupula · Vivek Srikumar · Chenwei Zhang · Josh Tenenbaum

Fri Dec 11 08:40 AM -- 05:25 PM (PST) @ None
Event URL: https://kr2ml.github.io/2020/ »

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. The KR2ML workshop aims at the intersection of these two subfields of AI. It will shine a light on the synergies that (could/should) exist between KRR and ML, and will initiate a discussion about the key challenges in the field.

Fri 8:40 a.m. - 9:00 a.m.
Poster Teasers (Talk)
Fri 9:00 a.m. - 9:05 a.m.
Opening Remarks (Talk)
Fri 9:06 a.m. - 9:35 a.m.
Invited Talk #1 (Talk)   
Oren Etzioni
Fri 9:36 a.m. - 10:05 a.m.
Invited Talk #2 (Talk)
Victoria Lin
Fri 10:06 a.m. - 10:35 a.m.
Invited Talk #3 (Talk)   
Tim Rocktäschel
Fri 10:35 a.m. - 10:50 a.m.
Q&A #1 (Discussion Panel)
Oren Etzioni, Tim Rocktäschel, Victoria Lin
Fri 10:50 a.m. - 11:05 a.m.
Break #1 (Break)
Fri 11:06 a.m. - 11:35 a.m.
Invited Talk #4 (Talk)   
Jure Leskovec
Fri 11:36 a.m. - 12:05 p.m.
Invited Talk #5 (Talk)   
Heng Ji
Fri 12:06 p.m. - 12:35 p.m.
Invited Talk #6 (Talk)   
Jiajun Wu
Fri 12:35 p.m. - 12:50 p.m.
Q&A #2 (Discussion Panel)
Heng Ji, Jure Leskovec, Jiajun Wu
Fri 12:50 p.m. - 2:00 p.m.
Poster Session (Breakout)  link »
Fri 2:00 p.m. - 2:45 p.m.
Panel #1 (Discussion Panel)
Yoshua Bengio, Daniel Kahneman, Henry Kautz, Luis Lamb Lamb, Gary Marcus, Francesca Rossi
Fri 3:01 p.m. - 3:15 p.m.
Contributed Talk #2-v2 (Talk)
León Illanes
Fri 3:30 p.m. - 3:45 p.m.
Q&A #3 (Discussion Panel)
Fri 3:45 p.m. - 4:00 p.m.
Break #2 (Break)
Fri 4:01 p.m. - 4:35 p.m.
Invited Talk #7 (Talk)   
Yoshua Bengio
Fri 4:35 p.m. - 5:20 p.m.
Panel #2 (Discussion Panel)
Oren Etzioni, Heng Ji, Subbarao Kambhampati, Victoria Lin, Jiajun Wu
Fri 5:20 p.m. - 5:25 p.m.
Closing Remarks (Talk)
Poster #1 (Talk)   
Hongyu Ren
Poster #2 (Talk)
Xiang Ren
Poster #3 (Talk)   
Srivamshi Pittala
Poster #4 (Talk)   
Lu Zhang
Poster #5 (Talk)   
Osama Rama
Poster #6 (Talk)   
Sheila McIlraith
Poster #7 (Talk)   
Neev Parikh
Poster #8 (Talk)   
Phillip Christoffersen
Poster #9 (Talk)   
Tara Safavi
Poster #10 (Talk)   
Jun Yan
Poster #11 (Talk)   
Alexander Rader
Poster #12 (Talk)   
Luca Biggio
Poster #13 (Talk)   
Jupinder Parmar
Poster #14 (Talk)   
George Stoica
Poster #15 (Talk)   
George Stoica
Poster #16 (Talk)   
Feiyang Niu, Govind Thattai
Poster #17 (Talk)   
Victor Kolev
Poster #18 (Talk)   
Pashootan Vaezipoor
Poster #19 (Talk)   
Agnieszka Dobrowolska
Poster #20 (Talk)   
Maayan Shvo

Author Information

Veronika Thost (MIT-IBM Watson AI Lab, IBM Research)
Kartik Talamadupula (IBM Research)
Vivek Srikumar (University of Utah)
Chenwei Zhang (Amazon)
Josh Tenenbaum (MIT)

Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).

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