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KR2ML - Knowledge Representation and Reasoning Meets Machine Learning
Veronika Thost · Christian Muise · Kartik Talamadupula · Sameer Singh · Christopher Ré

Fri Dec 13 08:00 AM -- 06:00 PM (PST) @ West 109 + 110
Event URL: https://kr2ml.github.io/2019/ »

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 8:00 a.m. - 8:05 a.m.
Opening Remarks (Talk)
Fri 8:05 a.m. - 8:35 a.m.
Invited Talk (William W. Cohen) (Talk)
William Cohen
Fri 8:35 a.m. - 8:50 a.m.
Contributed Talk: Neural-Guided Symbolic Regression with Asymptotic Constraints (Talk)
Rishabh Singh
Fri 8:50 a.m. - 9:05 a.m.
Contributed Talk: Towards Finding Longer Proofs (Talk)
Zsolt Zombori
Fri 9:05 a.m. - 9:20 a.m.
Contributed Talk: Neural Markov Logic Networks (Talk)
Ondrej Kuzelka
Fri 9:20 a.m. - 9:45 a.m.
Poster Spotlights A (23 posters) (Talk)
DongHa Bahn, Xiaoran (Sean) Xu, Shih-Chieh Su, Dan Cunnington, Wonseok Hwang, Sarthak Dash, Alberto Camacho, Theodoros Salonidis, Shiyang Li, Yuyu Zhang, Habibeh Naderi, Zhe Zeng, Pasha Khosravi, Pedro Colon-Hernandez, Dimitris Diochnos, David Windridge, Robin Manhaeve, Vaishak Belle, Brendan Juba, Naveen Sundar Govindarajulu, Joe Bockhorst
Fri 9:45 a.m. - 10:30 a.m.
Coffee Break + Poster Session (Break)
Fri 10:30 a.m. - 11:00 a.m.
Invited Talk (Xin Luna Dong) (Talk)
Luna Dong
Fri 11:00 a.m. - 11:15 a.m.
Contributed Talk: Layerwise Knowledge Extraction from Deep Convolutional Networks (Talk)
Simon Odense
Fri 11:15 a.m. - 11:30 a.m.
Contributed Talk: Ontology-based Interpretable Machine Learning with Learnable Anchors (Talk)
Thi Kim Phung Lai
Fri 11:30 a.m. - 11:45 a.m.
Contributed Talk: Learning multi-step spatio-temporal reasoning with Selective Attention Memory Network (Talk)
T.S. Jayram
Fri 11:45 a.m. - 12:00 p.m.
Contributed Talk: MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library (Talk)
Dmitry Kazhdan
Fri 12:00 p.m. - 12:30 p.m.
Invited Talk (Vivek Srikumar) (Talk)
Vivek Srikumar
Fri 12:30 p.m. - 2:00 p.m.
Lunch Break (Break)
Fri 2:00 p.m. - 2:30 p.m.
Invited Talk (Francesca Rossi) (Talk)
Francesca Rossi
Fri 2:30 p.m. - 2:45 p.m.
Contributed Talk: TP-N2F: Tensor Product Representation for Natural To Formal Language Generation (Talk)
Kezhen Chen
Fri 2:45 p.m. - 3:00 p.m.
Contributed Talk: TabFact: A Large-scale Dataset for Table-based Fact Verification (Talk)
Wenhu Chen
Fri 3:00 p.m. - 3:15 p.m.
Contributed Talk: LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games (Talk)
Leonard Adolphs
Fri 3:15 p.m. - 3:30 p.m.
Poster Spotlights B (13 posters) (Talk)
Alberto Camacho, Chris Percy, Vaishak Belle, Beliz Gunel, Toryn Klassen, Tillman Weyde, Mohamed Ghalwash, Siddhant Arora, León Illanes, Jonathan Raiman, Qing Wang, Alexander Lew, Tiffany Min
Fri 3:30 p.m. - 4:15 p.m.
Coffee Break + Poster Session (Break)
Fri 4:15 p.m. - 4:45 p.m.
Invited Talk (Yejin Choi) (Talk)
Yejin Choi
Fri 4:45 p.m. - 5:15 p.m.
Invited Talk (Guy Van den Broeck) (Talk)
Guy Van den Broeck
Fri 5:15 p.m. - 5:55 p.m.
Discussion Panel
Fri 5:55 p.m. - 6:00 p.m.
Closing Remarks (Talk)

Author Information

Veronika Thost (MIT-IBM Watson AI Lab)
Christian Muise (IBM Research AI)
Kartik Talamadupula (IBM Research)
Sameer Singh (University of California, Irvine)

Sameer Singh is an Assistant Professor at UC Irvine working on robustness and interpretability of machine learning. Sameer has presented tutorials and invited workshop talks at EMNLP, Neurips, NAACL, WSDM, ICLR, ACL, and AAAI, and received paper awards at KDD 2016, ACL 2018, EMNLP 2019, AKBC 2020, and ACL 2020. Website: http://sameersingh.org/

Chris Ré (Stanford)

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