AAAI-20 Tutorial Forum

Thirty-Fourth Conference on Artificial Intelligence
February 7-8, 2020
New York, NY, USA

What Is the Tutorial Forum?

The Tutorial Forum provides an opportunity for researchers and practitioners to spend two days each year exploring exciting advances in disciplines outside their normal focus. We believe this type of forum is essential for the cross fertilization, cohesiveness, and vitality of the AI field. We all have a lot to learn from each other; the Tutorial Forum promotes the continuing education of each member of AAAI.


The following list of tutorials have been accepted for presentation at AAAI-20 (list is preliminary and may be amended):

Half-Day Tutorials

AI in Precision Medicine: Towards Knowledge Empowered Intelligence over “Small” Data
Fei Wang

AI Planning for Robotics with ROSPlan
Michael Cashmore and Daniele Magazzeni

Differential Deep Learning on Graphs and its Applications
Chengxi Zang and Fei Wang

Exploration-Exploitation in Reinforcement Learning
Mohammad Ghavamzadeh, Alessandro Lazaric and Matteo Pirotta

Fairness and Bias in Peer Review and other Sociotechnical Intelligent Systems
Nihar Shah and Zachary Lipton

Graph Neural Networks: Models and Applications
Yao Ma, Wei Jin, Jiliang Tang, Lingfei Wu and Tengfei Ma

Logic-Enabled Verification and Explanation of ML Models
Alexey Ignatiev, Joao Marques-Silva, Kuldeep Meel and Nina Narodytska

New Frontiers of Automated Mechanism Design for Pricing and Auctions
Maria-Florina Balcan, Tuomas Sandholm and Ellen Vitercik

Optimization and Learning Approaches to Resource Allocation for Social Good
Sanmay Das, John Dickerson, Duncan McElfresh and Bryan Wilder

Probabilistic Circuits: Representations, Inference, Learning and Applications
Guy Van den Broeck, Nicola Di Mauro and Antonio Vergari

Recent Advances in Fair Resource Allocation
Rupert Freeman and Nisarg Shah

Recent Advances in Transferable Representation Learning
Muhao Chen, Kai-Wei Chang and Dan Roth

Recent Directions in Heuristic-Search
Ariel Felner, Sven Koenig, Nathan Sturtevant and Daniel Harabor

Representation Learning for Causal Inference
Sheng Li, Liuyi Yao, Yaliang Li, Jing Gao and Aidong Zhang

Statistical Machine Learning: Big, Multi-Source and Sparse Data with Complex Relations and Dynamics
Trong Dinh Thac Do, Longbing Cao and Jinjin Guo

Synthesizing Explainable and Deceptive Behavior in Human-AI Interaction
Subbarao Kambhampati, Tathagata Chakraborti, Sarath Sreedharan and Anagha Kulkarni

3/4-Day Tutorial

Explainable AI: Foundations, Industrial Applications, Practical Challenges, and Lessons Learned
Freddy Lecue, Krishna Gade, Fosca Giannotti, Sahin Geyik, Riccardo Guidotti, Krishnaram Kenthapadi, Pasquale Minervini, Varun Mithal and Ankur Taly

Quarter-Day Tutorials

Creative and Artistic Writing via Text Generation
Juntao Li and Rui Yan

Guidelines for Human-AI Interaction
Besmira Nushi, Dan Weld, Saleema Amershi and Adam Adam

Modularizing Natural Language Processing
Zhenzhong Liu, Zhiting Hu and Eric Xing

Multi-Agent Distributed Constrained Optimization
Ferdinando Fioretto and William Yeah

Recent Advances in Machine Teaching: From Machine to Human
Yao Zhou and Jingrui He

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