Overview
The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. AAAI-22 will have a diverse technical track, student abstracts, poster sessions, invited speakers, tutorials, workshops, and exhibit and competition programs, all selected according to the highest reviewing standards.
Accepted publications
Workshops
AAAI 2022 Workshop on Combining Learning and Reasoning: Programming Languages, Formalisms, and Representations (CLeaR)
Unknown date
AAAI 2022 DE-FACTIFY Workshop: Multi-Modal Fake News and Hate-Speech Detection
February 22
AAAI 2022 Workshop on Fair Clustering & Unsupervised Learning
Unknown date
The goal of this tutorial is to introduce a wide audience interested in algorithmic fairness to the nascent research area of fair clustering.
Amazon organizers: Matthäus Kleindessner, Aravind Srinivasan (Amazon Scholar),
Website: https://www.fairclustering.com
Amazon organizers: Matthäus Kleindessner, Aravind Srinivasan (Amazon Scholar),
Website: https://www.fairclustering.com
Tutorials
AAAI 2022 Workshop on Formal Verification of Deep Neural Networks: Theory and Practice
February 23
Amazon organizer: Cho-Jui Hsieh (Amazon Visiting Academic)
AAAI 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
February 23
Lingfei Wu, Yu Chen, Heng Ji (Amazon Scholar) , Yunyao Li and Bang Liu
AAAI 2022 Workshop on Privacy-Preserving Artificial Intelligence
Unknown date
Related content
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Source: New York TimesNovember 16, 2023Real-world deployment requires notions of fairness that are task relevant and responsive to the available data, recognition of unforeseen variation in the “last mile” of AI delivery, and collaboration with AI activists.
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November 06, 2023One Howard student and five faculty members awarded for projects that elevate differentiated research.
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October 17, 2023Research award recipients named as part of the JHU + Amazon Initiative for Interactive AI (AI2AI), now in its second year.
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October 12, 2023In a series of papers, Amazon researchers performed a theoretical analysis of a simplified problem that led to a learnable learning-rate scheduler, applied that scheduler to a more complex neural model, and distilled the results into a practical algorithm.