Stanford AI Lab Showcases Research at AAAI 2022 Conference
The Stanford Artificial Intelligence Laboratory (SAIL) has announced its contributions to the 36th AAAI Conference on Artificial Intelligence, held virtually from February 22 to March 1, 2022. The lab presented a diverse portfolio of accepted papers covering advanced topics in machine learning and artificial intelligence. Key research includes partner-aware algorithms for decentralized cooperative bandit teams, constraint sampling in reinforcement learning to accelerate training, and IS-Count, a method for large-scale object counting in satellite imagery using importance sampling. Additionally, SAIL introduced PantheonRL, a software package for multi-agent reinforcement learning, and investigated synthetic disinformation attacks on automated fact-checking systems. Another significant paper focused on similarity search techniques for efficient active learning of rare concepts. This collection highlights Stanford's ongoing innovation in areas such as human-robot interaction, remote sensing, and AI security. The announcement provides direct links to academic papers, video presentations, and project websites, encouraging further engagement with the researchers. This dissemination of knowledge underscores the laboratory's commitment to advancing the field through open collaboration and rigorous scientific inquiry at one of the premier global AI conferences.
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Stanford AI Lab Showcases Research at AAAI 2022 Conference
The Stanford Artificial Intelligence Laboratory (SAIL) has announced its contributions to the 36th AAAI Conference on Artificial Intelligence, held virtually from February 22 to March 1, 2022. The lab presented a diverse portfolio of accepted papers covering advanced topics in machine learning and artificial intelligence. Key research includes partner-aware algorithms for decentralized cooperative bandit teams, constraint sampling in reinforcement learning to accelerate training, and IS-Count, a method for large-scale object counting in satellite imagery using importance sampling. Additionally, SAIL introduced PantheonRL, a software package for multi-agent reinforcement learning, and investigated synthetic disinformation attacks on automated fact-checking systems. Another significant paper focused on similarity search techniques for efficient active learning of rare concepts. This collection highlights Stanford's ongoing innovation in areas such as human-robot interaction, remote sensing, and AI security. The announcement provides direct links to academic papers, video presentations, and project websites, encouraging further engagement with the researchers. This dissemination of knowledge underscores the laboratory's commitment to advancing the field through open collaboration and rigorous scientific inquiry at one of the premier global AI conferences.
The Stanford AI Lab Blog