Technology
Generalizing Tree Probability Estimation via Bayesian Networks
Cheng Zhang, Frederick A Matsen IV
Neural Information Processing Systems http://nips.cc/
STAR-Caps: Capsule Networks with Straight-Through Attentive Routing
Karim Ahmed, Lorenzo Torresani
Neural Information Processing Systems http://nips.cc/
AI tool Claude helped capture Venezuelan dictator Maduro in US military raid operation: report
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Learning to Influence Human Behavior with Offline Reinforcement Learning
When interacting with people, AI agents do not just influence the state of the world - they also influence the actions people take in response to the agent, and even their underlying intentions and strategies.
Provable Non-linear Inductive Matrix Completion
Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon
Neural Information Processing Systems http://nips.cc/
Optimistic optimization of a Brownian
Jean-Bastien Grill, Michal Valko, Remi Munos
Neural Information Processing Systems http://nips.cc/
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth Stanley, Jeff Clune
Neural Information Processing Systems http://nips.cc/
RobustnessVerificationofTree-basedModels
Although this verification problem is NP-complete in general, we give a more precise complexity characterization. We show that there is a simple linear time algorithm for verifying a single tree, and for tree ensembles the verification problem can be cast as a max-clique problem on a multi-partite graph withbounded boxicity. Forlowdimensional problems when boxicity can be viewed as constant, this reformulation leads to a polynomial time algorithm.