Upcoming Events
From Hierarchical Clustering to Phylogenetic CSPs
Abstract: Hierarchical Clustering (HC) is a widely studied problem in unsupervised learning and exploratory data analysis, usually tackled by simple agglomerative procedures like average-linkage, single-linkage…
Evaluating the Robustness and Efficiency of Estimators for Informative Covariate Censoring
Abstract: While right-censored time-to-event outcomes have been studied for decades, handling time-to-event covariates, also known as censored covariates, is now of growing interest. So far,…
Who’s Afraid of AI? Myths and Realities of Generative AI
RSVP Today! Abstract: There has been a spike in concern about existential risk from artificial general intelligence, or AGI. This fear, commonly associated with terms…
Specifying Goals to Deep Neural Networks with Answer Set Programming
Abstract: Methods such as DeepCubeA have used deep reinforcement learning to learn domain-specific heuristic functions in a largely domain-independent fashion to solve planning problems. However,…