Sub-network Laplace approximations always underestimate full-model predictive variance, and two new gradient-based and greedy selection rules provide theoretically grounded improvements.
arXiv preprint arXiv:2211.06516 , year=
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Optimality of Sub-network Laplace Approximations: New Results and Methods
Sub-network Laplace approximations always underestimate full-model predictive variance, and two new gradient-based and greedy selection rules provide theoretically grounded improvements.
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The Enforcement and Feasibility of Hate Speech Moderation on Twitter
80% of hateful tweets remain online after five months with no higher removal rate than non-hateful content, while human-AI moderation pipelines can feasibly cut user exposure below regulatory penalty costs.