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 Learning Graphical Models



Appendix A Proofs

Neural Information Processing Systems

This result is well-known [29] but we include a proof here for completeness. First, let's define the conditional risk of h at x, denoted by R Next, suppose q ( x) null= c and h( x) null= h( x). From the proof of Lemma 4.1, we know that The proof of Lemma 4.5 depends on another lemma, which will also be useful in the unknown hypothesis class setting. This is the first claim of the lemma. Thus, ( null) must be false!





7e0ff37942c2de60cbcbd27041196ce3-Paper.pdf

Neural Information Processing Systems

To measure the difference between two probability distributions, referred to as the source and target, respectively, we exploit both the chain rule and Bayes' theorem to construct conditional transport (CT), which is constituted by both a


Multi-agent active perception with prediction rewards

Neural Information Processing Systems

Multi-agent active perception is a task where a team of agents cooperatively gathers observations to compute a joint estimate of a hidden variable.