{"arxiv_id":"1912.05738","incoming":{"papers":1,"sentences":2,"groups":[{"kind":"background","label":"Cites it as background","papers":1,"sentences":1,"quotes":[{"arxiv_id":"2007.07021","title":"Bayesian Group Regularization in Generalized Linear Models with a Continuous Spike-and-Slab Prior","url":"/paper/2007.07021","section":"Generalized Additive Models","sentence":"Theory for Bayesian estimation and variable selection in high-dimensional Gaussian nonparametric regression has been well-studied under basis expansions [ref] , tree and forest priors [ref] , and Gaussian process priors [ref] .","intent_p":0.97,"contests_p":null}]}],"unlabelled_sentences":1},"outgoing":{"papers":1,"sentences":1,"groups":[{"kind":"supports_this_paper","label":"Cites as support","papers":1,"sentences":1,"quotes":[{"arxiv_id":"1112.0716","title":"Dimension adaptability of Gaussian process models with variable selection and projection","url":"/paper/1112.0716","section":"Introduction","sentence":"Indeed, under such Bayesian approaches, the posterior distribution of the regression function is known to contract to the true function at a near minimax optimal rate, adapting to both unknown smoothness and sparsity [ref] .","intent_p":0.97,"contests_p":null}]}],"unlabelled_sentences":0},"claims":[],"thresholds":{"intent_p":0.8,"supports_p":0.95,"contests_p":0.7},"method":"Citing sentences come from each paper's LaTeX source; every \\cite marks the sentence and the works it cites. Pith's classifier typed each sentence. A label is shown at intent probability 0.8 or more (0.95 for support), a contest at 0.7 or more. Measured accuracy against two blind model families is at https://pith.science/accuracy. The quoted sentences are the citing authors' own.","held_up":null,"_links":{"html":"https://pith.science/paper/1912.05738"}}