Wavelet DPP kernels deliver improved continuous variance reduction and a discretization procedure that preserves decay rates for discrete ML subsampling tasks including rough objectives.
Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
Abstract. Whenever a new approach to perform Bayesian computation is introduced, a common practice is to showcase this approach on a binary regression model and datasets of moderate size. This paper discusses to which extent this practice is sound. It also reviews the current state of the art of Bayesian computation, using binary regression as a running example. Both sampling-based algorithms (importance sampling, MCMC and SMC) and fast approximations (Laplace and EP) are covered. Extensive numerical results are provided, some of which might go against conventional wisdom regarding the effectiveness of certain algorithms. Implications for other problems (variable selection) and other models are also discussed.
representative citing papers
dynesty is an open-source Python package for dynamic nested sampling that improves efficiency in Bayesian posterior and evidence estimation compared to MCMC on certain problems.
citing papers explorer
-
State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
Wavelet DPP kernels deliver improved continuous variance reduction and a discretization procedure that preserves decay rates for discrete ML subsampling tasks including rough objectives.
-
dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences
dynesty is an open-source Python package for dynamic nested sampling that improves efficiency in Bayesian posterior and evidence estimation compared to MCMC on certain problems.