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Challenges and Opportunities in High Dimensional Variational Inference

NeurIPS 2021 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Variational inference theory and gradients · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
apply PSIS correction to exclusive KL approximation (ablation)BBVI exclusive KL · posteriordb radonpredictive log-likelihood: -353.0 -> -325.0Helped
apply PSIS correction to exclusive KL approximation (ablation)BBVI exclusive KL · posteriordb diamondspredictive log-likelihood: -2.6 -> 1.5Helped
apply PSIS correction to exclusive KL approximation (ablation)BBVI exclusive KL · posteriordb mesquitepredictive log-likelihood: -2512 -> -5418Hurt
apply PSIS correction to exclusive KL approximation (ablation)BBVI exclusive KL · posteriordb nes1996predictive log-likelihood: -412.8 -> -427.9Hurt
apply PSIS correction to inclusive KL approximation (ablation)BBVI inclusive KL · posteriordb dogspredictive log-likelihood: -110 -> -70.5Helped
apply PSIS correction to inclusive KL approximation (ablation)BBVI inclusive KL · posteriordb nes1996predictive log-likelihood: -2140.5 -> -499.3Helped
use exclusive KL instead of inclusive KLBBVI · posteriordb diamondspredictive log-likelihood: -3196.6 -> -2.6Helped
use exclusive KL instead of inclusive KLBBVI · posteriordb radonpredictive log-likelihood: -377.4 -> -353.0Helped
use exclusive KL instead of inclusive KLBBVI · posteriordb dogspredictive log-likelihood: -110 -> -71.2Helped
use exclusive KL instead of inclusive KL in low dimensionBBVI · posteriordb arK (D=7)predictive log-likelihood: -34.3 -> -35.2Hurt
increase target dimension D from 2 to 50 with inclusive KLmean-field and flow BBVI · robust regressionPareto k-hatHurt
use normalizing-flow family with inclusive KLBBVI · robust regressionlargest converged dimension: D = 2Hurt

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