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Flexible mean field variational inference using mixtures of non-overlapping exponential families

NeurIPS 2020 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Variational inference theory and gradients

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the non-overlapping-mixtures VI scheme instead of naive auxiliary-variable mean field VILDpred · LDpred simulationsMSEHelped
Use the non-overlapping-mixtures VI scheme instead of naive auxiliary-variable mean field VILDpred · LDpred simulationscorrelationMixed
Use the non-overlapping-mixtures VI scheme instead of MCMCLDpred · LDpred simulationscorrelationNo clear effect
Use the non-overlapping-mixtures VI scheme instead of raw observationsLDpred · LDpred simulationsMSEHelped
Set naive-model σ₀² to 1 (ablation)LDpred naive VI · LDpred simulationscorrelationHurt
Set naive-model σ₀² to 10⁻⁴ rather than 10⁻¹⁰ (ablation)LDpred naive VI · LDpred simulationscorrelationNo clear effect
Use the non-overlapping-mixtures scheme instead of naive auxiliary-variable VISparse probabilistic PCA · Sparse PCA simulationscluster separationHelped
Use the non-overlapping-mixtures scheme instead of naive auxiliary-variable VISparse probabilistic PCA · Sparse PCA simulationsloading sparsityHelped
Reduce naive-model σ₀² toward zero (ablation)Sparse probabilistic PCA · Sparse PCA simulationscluster separationHurt
Use naive sparse probabilistic PCA instead of classical PCASparse probabilistic PCA · Sparse PCA simulationsloading sparsityHurt
Apply sparsity to LDA topic probabilities using the non-overlapping-mixtures trickLDA mean-field VI · LDAsparsity of variational posteriorNo clear effect

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