Relative Performance guarantees for Approximate Inference in Latent Dirichlet Allocation Indraneel Mukherjee, David M. Blei · Inference: Prob ( latent variables | data ). Intractable · [TehNeWe06] Collapse latent space for better approximation. · Q: Is the improvement worth the extra computation? · Theorem: For LDA, when documents are long, computation not worth it. · Our rigorous mathematical analysis is also verified empirically: VB vs CVB: per word free energy (10 mov. avgd.) 0.12 2.5 %age change in free energy VB vs CVB: per word free energy (1000 mov. avgd.) 2.0 0.02 0.04 5 10 25 50 0.08 k %age change in free energy 0.10 k 5 10 25 50 1.0 0.06 1.5 0 20 40 60 80 #words 100 120 140 2000 4000 6000 #words 8000 10000 M43 1 0.5 0.0 0.00