Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 2012 |
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Host publication | Advances in Neural Information Processing Systems |
Pages | 2888-2896 |
Number of pages | 9 |
Volume | 4 |
<mark>Original language</mark> | English |
Event | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 - Lake Tahoe, NV, United States Duration: 3/12/2012 → 6/12/2012 |
Conference | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 |
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Country/Territory | United States |
City | Lake Tahoe, NV |
Period | 3/12/12 → 6/12/12 |
Conference | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 |
---|---|
Country/Territory | United States |
City | Lake Tahoe, NV |
Period | 3/12/12 → 6/12/12 |
We present a general method for deriving collapsed variational inference algorithms for probabilistic models in the conjugate exponential family. Our method unifies many existing approaches to collapsed variational inference. Our collapsed variational inference leads to a new lower bound on the marginal likelihood. We exploit the information geometry of the bound to derive much faster optimization methods based on conjugate gradients for these models. Our approach is very general and is easily applied to any model where the mean field update equations have been derived. Empirically we show significant speed-ups for probabilistic inference using our bound.