|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Fast Variational Inference in the Conjugate Exponential Family
If you have a question about this talk, please contact Microsoft Research Cambridge Talks Admins.
This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
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.
This talk is part of the Microsoft Research Machine Learning and Perception Seminars series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsAnnual Meeting of the Cambridge Cell Cycle Club Graduate Workshops in Economic and Social History The Yerushah Lecture 2012
Other talksAnthropometric measures and health in nineteenth and twentieth century Sweden: precise title tbc Lifting the mystery of cyclic nucleotide signalling in kinetoplastids Of mice and men and Tasmanian devils; assembling reference genomes from single cells. Evolution and development of early land plant rooting systems Clinical Systems Medicine in Acute Myeloid Leukemia and Beyond Making Scientific Capacity in Africa: An Interdisciplinary Conversation