University of Sheffield; Facebook; Facebook; The University of Sheffield; University of Cambridge; University of Amsterdam; Princeton University; Amazon Development Center Germany GmbH
University of Cambridge; University of Amsterdam; Princeton University; Amazon Development Center Germany GmbH
Workshop: Probabilistic Models for Big Data
7:30am – 6:30pm Monday, December 09, 2013
Harvey's Emerald Bay A
First Session: “Problem Set Up: Methodological and Practical Issues and current state of the art”
7:45:8:30 Max Welling "Minibatch Based Bayesian Posterior Inference"
08:30-9:15 Zoubin Ghahramani "Scaling up Bayesian Nonparametrics"
9:15-9:30 coffee break
9:30-10:15 David Blei "Stochastic Variational Inference: An Overview and Open Problems"
Bjarne Ø Fruergaard, "Dimensionality reduction for click-through rate prediction: Dense versus sparse representation"
Beyza Ermis "A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction"
Farideh Fazayeli "Uncertainty Quantified Matrix Completion using Bayesian Hierarchical Matrix Factorization"
10:30-15:30 break and posters
Second Session: “Challenges and Where probability Could Help”
15:30-16:15 Yoram Singer "Yet Another Generalization of Accelerated Gradient Ascent "
16:15-16:35 Ralf Herbrich "Practical Problems for Probabilities"
16:35-17:10 Contributed talks:
Anoop Korattikara "Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget"
Matthew Hoffman "Stochastic Structured Mean-Field Variational Inference"
Prem Gopalan "Bayesian Nonparametric Poisson Factorization for Recommendation Systems"
17:10-17:30 coffee break
17:30-18:00 Joaquin Quinonero Candela "Some Lessons from Machine Learning in Practice"
Processing of web scale data sets has proven its worth in a range of applications, from ad-click prediction to large recommender systems. In most cases, learning needs to happen real-time, and the latency allowance for predictions is restrictive. Probabilistic predictions are critical in practice on web applications because optimizing the user experience requires being able to compute the expected utilities of mutually exclusive pieces of content. The quality of the knowledge extracted from the information available is restricted by complexity of the model.
One framework that enables complex modelling of data is probabilistic modelling. However, its applicability to big data is restricted by the difficulties of inference in complex probabilistic models, and by computational constraints.
This workshop will focus on applying probabilistic models to big data. Of interest will be algorithms that allow for inference in probabilistic models for big data such as stochastic variational inference and stochastic Monte Carlo. A particular focus will be on existing applications in big data and future applications that would benefit from such approaches.
This workshop brings together leading academic and industrial researchers in probabilistic modelling and large scale data sets.