Streaming, Distributed Variational Inference for Bayesian Nonparametrics

This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models. In the proposed framework, processing nodes receive a sequence of data minibatches, compute a variational posterior for each, and make asynchronous streaming updates to a central model. In contrast to previous algorithms, the proposed framework is truly streaming, distributed, asynchronous, learning-rate-free, and truncation-free. The key challenge in developing the framework, arising from the fact that BNP models do not impose an inherent ordering on their components, is finding the correspondence between minibatch and central BNP posterior components before performing each update. To address this, the paper develops a combinatorial optimization problem over component correspondences, and provides an efficient solution technique. The paper concludes with an application of the methodology to the DP mixture model, with experimental results demonstrating its practical scalability and performance.Comment: This paper was presented at NIPS 2015. Please use the following BibTeX citation: @inproceedings{Campbell15_NIPS, Author = {Trevor Campbell and Julian Straub and John W. {Fisher III} and Jonathan P. How}, Title = {Streaming, Distributed Variational Inference for Bayesian Nonparametrics}, Booktitle = {Advances in Neural Information Processing Systems (NIPS)}, Year = {2015}

Dynamic Dependency Tests for Audio-Visual Speaker Association

We formulate the problem of audio-visual speaker association as a dynamic dependency test. That is, given an audio stream and multiple video streams, we wish to determine their dependancy structure as it evolves over time. To this end, we propose the use of a hidden factorization Markov model in which the hidden state encodes a finite number of possible dependency structures. Each dependency structure has an explicit semantic meaning, namely “who is speaking. ” This model takes advantage of both structural and parametric changes associated with changes in speaker. This is contrasted with standard sliding window based dependence analysis. Using this model we obtain state-of-the-art performance on an audio-visual association task without benefit of training data. Index Terms — Pattern clustering methods 1

Construction of Dependent Dirichlet Processes Based on Poisson Processes

We present a method for constructing dependent Dirichlet processes. The new approach exploits the intrinsic relationship between Dirichlet and Poisson processes in order to create a Markov chain of Dirichlet processes suitable for use as a prior over evolving mixture models. The method allows for the creation, removal, and location variation of component models over time while maintaining the property that the random measures are marginally DP distributed. Additionally, we derive a Gibbs sampling algorithm for model inference and test it on both synthetic and real data. Empirical results demonstrate that the approach is effective in estimating dynamically varying mixture models