Yutian Chen
now a Staff Research Scientist, Google DeepMind
Publications
Variational Gaussian Process State-Space Models
Roger Frigola, Yutian Chen, Carl Edward Rasmussen, 2014. (In Advances in Neural Information Processing Systems 27). Edited by Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger.
Abstract▼ URL
State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dynamical systems. In comparison to conventional parametric models, we offer the possibility to straightforwardly trade off model capacity and computational cost whilst avoiding overfitting. Our main algorithm uses a hybrid inference approach combining variational Bayes and sequential Monte Carlo. We also present stochastic variational inference and online learning approaches for fast learning with long time series.
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Yarin Gal, Yutian Chen, Zoubin Ghahramani, 2015. (In Proceedings of the 32nd International Conference on Machine Learning (ICML-15)).
Abstract▼ URL
Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of possible observations grows exponentially with vector length, but dataset diversity might be poor in comparison. Recent models have gained significant improvement in supervised tasks with this data. These models embed observations in a continuous space to capture similarities between them. Building on these ideas we propose a Bayesian model for the unsupervised task of distribution estimation of multivariate categorical data. We model vectors of categorical variables as generated from a non-linear transformation of a continuous latent space. Non-linearity captures multi-modality in the distribution. The continuous representation addresses sparsity. Our model ties together many existing models, linking the linear categorical latent Gaussian model, the Gaussian process latent variable model, and Gaussian process classification. We derive inference for our model based on recent developments in sampling based variational inference. We show empirically that the model outperforms its linear and discrete counterparts in imputation tasks of sparse data.
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
Anoop Korattikara, Yutian Chen, Max Welling, June 2014. (In 31st International Conference on Machine Learning). Beijing, China.
Abstract▼ URL
Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets? We argue that computing the likelihood for N datapoints in the Metropolis-Hastings (MH) test to reach a single binary decision is computationally inefficient. We introduce an approximate MH rule based on a sequential hypothesis test that allows us to accept or reject samples with high confidence using only a fraction of the data required for the exact MH rule. While this method introduces an asymptotic bias, we show that this bias can be controlled and is more than offset by a decrease in variance due to our ability to draw more samples per unit of time.
Comment: supplementary
Distributed Inference for Dirichlet Process Mixture Models
Hong Ge, Yutian Chen, Moquan Wan, Zoubin Ghahramani, 07–09 Jul 2015. (In Proceedings of the 32nd International Conference on Machine Learning). Edited by Francis Bach, David Blei. Lille, France. PMLR. Proceedings of Machine Learning Research.
Abstract▼ URL
Bayesian nonparametric mixture models based on the Dirichlet process (DP) have been widely used for solving problems like clustering, density estimation and topic modelling. These models make weak assumptions about the underlying process that generated the observed data. Thus, when more data are collected, the complexity of these models can change accordingly. These theoretical properties often lead to superior predictive performance when compared to traditional finite mixture models. However, despite the increasing amount of data available, the application of Bayesian nonparametric mixture models is so far limited to relatively small data sets. In this paper, we propose an efficient distributed inference algorithm for the DP and the HDP mixture model. The proposed method is based on a variant of the slice sampler for DPs. Since this sampler does not involve a pre-determined truncation, the stationary distribution of the sampling algorithm is unbiased. We provide both local thread-level and distributed machine-level parallel implementations and study the performance of this sampler through an extensive set of experiments on image and text data. When compared to existing inference algorithms, the proposed method exhibits state-of-the-art accuracy and strong scalability with up to 512 cores.