This article focuses on inferring a general class of hidden Markov models (HMMs) using
data acquired from experts. Expert-acquired data contain decisions/actions made by …

The explosive growth in data volume and the availability of cheap computing resources
have sparked increasing interest in Big learning, an emerging subfield that studies scalable …

We propose a fast inference method for Bayesian nonlinear support vector machines that
leverages stochastic variational inference and inducing points. Our experiments show that …

Predictive inference with Fleming…Viot-driven dependent Dirichlet processes Page 1 Bayesian
Analysis (2021) 16, Number 2, pp. 371–395 Predictive inference with Fleming–Viot-driven …

This paper addresses the Bayesian estimation of the discriminative probabilistic latent
models, especially the mixture models. We develop the max-margin factor analysis (MMFA) …

We extend classic characterisations of posterior distributions under Dirichlet process and
gamma random measures priors to a dynamic framework. We consider the problem of …

Infinite SVM (iSVM) is a Dirichlet process (DP) mixture of large-margin classifiers. Though
flexible in learning nonlinear classifiers and discovering latent clustering structures, iSVM …

We develop a new framework for training hidden Markov models that balances generative
and discriminative goals. Our approach requires likelihood-based or Bayesian learning to …

Growing server energy consumption is a significant environmental issue, and mitigating it is
a key technological challenge. Application-level energy minimization strategies depend on …

We propose a general framework for discriminative Bayesian nonparametric clustering to
promote the inter-discrimination among the learned clusters in a fully Bayesian …