Yura N. Perov

Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation

We describe an approach to automatic discovery of samplers in the form of human interpretable probabilistic programs. Specifically, we learn the procedure code of samplers for one-dimensional distributions. We formulate a Bayesian approach to this problem by specifying an adaptor grammar prior over probabilistic program code, and use approximate Bayesian computation to learn a program whose execution generates samples that match observed data or analytical characteristics of a distribution of interest. In our experiments we leverage the probabilistic programming system Anglican to perform Markov chain Monte Carlo sampling over the space of programs. Our results are competive relative to state-of-the-art genetic programming methods and demonstrate that we can learn approximate and even exact samplers.

Nonparametric Bayesian models for unsupervised activity recognition and tracking

Human locomotion and activity recognition systems form a critical part in a robots ability to safely and effectively operate in a environment populated with human end users. Previous work in this area relies upon strong assumptions about the labels in the training data; e.g. that are noise-free and that they exist at all. Our approach does not predefine the relevant behaviours or their number, as both are learned directly from observations, similar to real-world human-robot interactions, where labels are neither available. Instead we introduce models that make no assumptions about the state space, by presenting a fully unsupervised nonparametric Bayesian recognition approach, in which we leverage recent advances in state space modelling with automatic inference using probabilistic programming. We demonstrate the utility of full model optimisation using Bayesian optimisation and validate our approach on several challenging problems, using different feature modalities.