Applications of time-series generative models and inference techniques

In this dissertation, we apply deep generative modelling, amortised inference and reinforcement learning methods to real-world, practical phenomenon, and we ask if these techniques can be used to predict complex system dynamics, model biologically plausible behaviour, and guide decision making. In the past, probabilistic modelling and Bayesian inference techniques have been successfully applied in a wide array of fields, achieving success in financial market prediction, robotics, and the natural sciences. However, the use of generative models in these contexts has usually required a rigid set of linearity constraints or assumptions about the distributions used for modelling. Furthermore, inference in non-linear models can be very difficult to scale to high-dimensional models. In recent years, deep learning has been a key innovation in enabling non-linear function approximation. When applied to probabilistic modelling, deep non-linear models have significantly improved the generative capabilities of computer vision models. While an important step towards general artificial intelligence, there remains a gap between the successes of these early single-time-step deep generative models and the temporal models that will be required to deploy machine learning in the real-world.
We posit that deep non-linear time-series models and sequential inference are useful in a number of these complex domains. In order to test this hypothesis, we made methodological developments related to model learning and approximate inference. We then present experimental results, which address several questions about the application of deep generative models.
First, can we train a deep temporal model learning complex dynamics to perform sufficiently accurate inference and predictions at run-time. Here,
sufficient accuracy'' means that the predictions and inferences made using our model lead to stronger performance than that given by a heuristic approach on some downstream task performed in real-time. We specifically model large compute cluster hardware performance using a deep generative model in order to use the model to tackle the downstream task of improving the overall throughput of the cluster. Generally, this question is useful to answer for a number of wider applications similar to ours which may use such modelling techniques to intervene in real-time. For example, we may be interested in applying generative modelling and inference to come up with better trading algorithms with the goal of increasing returns. We may also wish to use a deep generative epidemiology model to determine government policies that help prevent the spread of disease. Simply put, we want to ask the question, "are deep generative models powerful enough to be useful?"
Next, are deep state-space models important for the generative quality of animal-like behaviour? Given a perceptual dataset of animal behaviour, such as camera views of fruit-flies interactions or collections of human handwriting samples, can a deep generative model capture the latent variability underlying such behaviour. As a step towards artificial intelligence that mirrors human and other biological organisms, we must assess whether deep generative modelling is a viable approach to capture what may be one of the most stochastic and challenging phenomenon to model.
Finally, is inference a useful perspective in decision making and reinforcement learning? If so, can we improve the uncertainty estimation of different quantities used in classic reinforcement learning to further take advantage of an inference perspective? Answering these questions may help us determine if a
Reinforcement Learning as Inference'' framework coupled with a distributional estimate of the sum of future rewards can lead to better decision making under the control setting.
Although our findings are positive in terms of these questions, they come with caveats for each. First, deep generative models must be accurate to be useful for downstream tasks. Second, modelling biologically plausible behaviour is difficult without additional partial supervision in the latent space. Third, while we have made orthogonal progress in using the inference perspective for policy learning and leveraging a distributional estimate in reinforcement learning, it remains unclear how to best combine these two approaches. This thesis presents the progress made in tackling these challenges.

Keywords: generative modeling
reinforcement learning
amortised inference
control as inference
probabilistic machine learning
variational auto-encoders
deep learning
time-series modeling
Advisor: Wood, F