Bayesian experimental design (BED) provides a powerful and general framework for
optimizing the design of experiments. However, its deployment often poses substantial …

Markov chain Monte Carlo methods are often deemed too computationally intensive to be of
any practical use for big data applications, and in particular for inference on datasets …

Flow-based generative models parameterize probability distributions through an invertible
transformation and can be trained by maximum likelihood. Invertible residual networks …

Under suitable assumptions, the quantum-phase-estimation (QPE) algorithm is able to
achieve Heisenberg-limited precision scaling in estimating the ground-state energy …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

Monte Carlo methods are a very general and useful approach for the estimation of
expectations arising from stochastic simulation. However, they can be computationally …

Uncertainty quantification (UQ) includes the characterization, integration, and propagation of
uncertainties that result from stochastic variations and a lack of knowledge or data in the …

Markov chain Monte Carlo algorithms are used to simulate from complex statistical
distributions by way of a local exploration of these distributions. This local feature avoids …

We study the safe reinforcement learning problem with nonlinear function approximation,
where policy optimization is formulated as a constrained optimization problem with both the …

We show that normalising flows become pathological when used to model targets whose
supports have complicated topologies. In this scenario, we prove that a flow must become …