As experimental tools in the physical and life sciences become increasingly sophisticated and costly, there is a need to optimize the choice of experimental parameters to maximize the informativeness of the data and minimize cost. When designing a …
Inverse reinforcement learning (IRL) methods attempt to recover the reward function of an agent by observing its behavior. Given the large amount of uncertainty in the underlying reward function, it is often useful to model this function …
Slice sampling is a Markov chain Monte Carlo algorithm for simulating samples from probability distributions, with the convenient property that it is rejection-free. When the slice endpoints are known, the sampling path is a deterministic function of …
Scientists and engineers are often interested in learning the number of subpopulations (or components) present in a data set. Data science folk wisdom tells us that a finite mixture model (FMM) with a prior on the number of components will fail to …
Meta-learning leverages related source tasks to learn an initialization that can be quickly fine-tuned to a target task with limited labeled examples. However, many popular meta-learning algorithms, such as model-agnostic meta-learning (MAML), only …