Michael Pearce

JMLR Journal 2022 Journal Article

A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review

• Michael Pearce
• Elena A. Erosheva

Rankings and scores are two common data types used by judges to express preferences and/or perceptions of quality in a collection of objects. Numerous models exist to study data of each type separately, but no unified statistical model captures both data types simultaneously without first performing data conversion. We propose the Mallows-Binomial model to close this gap, which combines a Mallows $\phi$ ranking model with Binomial score models through shared parameters that quantify object quality, a consensus ranking, and the level of consensus among judges. We propose an efficient tree-search algorithm to calculate the exact MLE of model parameters, study statistical properties of the model both analytically and through simulation, and apply our model to real data from an instance of grant panel review that collected both scores and partial rankings. Furthermore, we demonstrate how model outputs can be used to rank objects with confidence. The proposed model is shown to sensibly combine information from both scores and rankings to quantify object quality and measure consensus with appropriate levels of statistical uncertainty. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

NeurIPS Conference 2019 Conference Paper

Scalable Global Optimization via Local Bayesian Optimization

• David Eriksson
• Michael Pearce
• Jacob Gardner
• Ryan Turner
• Matthias Poloczek

Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional problems with several thousand observations remains challenging, and on difficult problems Bayesian optimization is often not competitive with other paradigms. In this paper we take the view that this is due to the implicit homogeneity of the global probabilistic models and an overemphasized exploration that results from global acquisition. This motivates the design of a local probabilistic approach for global optimization of large-scale high-dimensional problems. We propose the TuRBO algorithm that fits a collection of local models and performs a principled global allocation of samples across these models via an implicit bandit approach. A comprehensive evaluation demonstrates that TuRBO outperforms state-of-the-art methods from machine learning and operations research on problems spanning reinforcement learning, robotics, and the natural sciences.

IROS Conference 1992 Conference Paper

The Learning Of Reactive Control Parameters Through Genetic Algorithms

• Michael Pearce
• Ronald C. Arkin
• Ashwin Ram 0001