Neil D. Lawrence

Batch Bayesian Optimization via Local Penalization

2015 · arXiv (Cornell University)

The popularity of Bayesian optimization methods for efficient exploration of parameter spaces has lead to a series of papers applying Gaussian processes as surrogates in the optimization of functions. However, most proposed approaches only allow …

Differentially Private Gaussian Processes

2016 · White Rose Research Online (University of Leeds, The University of Sheffield, University of York)

A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the Differential Privacy framework with the flexibility of Gaussian …

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

2019 · Research Explorer (The University of Manchester)

This paper is concerned with learning and stochastic control in physical systems that contain unknown input signals. These unknown signals are modeled as Gaussian processes (GP) with certain parameterized covariance structures. The resulting latent force …

Modeling the Machine Learning Multiverse

2022 · arXiv (Cornell University)

Amid mounting concern about the reliability and credibility of machine learning research, we present a principled framework for making robust and generalizable claims: the multiverse analysis. Our framework builds upon the multiverse analysis (Steegen et …