Philipp Hennig

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 …

Follow the Signs for Robust Stochastic Optimization.

2017 · arXiv (Cornell University)

Stochastic noise on gradients is now a common feature in machine learning. It complicates the design of optimization algorithms, and its effect can be unintuitive: We show that in some settings, particularly those of low …

A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization

2021 · arXiv (Cornell University)

Machine learning practitioners invest significant manual and computational resources in finding suitable learning rates for optimization algorithms. We provide a probabilistic motivation, in terms of Gaussian inference, for popular stochastic first-order methods. As an important …

Classified Regression for Bayesian Optimization: Robot Learning with\n Unknown Penalties

2019 · arXiv (Cornell University)

Learning robot controllers by minimizing a black-box objective cost using\nBayesian optimization (BO) can be time-consuming and challenging. It is very\noften the case that some roll-outs result in failure behaviors, causing\npremature experiment detention. In such cases, …

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers

2022 · arXiv (Cornell University)

Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical processes such as heat transfer, electromagnetism, and wave propagation. In practice, specialized numerical methods based on discretization are used …