214 citations · 1.8k across the 88 of their papers we have counts for
4 papers · 1 filter
Dual Control for Approximate Bayesian Reinforcement Learning
Edgar D. Klenske, Philipp Hennig
Control of non-episodic, finite-horizon dynamical systems with uncertain dynamics poses a tough and elementary case of the exploration-exploitation trade-off. Bayesian reinforcemen…
Probabilistic Numerics and Uncertainty in Computations
Philipp Hennig, Michael A Osborne, Mark Girolami
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equation…
Batch Bayesian Optimization via Local Penalization
Javier González, Zhenwen Dai, Philipp Hennig +1
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 optimi…
Probabilistic Line Searches for Stochastic Optimization
Maren Mahsereci, Philipp Hennig
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far b…