no code implementations • ICLR 2022 • Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin Riedmiller
There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches.
1 code implementation • NeurIPS 2021 • Joseph Marino, Alexandre Piché, Alessandro Davide Ialongo, Yisong Yue
Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control, enabling the estimation and sampling of high-value actions.
1 code implementation • 13 Jun 2019 • Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
As we demonstrate in our experiments, the factorisation between latent system states and transition function can lead to a miscalibrated posterior and to learning unnecessarily large noise terms.
no code implementations • 14 Dec 2018 • Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
We focus on variational inference in dynamical systems where the discrete time transition function (or evolution rule) is modelled by a Gaussian process.
no code implementations • 10 Dec 2018 • Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen
We examine an analytic variational inference scheme for the Gaussian Process State Space Model (GPSSM) - a probabilistic model for system identification and time-series modelling.
no code implementations • 26 Jul 2017 • Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.