bayesopt github.io

BayesOpt 2014

NIPS Workshop on Bayesian Optimization. December 12, 2014. Bayesian Optimization in Academia and Industry. How to tackle high-dimensional data? How to make Bayesian optimization work in massive, distributed systems? What kind of structural assumptions are we able to make? And finally, what can we say about these questions both empirically and theoretically? Invited speakers and panelists. Ryan P. Adams. Matthew W. Hoffman.
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LINKS TO BAYESOPT.GITHUB.IO

Harvard Intelligent Probabilistic Systems

School of Engineering and Applied Sciences. Harvard School of Engineering and Applied Sciences. In the HIPS group, we are interested in building intelligent algorithms. What makes a system intelligent? Discovering Structure with Bayesian Nonparametrics. A perpetual challenge in statistical modelling is trying to find the parsimonious complexity. Point Processes as Primitives of Computation. Friday, December 11th, 2015.

Cambridge Machine Learning Group Homepage

MLG member Adrian Weller has been appointed as the Programme Director for Artificial Intelligence at The Alan Turing Institute. More details can be found here. Several papers from MLG have won prizes at.

Probabilistic Numerics.org

This page serves as a communication centre. For a community of researchers working on. Methods that assign probability distributions. To the unknown result of deterministic computations. Tübingen and Michael A Osborne,. Taking this observation seriously, a probabilistic numerical method is a numerical algorithm that takes in a probability distribution over its inputs, and returns a probability distribution over its output.

Javier González

I am a Senior Machine Learning Scientist at Amazon, Cambridge. I am interested in probabilistic modelling techniques, such as Gaussian processes, that allow to quantify uncertainly and improve the data efficiency of autonomous systems. Poseidon House, Castle Park, Cambridge CB3 0RD. Sheffield, UK, 17 Septembe.

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BayesOpt 2014

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NIPS Workshop on Bayesian Optimization. December 12, 2014. Bayesian Optimization in Academia and Industry. How to tackle high-dimensional data? How to make Bayesian optimization work in massive, distributed systems? What kind of structural assumptions are we able to make? And finally, what can we say about these questions both empirically and theoretically? Invited speakers and panelists. Ryan P. Adams. Matthew W. Hoffman.

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