Adam Cobb PhD Student in Machine Learning

Academic Work


Papers

Scalable Bounding of Predictive Uncertainty in Regression Problems with SLAC
“Arno Blaas, Adam D. Cobb, Jan-Peter Calliess, Stephen J. Roberts
International Conference on Scalable Uncertainty Management, Sep 2018.

Loss-Calibrated Approximate Inference in Bayesian Neural Networks
“Adam D. Cobb, Stephen J. Roberts, Yarin Gal
Theory of deep learning workshop, ICML, May 2018. Code

Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
“Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts
Accepted KDD 2018. Code Video

Learning from lions: inferring the utility of agents from their trajectories
“Adam D. Cobb, Andrew Markham, Stephen J. Roberts
September 2017. Paper

Adaptive sampling of lion accelerometer data
“Adam D. Cobb, Andrew Markham
September 2016. CDT in AIMS mini-project

Active sampling to increase the battery life of mosquito-detecting sensor networks
“Adam D. Cobb, Stephen J. Roberts
June 2016. CDT in AIMS mini-project

Exoplanet Detection in Large Astronomical Data Sets
“Adam D. Cobb, Stephen J. Roberts
June 2015. Master’s Thesis

Presentations

Our final presentation on modelling the 3D shapes of asteroids during NASA FDL 2017.

Additional Paper Materials

A high level video summarising our paper Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus. Submitted to KDD 2018 video competition.