Adam Cobb PhD Student in Machine Learning

Academic Work


An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
“Adam D. Cobb, Michael D. Himes, Frank Soboczenski, Simone Zorzan, Molly D. O’Beirne, Atılım Güneş Baydin, Yarin Gal, Shawn D. Domagal-Goldman, Giada N. Arney, Daniel Angerhausen”
To appear in The Astronomical Journal.

Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents
“Richard Everett, Adam Cobb, Andrew Markham, Stephen Roberts”
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems.

Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer’s disease severity
“Wolfgang Fruehwirt, Adam D. Cobb, Martin Mairhofer, Leonard Weydemann, Heinrich Garn, Reinhold Schmidt, Thomas Benke, Peter Dal-Bianco, Gerhard Ransmayr, Markus Waser, Dieter Grossegger, Pengfei Zhang, Georg Dorffner, Stephen Roberts”
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018.

Bayesian Deep Learning for Exoplanet Atmospheric Retrieval
“Frank Soboczenski, Michael D. Himes, Molly D. O’Beirne, Simone Zorzan, Atılım Güneş Baydin, Adam D. Cobb, Yarin Gal, Daniel Angerhausen, Massimo Mascaro, Giada N. Arney, Shawn D. Domagal-Goldman”
Third workshop on Bayesian Deep Learning (NeurIPS 2018), Montreal, Canada.

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


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.