The Practicalities of Scaling Bayesian Neural Networks to Real-World Applications
“Adam D. Cobb”
University of Oxford 2020.
AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs
“Adam D. Cobb, Anirban Roy, Daniel Elenius, F. Michael Heim, Brian Swenson, Sydney Whittington, James D. Walker, Theodore Bapty, Joseph Hite, Karthik Ramani, Christopher McComb, Susmit Jha”
NeurIPS 2023.
Principal Component Flows
“Edmond Cunningham, Adam D. Cobb, Susmit Jha”
ICML 2022.
On Diverse System-Level Design Using Manifold Learning and Partial Simulated Annealing
“Adam D. Cobb, Anirban Roy, Daniel Elenius, Kaushik Koneripalli, Susmit Jha”
Proceedings of the Design Society (DESIGN) 2022.
Paper was in top 10 % of accepted conference papers.
URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference methods
“Meet Vadera, Jinyang Li, Adam D. Cobb, Brian Jalaian, Tarek Abdelzaher, Benjamin Marlin”
Proceedings of Machine Learning and Systems (MLSys) 2022.
Can Sequential Bayesian Inference Solve Continual Learning?
“Samuel Kessler, Adam D. Cobb, Stefan Zohren, Stephen J. Roberts”
Fourth Symposium on Advances in Approximate Bayesian Inference 2022.
Robust decision-making in the internet of battlefield things using Bayesian neural networks
“Adam D. Cobb, Brian A. Jalaian, Nathaniel D. Bastian, Stephen Russell”
Winter Simulation Conference (WSC) 2021.
HumBugDB: A Large-scale Acoustic Mosquito Dataset
“Ivan Kiskin, Marianne Sinka, Adam D Cobb, Waqas Rafique, Lawrence Wang, Davide Zilli, Benjamin Gutteridge, Rinita Dam, Theodoros Marinos, Yunpeng Li, Dickson Msaky, Emmanuel Kaindoa, Gerard Killeen, Eva Herreros-Moya, Kathy J Willis, Stephen J Roberts”
Neural Information Processing Systems Track on Datasets and Benchmarks 2021.
Improving differential evolution through Bayesian hyperparameter optimization
“Subhodip Biswas, Debanjan Saha, Shuvodeep De, Adam D Cobb, Swagatam Das, Brian Jalaian”
IEEE Congress on Evolutionary Computation (CEC) 2021.
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting
“Adam D. Cobb, Brian Jalaian”
UAI 2021.
BayesOpt Adversarial Attack
“Binxin Ru, Adam D. Cobb, Arno Blaas, Yarin Gal”
ICLR 2020.
Semi-separable Hamiltonian Monte Carlo for inference in Bayesian neural networks
“Adam D. Cobb, Atılım Güneş Baydin, Ivan Kiskin, Andrew Markham, Stephen J. Roberts”
Fourth workshop on Bayesian Deep Learning (NeurIPS 2019), Vancouver, Canada.
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo
“Adam D. Cobb, Atılım Güneş Baydin, Andrew Markham, Stephen J. Roberts”
Under review.
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”
The Astronomical Journal, 2019.
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
Toward Safe Decision-Making via Uncertainty Quantification in Machine Learning
“Adam D. Cobb, Brian Jalaian, Nathaniel D. Bastian, Stephen Russell”
Systems Engineering and Artificial Intelligence 2021.
Our final presentation on modelling the 3D shapes of asteroids during NASA FDL 2017.
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.