Adam D. Cobb

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 36

Top Topics

Top Venues

Publications

Adam D. Cobb, Brian Matejek, Daniel Elenius, Anirban Roy, Susmit Jha
Direct Amortized Likelihood Ratio Estimation. AAAI (2024)
Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts
On Sequential Bayesian Inference for Continual Learning. CoRR (2023)
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
AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs. CoRR (2023)
Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian
Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo. Mach. Learn. 112 (8) (2023)
Adam D. Cobb, Brian Matejek, Daniel Elenius, Anirban Roy, Susmit Jha
Direct Amortized Likelihood Ratio Estimation. CoRR (2023)
Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts
On Sequential Bayesian Inference for Continual Learning. Entropy 25 (6) (2023)
Adam D. Cobb
hamiltorch: A PyTorch-based library for Hamiltonian Monte Carlo. CPS-IoT Week Workshops (2023)
Susmit Jha, Anirban Roy, Adam D. Cobb, Alexander M. Berenbeim, Nathaniel D. Bastian
Challenges and Opportunities in Neuro-Symbolic Composition of Foundation Models. MILCOM (2023)
Meet P. Vadera, Adam D. Cobb, Brian Jalaian, Benjamin M. Marlin
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning. CoRR (2022)
Adam D. Cobb, Anirban Roy, Daniel Elenius, Susmit Jha
Design of Unmanned Air Vehicles Using Transformer Surrogate Models. CoRR (2022)
Edmond Cunningham, Adam D. Cobb, Susmit Jha
Principal Component Flows. ICML (2022)
Meet P. Vadera, Jinyang Li, Adam D. Cobb, Brian Jalaian, Tarek F. Abdelzaher, Benjamin M. Marlin
URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference methods. MLSys (2022)
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 Willis, Stephen J. Roberts
HumBugDB: A Large-scale Acoustic Mosquito Dataset. NeurIPS Datasets and Benchmarks (2021)
Subhodip Biswas, Debanjan Saha, Shuvodeep De, Adam D. Cobb, Swagatam Das, Brian Jalaian
Improving Differential Evolution through Bayesian Hyperparameter Optimization. CEC (2021)
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
HumBugDB: A Large-scale Acoustic Mosquito Dataset. CoRR (2021)
Adam D. Cobb, Brian Jalaian
Scaling Hamiltonian Monte Carlo inference for Bayesian neural networks with symmetric splitting. UAI (2021)
Ivan Kiskin, Adam D. Cobb, Marianne Sinka, Kathy Willis, Stephen J. Roberts
Automatic Acoustic Mosquito Tagging with Bayesian Neural Networks. ECML/PKDD (4) (2021)
Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo. CoRR (2021)
Adam D. Cobb, Brian Jalaian, Nathaniel D. Bastian, Stephen Russell
Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks. WSC (2021)
Ivan Kiskin, Adam D. Cobb, Lawrence Wang, Stephen Roberts
Humbug Zooniverse: A Crowd-Sourced Acoustic Mosquito Dataset. ICASSP (2020)
Benjamin M. Marlin, Tarek F. Abdelzaher, Gabriela F. Ciocarlie, Adam D. Cobb, Mark Dennison, Brian Jalaian, Lance M. Kaplan, Tiffany Raber, Adrienne Raglin, Piyush K. Sharma, Mani B. Srivastava, Theron Trout, Meet P. Vadera, Maggie B. Wigness
On Uncertainty and Robustness in Large-Scale Intelligent Data Fusion Systems. CogMI (2020)
Binxin Ru, Adam D. Cobb, Arno Blaas, Yarin Gal
BayesOpt Adversarial Attack. ICLR (2020)
Adam D. Cobb, Brian Jalaian
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting. CoRR (2020)
Subhodip Biswas, Adam D. Cobb, Andreea Sistrunk, Naren Ramakrishnan, Brian Jalaian
Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization. CoRR (2020)
Ivan Kiskin, Adam D. Cobb, Lawrence Wang, Stephen Roberts
HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset. CoRR (2020)
Meet P. Vadera, Adam D. Cobb, Brian Jalaian, Benjamin M. Marlin
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks. CoRR (2020)
Adam D. Cobb, Michael D. Himes, Frank Soboczenski, Simone Zorzan, Molly D. O'Beirne, Atilim Günes Baydin, Yarin Gal, Shawn D. Domagal-Goldman, Giada N. Arney, Daniel Angerhausen
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval. CoRR (2019)
Richard Everett, Adam D. Cobb, Andrew Markham, Stephen J. Roberts
Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents. AAMAS (2019)
Adam D. Cobb, Atilim Günes Baydin, Andrew Markham, Stephen J. Roberts
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo. CoRR (2019)
Arno Blaas, Adam D. Cobb, Jan-Peter Calliess, Stephen J. Roberts
Scalable Bounding of Predictive Uncertainty in Regression Problems with SLAC. SUM (2018)
Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus. CoRR (2018)
Frank Soboczenski, Michael D. Himes, Molly D. O'Beirne, Simone Zorzan, Atilim Gunes Baydin, Adam D. Cobb, Daniel Angerhausen, Giada N. Arney, Shawn D. Domagal-Goldman
Bayesian Deep Learning for Exoplanet Atmospheric Retrieval. CoRR (2018)
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 J. Roberts
Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity. CoRR (2018)
Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus. KDD (2018)
Adam D. Cobb, Stephen J. Roberts, Yarin Gal
Loss-Calibrated Approximate Inference in Bayesian Neural Networks. CoRR (2018)
Adam D. Cobb, Andrew Markham, Stephen J. Roberts
Learning from lions: inferring the utility of agents from their trajectories. CoRR (2017)