Learning from lions: inferring the utility of agents from their trajectories. - Adam D. Cobb; Andrew Markham; Stephen J. Roberts (CoRR, 2017)

Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus. - Adam D. Cobb; Richard Everett 001; Andrew Markham; Stephen J. Roberts (KDD, 2018)

Scalable Bounding of Predictive Uncertainty in Regression Problems with SLAC. - Arno Blaas; Adam D. Cobb; Jan-Peter Calliess; Stephen J. Roberts (SUM, 2018)

Loss-Calibrated Approximate Inference in Bayesian Neural Networks. - Adam D. Cobb; Stephen J. Roberts; Yarin Gal (CoRR, 2018)

Bayesian Deep Learning for Exoplanet Atmospheric Retrieval. - 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 (CoRR, 2018)

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 001; et al. (CoRR, 2018)

Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents. - Richard Everett 001; Adam D. Cobb; Andrew Markham; Stephen J. Roberts (AAMAS, 2019)

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval. - 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 (CoRR, 2019)

Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo. - Adam D. Cobb; Atilim Günes Baydin; Andrew Markham; Stephen J. Roberts (CoRR, 2019)

On Uncertainty and Robustness in Large-Scale Intelligent Data Fusion Systems. - 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; et al. (CogMI, 2020)

Humbug Zooniverse: A Crowd-Sourced Acoustic Mosquito Dataset. - Ivan Kiskin; Adam D. Cobb; Lawrence Wang; Stephen Roberts 001 (ICASSP, 2020)

BayesOpt Adversarial Attack. - Binxin Ru; Adam D. Cobb; Arno Blaas; Yarin Gal (ICLR, 2020)

URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks. - Meet P. Vadera; Adam D. Cobb; Brian Jalaian; Benjamin M. Marlin (CoRR, 2020)

Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization. - Subhodip Biswas; Adam D. Cobb; Andreea Sistrunk; Naren Ramakrishnan; Brian Jalaian (CoRR, 2020)

The practicalities of scaling Bayesian neural networks to real-world applications. - Adam D. Cobb (2020)

Improving Differential Evolution through Bayesian Hyperparameter Optimization. - Subhodip Biswas; Debanjan Saha; Shuvodeep De; Adam D. Cobb; Swagatam Das; Brian Jalaian (CEC, 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 001; Dickson Msaky; Emmanuel Kaindoa; et al. (NeurIPS Datasets and Benchmarks, 2021)

Automatic Acoustic Mosquito Tagging with Bayesian Neural Networks. - Ivan Kiskin; Adam D. Cobb; Marianne Sinka; Kathy Willis; Stephen J. Roberts (ECML/PKDD (4), 2021)

Scaling Hamiltonian Monte Carlo inference for Bayesian neural networks with symmetric splitting. - Adam D. Cobb; Brian Jalaian (UAI, 2021)

Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks. - Adam D. Cobb; Brian Jalaian; Nathaniel D. Bastian; Stephen Russell 001 (WSC, 2021)

HumBugDB: a large-scale acoustic mosquito dataset. - Ivan Kiskin; Lawrence Wang; Marianne Sinka; Adam D. Cobb; Benjamin Gutteridge; Davide Zilli; Waqas Rafique; Rinita Dam; Theodoros Marinos; Yunpeng Li 001; Gerard Killeen; Dickson Msaky; et al. (Zenodo, 2021)

Principal Component Flows. - Edmond Cunningham; Adam D. Cobb; Susmit Jha (ICML, 2022)

URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference methods. - Meet P. Vadera; Jinyang Li 004; Adam D. Cobb; Brian Jalaian; Tarek F. Abdelzaher; Benjamin M. Marlin (MLSys, 2022)

Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning. - Meet P. Vadera; Adam D. Cobb; Brian Jalaian; Benjamin M. Marlin (CoRR, 2022)

Principal Manifold Flows. - Edmond Cunningham; Adam D. Cobb; Susmit Jha (CoRR, 2022)

Design of Unmanned Air Vehicles Using Transformer Surrogate Models. - Adam D. Cobb; Anirban Roy; Daniel Elenius; Susmit Jha (CoRR, 2022)

The HumBug Challenge: ComParE 2022. - Ivan Kiskin; Lawrence Wang; Marianne Sinka; Adam D. Cobb; Benjamin Gutteridge; Davide Zilli; Alexandru Constantin; Andreas Pentaliotis; Waqas Rafique; Rinita Dam; Theodoros Marinos; Yunpeng Li 001; et al. (Zenodo, 2022)

hamiltorch: A PyTorch-based library for Hamiltonian Monte Carlo. - Adam D. Cobb (CPS-IoT Week Workshops, 2023)

Challenges and Opportunities in Neuro-Symbolic Composition of Foundation Models. - Susmit Jha; Anirban Roy; Adam D. Cobb; Alexander M. Berenbeim; Nathaniel D. Bastian (MILCOM, 2023)

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)

Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo. - Vyacheslav Kungurtsev; Adam D. Cobb; Tara Javidi; Brian Jalaian (Mach. Learn., 2023)

On Sequential Bayesian Inference for Continual Learning. - Samuel Kessler; Adam D. Cobb; Tim G. J. Rudner; Stefan Zohren; Stephen J. Roberts (Entropy, 2023)

Direct Amortized Likelihood Ratio Estimation. - Adam D. Cobb; Brian Matejek; Daniel Elenius; Anirban Roy; Susmit Jha (AAAI, 2024)

Post-hoc Uncertainty Quantification for Neurosymbolic Artificial Intelligence. - Alexander M. Berenbeim; Ramneet Kaur; Adam D. Cobb; Anirban Roy; Susmit Jha; Nathaniel D. Bastian (MILCOM, 2024)

Second-Order Forward-Mode Automatic Differentiation for Optimization. - Adam D. Cobb; Atilim Günes Baydin; Barak A. Pearlmutter; Susmit Jha (CoRR, 2024)

Resource-Constrained Heuristic for Max-SAT. - Brian Matejek; Daniel Elenius; Cale Gentry; David Stoker; Adam D. Cobb (CoRR, 2024)

Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI. - Ramneet Kaur; Colin Samplawski; Adam D. Cobb; Anirban Roy; Brian Matejek; Manoj Acharya; Daniel Elenius; Alexander M. Berenbeim; John A. Pavlik; Nathaniel D. Bastian; Susmit Jha (CoRR, 2024)

Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference. - Colin Samplawski; Adam D. Cobb; Manoj Acharya; Ramneet Kaur; Susmit Jha (UAI, 2025)

Zero-Shot Detection of Out-of-Context Objects Using Foundation Models. - Anirban Roy; Adam D. Cobb; Ramneet Kaur; Sumit Jha 001; Nathaniel D. Bastian; Alexander M. Berenbeim; Robert H. Thomson 001; Iain Cruickshank; Alvaro Velasquez; Susmit Jha (WACV, 2025)

AGENT: An Aerial Vehicle Generation and Design Tool Using Large Language Models. - Colin Samplawski; Adam D. Cobb; Susmit Jha (CoRR, 2025)

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding. - Trilok Padhi; Ramneet Kaur; Adam D. Cobb; Manoj Acharya; Anirban Roy; Colin Samplawski; Brian Matejek; Alexander M. Berenbeim; Nathaniel D. Bastian; Susmit Jha (CoRR, 2025)

Backpropagation-Free Metropolis-Adjusted Langevin Algorithm. - Adam D. Cobb; Susmit Jha (CoRR, 2025)