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ORCIDAdam D. Cobb
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2020 – today
- 2024
[c16]Adam D. Cobb, Brian Matejek, Daniel Elenius, Anirban Roy, Susmit Jha:
Direct Amortized Likelihood Ratio Estimation. AAAI 2024: 20362-20369- [i22]Adam D. Cobb, Atilim Günes Baydin, Barak A. Pearlmutter, Susmit Jha:
Second-Order Forward-Mode Automatic Differentiation for Optimization. CoRR abs/2408.10419 (2024) - [i21]Brian Matejek, Daniel Elenius, Cale Gentry, David Stoker, Adam D. Cobb:
Resource-Constrained Heuristic for Max-SAT. CoRR abs/2410.09173 (2024) - [i20]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:
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI. CoRR abs/2411.02381 (2024) - 2023
- [j2]Samuel Kessler
, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts:
On Sequential Bayesian Inference for Continual Learning. Entropy 25(6): 884 (2023) - [j1]Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian:
Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo. Mach. Learn. 112(8): 2791-2819 (2023) - [c15]Adam D. Cobb:
hamiltorch: A PyTorch-based library for Hamiltonian Monte Carlo. CPS-IoT Week Workshops 2023: 114-115 - [c14]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: 156-161 - [i19]Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts:
On Sequential Bayesian Inference for Continual Learning. CoRR abs/2301.01828 (2023) - [i18]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 abs/2306.05562 (2023) - [i17]Adam D. Cobb, Brian Matejek, Daniel Elenius, Anirban Roy, Susmit Jha:
Direct Amortized Likelihood Ratio Estimation. CoRR abs/2311.10571 (2023) - 2022
- [c13]Edmond Cunningham, Adam D. Cobb, Susmit Jha:
Principal Component Flows. ICML 2022: 4492-4519 - [c12]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 - [d2]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, Gerard Killeen, Dickson Msaky, Emmanuel Kaindoa, Kathy Willis, Steve J. Roberts:
The HumBug Challenge: ComParE 2022. Version 0.0.2. Zenodo, 2022 [all versions]
- [i16]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 abs/2202.03770 (2022) - [i15]Edmond Cunningham, Adam D. Cobb, Susmit Jha:
Principal Manifold Flows. CoRR abs/2202.07037 (2022) - [i14]Adam D. Cobb, Anirban Roy, Daniel Elenius, Susmit Jha:
Design of Unmanned Air Vehicles Using Transformer Surrogate Models. CoRR abs/2211.08138 (2022) - 2021
- [c11]Subhodip Biswas, Debanjan Saha, Shuvodeep De, Adam D. Cobb, Swagatam Das, Brian Jalaian:
Improving Differential Evolution through Bayesian Hyperparameter Optimization. CEC 2021: 832-840 - [c10]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 - [c9]Ivan Kiskin, Adam D. Cobb, Marianne Sinka, Kathy Willis, Stephen J. Roberts:
Automatic Acoustic Mosquito Tagging with Bayesian Neural Networks. ECML/PKDD (4) 2021: 351-366 - [c8]Adam D. Cobb, Brian Jalaian:
Scaling Hamiltonian Monte Carlo inference for Bayesian neural networks with symmetric splitting. UAI 2021: 675-685 - [c7]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: 1-12 - [d1]Ivan Kiskin, Lawrence Wang, Marianne Sinka, Adam D. Cobb, Benjamin Gutteridge, Davide Zilli, Waqas Rafique, Rinita Dam, Theodoros Marinos, Yunpeng Li, Gerard Killeen, Dickson Msaky, Emmanuel Kaindoa, Kathy Willis, Steve J. Roberts:
HumBugDB: a large-scale acoustic mosquito dataset. Version 0.0.1. Zenodo, 2021 [all versions] - [i13]Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian:
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo. CoRR abs/2107.07211 (2021) - [i12]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 abs/2110.07607 (2021) - 2020
- [b1]Adam D. Cobb:
The practicalities of scaling Bayesian neural networks to real-world applications. University of Oxford, UK, 2020 - [c6]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: 82-91 - [c5]Ivan Kiskin, Adam D. Cobb, Lawrence Wang, Stephen Roberts:
Humbug Zooniverse: A Crowd-Sourced Acoustic Mosquito Dataset. ICASSP 2020: 916-920 - [c4]Binxin Ru, Adam D. Cobb, Arno Blaas, Yarin Gal:
BayesOpt Adversarial Attack. ICLR 2020 - [i11]Ivan Kiskin, Adam D. Cobb, Lawrence Wang, Stephen Roberts:
HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset. CoRR abs/2001.04733 (2020) - [i10]Meet P. Vadera, Adam D. Cobb, Brian Jalaian, Benjamin M. Marlin:
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks. CoRR abs/2007.04466 (2020) - [i9]Adam D. Cobb, Brian Jalaian:
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting. CoRR abs/2010.06772 (2020) - [i8]Subhodip Biswas, Adam D. Cobb, Andreea Sistrunk, Naren Ramakrishnan, Brian Jalaian:
Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization. CoRR abs/2012.06453 (2020)
2010 – 2019
- 2019
- [c3]Richard Everett, Adam D. Cobb, Andrew Markham, Stephen J. Roberts:
Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents. AAMAS 2019: 1943-1945 - [i7]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 abs/1905.10659 (2019) - [i6]Adam D. Cobb, Atilim Günes Baydin, Andrew Markham, Stephen J. Roberts:
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo. CoRR abs/1910.06243 (2019) - 2018
- [c2]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: 1254-1262 - [c1]Arno Blaas, Adam D. Cobb, Jan-Peter Calliess, Stephen J. Roberts:
Scalable Bounding of Predictive Uncertainty in Regression Problems with SLAC. SUM 2018: 373-379 - [i5]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 abs/1802.10446 (2018) - [i4]Adam D. Cobb, Stephen J. Roberts, Yarin Gal:
Loss-Calibrated Approximate Inference in Bayesian Neural Networks. CoRR abs/1805.03901 (2018) - [i3]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 abs/1811.03390 (2018) - [i2]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 abs/1812.04994 (2018) - 2017
- [i1]Adam D. Cobb, Andrew Markham, Stephen J. Roberts:
Learning from lions: inferring the utility of agents from their trajectories. CoRR abs/1709.02357 (2017)
Coauthor Index
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