Adam D. Cobb - RankMe

56 papers A* 7A 4B 1Misc 2Journal 32Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Do Diffusion Models Dream of Electric Planes? Discrete and Continuous Simulation-Based Inference for Aircraft Design. CoRR Aurelien Ghiglino, Daniel Elenius, Anirban Roy, Ramneet Kaur, Manoj Acharya, Colin Samplawski, Brian Matejek, Susmit Jha, Juan Alonso, Adam D. Cobb
2026	A*	`conf`	Privacy Preserving In-Context-Learning Framework for Large Language Models. AAAI Bishnu Bhusal, Manoj Acharya, Ramneet Kaur, Colin Samplawski, Anirban Roy, Adam D. Cobb, Rohit Chadha, Susmit Jha
2025	J	`jnl`	AGENT: An Aerial Vehicle Generation and Design Tool Using Large Language Models. CoRR Colin Samplawski, Adam D. Cobb, Susmit Jha
2025	J	`jnl`	Backpropagation-Free Metropolis-Adjusted Langevin Algorithm. CoRR Adam D. Cobb, Susmit Jha
2025	J	`jnl`	Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding. CoRR Trilok Padhi, Ramneet Kaur, Adam D. Cobb, Manoj Acharya, Anirban Roy, Colin Samplawski, Brian Matejek, Alexander M. Berenbeim, Nathaniel D. Bastian, Susmit Jha
2025	A*	`conf`	Polysemantic Dropout: Conformal OOD Detection for Specialized LLMs. EMNLP Ayush Gupta, Ramneet Kaur, Anirban Roy, Adam D. Cobb, Rama Chellappa, Susmit Jha
2025	J	`jnl`	Polysemantic Dropout: Conformal OOD Detection for Specialized LLMs. CoRR Ayush Gupta, Ramneet Kaur, Anirban Roy, Adam D. Cobb, Rama Chellappa, Susmit Jha
2025	—	`conf`	Predicting Secure Messaging Traffic in Clinical Settings. MedInfo Laura R. Baratta, Sunny S. Lou, Thomas George Kannampallil, Susmit Jha, Anirban Roy, Adam D. Cobb
2025	J	`jnl`	Privacy-Aware In-Context Learning for Large Language Models. CoRR Bishnu Bhusal, Manoj Acharya, Ramneet Kaur, Colin Samplawski, Anirban Roy, Adam D. Cobb, Rohit Chadha, Susmit Jha
2025	A	`conf`	Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference. UAI Colin Samplawski, Adam D. Cobb, Manoj Acharya, Ramneet Kaur, Susmit Jha
2025	J	`jnl`	Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference. CoRR Colin Samplawski, Adam D. Cobb, Manoj Acharya, Ramneet Kaur, Susmit Jha
2025	J	`jnl`	Spatio-Temporal Pruning for Compressed Spiking Large Language Models. CoRR Yi Jiang, Malyaban Bal, Brian Matejek, Susmit Jha, Adam D. Cobb, Abhronil Sengupta
2025	A	`conf`	Zero-Shot Detection of Out-of-Context Objects Using Foundation Models. WACV Anirban Roy, Adam D. Cobb, Ramneet Kaur, Sumit Jha, Nathaniel D. Bastian, Alexander M. Berenbeim, Robert H. Thomson, Iain Cruickshank, Alvaro Velasquez, Susmit Jha
2024	J	`jnl`	Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI. CoRR 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
2024	A*	`conf`	Direct Amortized Likelihood Ratio Estimation. AAAI Adam D. Cobb, Brian Matejek, Daniel Elenius, Anirban Roy, Susmit Jha
2024	—	`conf`	Post-hoc Uncertainty Quantification for Neurosymbolic Artificial Intelligence. MILCOM Alexander M. Berenbeim, Ramneet Kaur, Adam D. Cobb, Anirban Roy, Susmit Jha, Nathaniel D. Bastian
2024	J	`jnl`	Resource-Constrained Heuristic for Max-SAT. CoRR Brian Matejek, Daniel Elenius, Cale Gentry, David Stoker, Adam D. Cobb
2024	J	`jnl`	Second-Order Forward-Mode Automatic Differentiation for Optimization. CoRR Adam D. Cobb, Atilim Günes Baydin, Barak A. Pearlmutter, Susmit Jha
2023	A*	`conf`	AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs. NeurIPS 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
2023	J	`jnl`	AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs. CoRR 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
2023	—	`conf`	Challenges and Opportunities in Neuro-Symbolic Composition of Foundation Models. MILCOM Susmit Jha, Anirban Roy, Adam D. Cobb, Alexander M. Berenbeim, Nathaniel D. Bastian
2023	J	`jnl`	Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo. Mach. Learn. Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian
2023	J	`jnl`	Direct Amortized Likelihood Ratio Estimation. CoRR Adam D. Cobb, Brian Matejek, Daniel Elenius, Anirban Roy, Susmit Jha
2023	J	`jnl`	On Sequential Bayesian Inference for Continual Learning. CoRR Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts
2023	J	`jnl`	On Sequential Bayesian Inference for Continual Learning. Entropy Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts
2023	—	`conf`	hamiltorch: A PyTorch-based library for Hamiltonian Monte Carlo. CPS-IoT Week Workshops Adam D. Cobb
2022	J	`jnl`	Design of Unmanned Air Vehicles Using Transformer Surrogate Models. CoRR Adam D. Cobb, Anirban Roy, Daniel Elenius, Susmit Jha
2022	J	`jnl`	Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning. CoRR Meet P. Vadera, Adam D. Cobb, Brian Jalaian, Benjamin M. Marlin
2022	A*	`conf`	Principal Component Flows. ICML Edmond Cunningham, Adam D. Cobb, Susmit Jha
2022	J	`jnl`	Principal Manifold Flows. CoRR Edmond Cunningham, Adam D. Cobb, Susmit Jha
2022	—	`conf`	URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference methods. MLSys Meet P. Vadera, Jinyang Li, Adam D. Cobb, Brian Jalaian, Tarek F. Abdelzaher, Benjamin M. Marlin
2021	—	`conf`	Automatic Acoustic Mosquito Tagging with Bayesian Neural Networks. ECML/PKDD (4) Ivan Kiskin, Adam D. Cobb, Marianne Sinka, Kathy Willis, Stephen J. Roberts
2021	J	`jnl`	Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo. CoRR Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian
2021	—	`conf`	HumBugDB: A Large-scale Acoustic Mosquito Dataset. NeurIPS Datasets and Benchmarks 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
2021	J	`jnl`	HumBugDB: A Large-scale Acoustic Mosquito Dataset. CoRR 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
2021	B	`conf`	Improving Differential Evolution through Bayesian Hyperparameter Optimization. CEC Subhodip Biswas, Debanjan Saha, Shuvodeep De, Adam D. Cobb, Swagatam Das, Brian Jalaian
2021	Misc	`conf`	Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks. WSC Adam D. Cobb, Brian Jalaian, Nathaniel D. Bastian, Stephen Russell
2021	A	`conf`	Scaling Hamiltonian Monte Carlo inference for Bayesian neural networks with symmetric splitting. UAI Adam D. Cobb, Brian Jalaian
2020	A*	`conf`	BayesOpt Adversarial Attack. ICLR Binxin Ru, Adam D. Cobb, Arno Blaas, Yarin Gal
2020	J	`jnl`	Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization. CoRR Subhodip Biswas, Adam D. Cobb, Andreea Sistrunk, Naren Ramakrishnan, Brian Jalaian
2020	J	`jnl`	HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset. CoRR Ivan Kiskin, Adam D. Cobb, Lawrence Wang, Stephen Roberts
2020	Misc	`conf`	Humbug Zooniverse: A Crowd-Sourced Acoustic Mosquito Dataset. ICASSP Ivan Kiskin, Adam D. Cobb, Lawrence Wang, Stephen Roberts
2020	—	`conf`	On Uncertainty and Robustness in Large-Scale Intelligent Data Fusion Systems. CogMI 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
2020	J	`jnl`	Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting. CoRR Adam D. Cobb, Brian Jalaian
2020	—	`—`	The practicalities of scaling Bayesian neural networks to real-world applications. Adam D. Cobb
2020	J	`jnl`	URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks. CoRR Meet P. Vadera, Adam D. Cobb, Brian Jalaian, Benjamin M. Marlin
2019	J	`jnl`	An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval. CoRR 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
2019	J	`jnl`	Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo. CoRR Adam D. Cobb, Atilim Günes Baydin, Andrew Markham, Stephen J. Roberts
2019	A	`conf`	Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents. AAMAS Richard Everett, Adam D. Cobb, Andrew Markham, Stephen J. Roberts
2018	J	`jnl`	Bayesian Deep Learning for Exoplanet Atmospheric Retrieval. CoRR 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
2018	J	`jnl`	Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity. CoRR 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
2018	A*	`conf`	Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus. KDD Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts
2018	J	`jnl`	Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus. CoRR Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts
2018	J	`jnl`	Loss-Calibrated Approximate Inference in Bayesian Neural Networks. CoRR Adam D. Cobb, Stephen J. Roberts, Yarin Gal
2018	—	`conf`	Scalable Bounding of Predictive Uncertainty in Regression Problems with SLAC. SUM Arno Blaas, Adam D. Cobb, Jan-Peter Calliess, Stephen J. Roberts
2017	J	`jnl`	Learning from lions: inferring the utility of agents from their trajectories. CoRR Adam D. Cobb, Andrew Markham, Stephen J. Roberts