Cobb, Adam D

11 publications

UAI 2025 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

NeurIPS 2025 SpikingVTG: A Spiking Detection Transformer for Video Temporal Grounding Malyaban Bal, Brian Matejek, Susmit Jha, Adam D. Cobb

NeurIPSW 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 Michael Berenbeim, John A. Pavlik, Nathaniel D. Bastian, Susmit Jha

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

NeurIPSW 2024 Second-Order Forward-Mode Automatic Differentiation for Optimization Adam D. Cobb, Atilim Gunes Baydin, Barak A. Pearlmutter, Susmit Jha

NeurIPSW 2024 SpikingVTG: Saliency Feedback Gating Enabled Spiking Video Temporal Grounding Malyaban Bal, Brian Matejek, Susmit Jha, Adam D. Cobb

NeurIPSW 2024 SpikingVTG: Saliency Feedback Gating Enabled Spiking Video Temporal Grounding Malyaban Bal, Brian Matejek, Susmit Jha, Adam D. Cobb

MLJ 2023 Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian

ICML 2022 Principal Component Flows Edmond Cunningham, Adam D Cobb, Susmit Jha

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

UAI 2021 Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting Adam D. Cobb, Brian Jalaian