Cunningham, John Patrick

11 publications

ICLRW 2025 Bayesian Invariance Modeling of Multi-Environment Data Luhuan Wu, Mingzhang Yin, Yixin Wang, John Patrick Cunningham, David Blei

NeurIPS 2025 Reverse Diffusion Sequential Monte Carlo Samplers Luhuan Wu, Yi Han, Christian A. Naesseth, John Patrick Cunningham

ICML 2025 Theoretical Limitations of Ensembles in the Age of Overparameterization Niclas Dern, John Patrick Cunningham, Geoff Pleiss

TMLR 2024 LoRA Learns Less and Forgets Less Dan Biderman, Jacob Portes, Jose Javier Gonzalez Ortiz, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, John Patrick Cunningham

TMLR 2024 Pathologies of Predictive Diversity in Deep Ensembles Taiga Abe, E. Kelly Buchanan, Geoff Pleiss, John Patrick Cunningham

ICMLW 2023 Practical and Asymptotically Exact Conditional Sampling in Diffusion Models Brian L. Trippe, Luhuan Wu, Christian A. Naesseth, David Blei, John Patrick Cunningham

NeurIPSW 2023 The Effects of Ensembling on Long-Tailed Data E. Kelly Buchanan, Geoff Pleiss, John Patrick Cunningham

NeurIPSW 2022 Denoising Deep Generative Models Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John Patrick Cunningham, Jesse C Cresswell, Anthony L. Caterini

NeurIPSW 2022 The Best Deep Ensembles Sacrifice Predictive Diversity Taiga Abe, E. Kelly Buchanan, Geoff Pleiss, John Patrick Cunningham

ICMLW 2021 Rectangular Flows for Manifold Learning Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John Patrick Cunningham

NeurIPSW 2020 Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John Patrick Cunningham