Loaiza-Ganem, Gabriel

35 publications

ICLR 2025 A Geometric Framework for Understanding Memorization in Generative Models Brendan Leigh Ross, Hamidreza Kamkari, Tongzi Wu, Rasa Hosseinzadeh, Zhaoyan Liu, George Stein, Jesse C. Cresswell, Gabriel Loaiza-Ganem

ICLRW 2025 Last Layer Empirical Bayes Valentin Villecroze, Yixin Wang, Gabriel Loaiza-Ganem

TMLR 2025 On Convolutions, Intrinsic Dimension, and Diffusion Models Kin Kwan Leung, Rasa Hosseinzadeh, Gabriel Loaiza-Ganem

ICML 2024 A Geometric Explanation of the Likelihood OOD Detection Paradox Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul Krishnan, Gabriel Loaiza-Ganem

ICMLW 2024 A Geometric Framework for Understanding Memorization in Generative Models Brendan Leigh Ross, Hamidreza Kamkari, Zhaoyan Liu, Tongzi Wu, George Stein, Gabriel Loaiza-Ganem, Jesse C. Cresswell

ICMLW 2024 A Geometric Framework for Understanding Memorization in Generative Models Brendan Leigh Ross, Hamidreza Kamkari, Zhaoyan Liu, Tongzi Wu, George Stein, Gabriel Loaiza-Ganem, Jesse C. Cresswell

NeurIPS 2024 A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem

ICMLW 2024 A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem

CVPR 2024 Data-Efficient Multimodal Fusion on a Single GPU Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs

TMLR 2024 Deep Generative Models Through the Lens of the Manifold Hypothesis: A Survey and New Connections Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L. Caterini, Jesse C. Cresswell

ICMLW 2024 Differentiable Local Intrinsic Dimension Estimation with Diffusion Models Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem

NeurIPSW 2024 Inconsistencies in Consistency Models: Better ODE Solving Does Not Imply Better Samples Noël Vouitsis, Rasa Hosseinzadeh, Brendan Leigh Ross, Valentin Villecroze, Satya Krishna Gorti, Jesse C. Cresswell, Gabriel Loaiza-Ganem

TMLR 2024 Neural Implicit Manifold Learning for Topology-Aware Density Estimation Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell

ICMLW 2024 Scalable Local Intrinsic Dimension Estimation with Diffusion Models Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem

NeurIPS 2023 Exposing Flaws of Generative Model Evaluation Metrics and Their Unfair Treatment of Diffusion Models George Stein, Jesse Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L Caterini, Eric Taylor, Gabriel Loaiza-Ganem

ICML 2023 TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation Zhaoyan Liu, Noël Vouitsis, Satya Krishna Gorti, Jimmy Ba, Gabriel Loaiza-Ganem

ICLR 2023 Verifying the Union of Manifolds Hypothesis for Image Data Bradley CA Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C Cresswell, Gabriel Loaiza-Ganem

ICML 2022 Bayesian Nonparametrics for Offline Skill Discovery Valentin Villecroze, Harry Braviner, Panteha Naderian, Chris Maddison, Gabriel Loaiza-Ganem

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

TMLR 2022 Diagnosing and Fixing Manifold Overfitting in Deep Generative Models Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C Cresswell, Anthony L. Caterini

NeurIPSW 2022 Relating Regularization and Generalization Through the Intrinsic Dimension of Activations Bradley CA Brown, Jordan Juravsky, Anthony L. Caterini, Gabriel Loaiza-Ganem

NeurIPSW 2022 Relating Regularization and Generalization Through the Intrinsic Dimension of Activations Bradley CA Brown, Jordan Juravsky, Anthony L. Caterini, Gabriel Loaiza-Ganem

NeurIPSW 2022 The Union of Manifolds Hypothesis Bradley CA Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C Cresswell, Gabriel Loaiza-Ganem

ICLR 2021 C-Learning: Horizon-Aware Cumulative Accessibility Estimation Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg

NeurIPSW 2021 Entropic Issues in Likelihood-Based OOD Detection Anthony L. Caterini, Gabriel Loaiza-Ganem

NeurIPSW 2021 Entropic Issues in Likelihood-Based OOD Detection Anthony L. Caterini, Gabriel Loaiza-Ganem

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

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

NeurIPS 2020 Invertible Gaussian Reparameterization: Revisiting the Gumbel-SoftMax Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham

ICML 2020 The Continuous Categorical: A Novel Simplex-Valued Exponential Family Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, John 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

ICLRW 2019 Deep Random Splines for Point Process Intensity Estimation Gabriel Loaiza-Ganem, John P. Cunningham

NeurIPS 2019 Deep Random Splines for Point Process Intensity Estimation of Neural Population Data Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark Churchland, John P. Cunningham

NeurIPS 2019 The Continuous Bernoulli: Fixing a Pervasive Error in Variational Autoencoders Gabriel Loaiza-Ganem, John P. Cunningham

ICLR 2017 Maximum Entropy Flow Networks Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham