Gabriel Loaiza-Ganem

54 papers A* 15Journal 33Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	A Geometric Framework for Understanding Memorization in Generative Models. ICLR Brendan Leigh Ross, Hamidreza Kamkari, Tongzi Wu, Rasa Hosseinzadeh, Zhaoyan Liu, George Stein, Jesse C. Cresswell, Gabriel Loaiza-Ganem
2025	J	`jnl`	Deep Ensembles Secretly Perform Empirical Bayes. CoRR Gabriel Loaiza-Ganem, Valentin Villecroze, Yixin Wang
2025	A*	`conf`	Last Layer Empirical Bayes. ICLR Valentin Villecroze, Yixin Wang, Gabriel Loaiza-Ganem
2025	J	`jnl`	Last Layer Empirical Bayes. CoRR Valentin Villecroze, Yixin Wang, Gabriel Loaiza-Ganem
2025	J	`jnl`	On Convolutions, Intrinsic Dimension, and Diffusion Models. CoRR Kin Kwan Leung, Rasa Hosseinzadeh, Gabriel Loaiza-Ganem
2025	J	`jnl`	On Convolutions, Intrinsic Dimension, and Diffusion Models. Trans. Mach. Learn. Res. Kin Kwan Leung, Rasa Hosseinzadeh, Gabriel Loaiza-Ganem
2025	J	`jnl`	Textual Bayes: Quantifying Uncertainty in LLM-Based Systems. CoRR Brendan Leigh Ross, Noël Vouitsis, Atiyeh Ashari Ghomi, Rasa Hosseinzadeh, Ji Xin, Zhaoyan Liu, Yi Sui, Shiyi Hou, Kin Kwan Leung, Gabriel Loaiza-Ganem, Jesse C. Cresswell
2024	A*	`conf`	A Geometric Explanation of the Likelihood OOD Detection Paradox. ICML Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem
2024	J	`jnl`	A Geometric Explanation of the Likelihood OOD Detection Paradox. CoRR Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem
2024	J	`jnl`	A Geometric Framework for Understanding Memorization in Generative Models. CoRR Brendan Leigh Ross, Hamidreza Kamkari, Tongzi Wu, Rasa Hosseinzadeh, Zhaoyan Liu, George Stein, Jesse C. Cresswell, Gabriel Loaiza-Ganem
2024	A*	`conf`	A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models. NeurIPS Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem
2024	J	`jnl`	A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models. CoRR Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem
2024	J	`jnl`	CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation. CoRR Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka, Benjamin Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Oz Amram, Kerstin Borras, Matthew R. Buckley, Erik Buhmann, Thorsten Buss, Renato Paulo Da Costa Cardoso, Anthony L. Caterini, Nadezda Chernyavskaya, Federico A. G. Corchia, Jesse C. Cresswell, Sascha Diefenbacher, Etienne Dreyer, Vijay Ekambaram, Engin Eren, Florian Ernst, Luigi Favaro, Matteo Franchini, Frank Gaede, Eilam Gross, Shih-Chieh Hsu, Kristina Jaruskova, Benno Käch, Jayant Kalagnanam, Raghav Kansal, Taewoo Kim, Dmitrii Kobylianskii, Anatolii Korol, William Korcari, Dirk Krücker, Katja Krüger, Marco Letizia, Shu Li, Qibin Liu, Xiulong Liu, Gabriel Loaiza-Ganem, Thandikire Madula, Peter McKeown, Isabell-A. Melzer-Pellmann, Vinicius Mikuni, Nam Nguyen, Ayodele Ore, Sofia Palacios Schweitzer, Ian Pang, Kevin Pedro, Tilman Plehn, Witold Pokorski, Huilin Qu, Piyush Raikwar, John A. Raine, Humberto Reyes-González, Lorenzo Rinaldi, Brendan Leigh Ross, Moritz A. W. Scham, Simon Schnake, Chase Shimmin, Eli Shlizerman, Nathalie Soybelman, Mudhakar Srivatsa, Kalliopi Tsolaki, Sofia Vallecorsa, Kyongmin Yeo, Rui Zhang
2024	A*	`conf`	Data-Efficient Multimodal Fusion on a Single GPU. CVPR Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs
2024	J	`jnl`	Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections. CoRR Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L. Caterini, Jesse C. Cresswell
2024	J	`jnl`	Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections. Trans. Mach. Learn. Res. Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L. Caterini, Jesse C. Cresswell
2024	J	`jnl`	Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples. CoRR Noël Vouitsis, Rasa Hosseinzadeh, Brendan Leigh Ross, Valentin Villecroze, Satya Krishna Gorti, Jesse C. Cresswell, Gabriel Loaiza-Ganem
2024	J	`jnl`	Neural Implicit Manifold Learning for Topology-Aware Density Estimation. Trans. Mach. Learn. Res. Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell
2023	J	`jnl`	Data-Efficient Multimodal Fusion on a Single GPU. CoRR Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs
2023	A*	`conf`	Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. NeurIPS George Stein, Jesse C. Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-Ganem
2023	J	`jnl`	Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. CoRR George Stein, Jesse C. Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-Ganem
2023	A*	`conf`	TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation. ICML Zhaoyan Liu, Noël Vouitsis, Satya Krishna Gorti, Jimmy Ba, Gabriel Loaiza-Ganem
2023	J	`jnl`	TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation. CoRR Zhaoyan Liu, Noël Vouitsis, Satya Krishna Gorti, Jimmy Ba, Gabriel Loaiza-Ganem
2023	A*	`conf`	Verifying the Union of Manifolds Hypothesis for Image Data. ICLR Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem
2022	A*	`conf`	Bayesian Nonparametrics for Offline Skill Discovery. ICML Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem
2022	J	`jnl`	Bayesian Nonparametrics for Offline Skill Discovery. CoRR Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem
2022	J	`jnl`	CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds. CoRR Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-González, Marco Letizia, Anthony L. Caterini
2022	—	`conf`	Denoising Deep Generative Models. ICBINB Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
2022	J	`jnl`	Denoising Deep Generative Models. CoRR Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
2022	J	`jnl`	Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. CoRR Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini
2022	J	`jnl`	Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. Trans. Mach. Learn. Res. Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini
2022	J	`jnl`	Neural Implicit Manifold Learning for Topology-Aware Generative Modelling. CoRR Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell
2022	J	`jnl`	On the Normalizing Constant of the Continuous Categorical Distribution. CoRR Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Andres Potapczynski, John P. Cunningham
2022	J	`jnl`	Relating Regularization and Generalization through the Intrinsic Dimension of Activations. CoRR Bradley C. A. Brown, Jordan Juravsky, Anthony L. Caterini, Gabriel Loaiza-Ganem
2022	J	`jnl`	The Union of Manifolds Hypothesis and its Implications for Deep Generative Modelling. CoRR Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem
2021	A*	`conf`	C-Learning: Horizon-Aware Cumulative Accessibility Estimation. ICLR Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg
2021	—	`conf`	Entropic Issues in Likelihood-Based OOD Detection. ICBINB@NeurIPS Anthony L. Caterini, Gabriel Loaiza-Ganem
2021	J	`jnl`	Entropic Issues in Likelihood-Based OOD Detection. CoRR Anthony L. Caterini, Gabriel Loaiza-Ganem
2021	A*	`conf`	Rectangular Flows for Manifold Learning. NeurIPS Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	Rectangular Flows for Manifold Learning. CoRR Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2020	J	`jnl`	C-Learning: Horizon-Aware Cumulative Accessibility Estimation. CoRR Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg
2020	A*	`conf`	Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. NeurIPS Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
2020	A*	`conf`	The continuous categorical: a novel simplex-valued exponential family. ICML Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham
2020	J	`jnl`	The continuous categorical: a novel simplex-valued exponential family. CoRR Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham
2020	—	`conf`	Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. ICBINB@NeurIPS Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2020	J	`jnl`	Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. CoRR Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2019	—	`—`	Advances in Deep Generative Modeling With Applications to Image Generation and Neuroscience. Gabriel Loaiza-Ganem
2019	A*	`conf`	Deep Random Splines for Point Process Intensity Estimation of Neural Population Data. NeurIPS Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark M. Churchland, John P. Cunningham
2019	—	`conf`	Deep Random Splines for Point Process Intensity Estimation. DGS@ICLR Gabriel Loaiza-Ganem, John P. Cunningham
2019	J	`jnl`	Deep Random Splines for Point Process Intensity Estimation. CoRR Gabriel Loaiza-Ganem, John P. Cunningham
2019	J	`jnl`	Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. CoRR Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
2019	A*	`conf`	The continuous Bernoulli: fixing a pervasive error in variational autoencoders. NeurIPS Gabriel Loaiza-Ganem, John P. Cunningham
2019	J	`jnl`	The continuous Bernoulli: fixing a pervasive error in variational autoencoders. CoRR Gabriel Loaiza-Ganem, John P. Cunningham
2017	—	`conf`	Maximum Entropy Flow Networks. ICLR (Poster) Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham