Jesse C. Cresswell

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 29

Top Topics

Diffusion Models

Density Estimation

Differential Privacy

Laplacian Eigenmaps

Top Venues

Trans. Mach. Learn. Res.

Publications

Jesse C. Cresswell, Yi Sui, Bhargava Kumar, Noël Vouitsis
Conformal Prediction Sets Improve Human Decision Making. CoRR (2024)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L. Caterini, Jesse C. Cresswell
Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections. CoRR (2024)
Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. Trans. Mach. Learn. Res. 2024 (2024)
Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell
Neural Implicit Manifold Learning for Topology-Aware Density Estimation. Trans. Mach. Learn. Res. 2024 (2024)
Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem
A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models. CoRR (2024)
Antoni Kowalczuk, Jan Dubinski, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks. CoRR (2024)
Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem
A Geometric Explanation of the Likelihood OOD Detection Paradox. CoRR (2024)
Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs
Self-supervised Representation Learning from Random Data Projectors. ICLR (2024)
Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. CoRR (2023)
Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs
Self-supervised Representation Learning From Random Data Projectors. CoRR (2023)
Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs
Data-Efficient Multimodal Fusion on a Single GPU. CoRR (2023)
Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem
Verifying the Union of Manifolds Hypothesis for Image Data. ICLR (2023)
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
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. NeurIPS (2023)
Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, Jesse C. Cresswell
Disparate Impact in Differential Privacy from Gradient Misalignment. ICLR (2023)
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
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. CoRR (2023)
Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, Jesse C. Cresswell
Disparate Impact in Differential Privacy from Gradient Misalignment. CoRR (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. CoRR (2022)
Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell
Neural Implicit Manifold Learning for Topology-Aware Generative Modelling. CoRR (2022)
Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross, Jesse C. Cresswell
Find Your Friends: Personalized Federated Learning with the Right Collaborators. CoRR (2022)
Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem
The Union of Manifolds Hypothesis and its Implications for Deep Generative Modelling. CoRR (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
Denoising Deep Generative Models. CoRR (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
Denoising Deep Generative Models. ICBINB (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. Trans. Mach. Learn. Res. 2022 (2022)
Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-González, Marco Letizia, Anthony L. Caterini
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds. CoRR (2022)
Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh
ProxyFL: Decentralized Federated Learning through Proxy Model Sharing. CoRR (2021)
Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg
C-Learning: Horizon-Aware Cumulative Accessibility Estimation. ICLR (2021)
Brendan Leigh Ross, Jesse C. Cresswell
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows. NeurIPS (2021)
Brendan Leigh Ross, Jesse C. Cresswell
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows. CoRR (2021)
Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg
C-Learning: Horizon-Aware Cumulative Accessibility Estimation. CoRR (2020)