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ORCIDJesse C. Cresswell
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2020 – today
- 2024
[j3]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)- [j2]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) - [c10]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. CVPR 2024: 27229-27241 - [c9]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 - [c8]Jesse C. Cresswell, Yi Sui, Bhargava Kumar, Noël Vouitsis:
Conformal Prediction Sets Improve Human Decision Making. ICML 2024 - [c7]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. ICML 2024 - [i21]Jesse C. Cresswell, Yi Sui, Bhargava Kumar, Noël Vouitsis:
Conformal Prediction Sets Improve Human Decision Making. CoRR abs/2401.13744 (2024) - [i20]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 abs/2403.18910 (2024) - [i19]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 abs/2404.02954 (2024) - [i18]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 abs/2406.03537 (2024) - [i17]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 abs/2407.12588 (2024) - [i16]Jesse C. Cresswell, Taewoo Kim:
Scaling Up Diffusion and Flow-based XGBoost Models. CoRR abs/2408.16046 (2024) - [i15]Jesse C. Cresswell, Bhargava Kumar, Yi Sui, Mouloud Belbahri:
Conformal Prediction Sets Can Cause Disparate Impact. CoRR abs/2410.01888 (2024) - 2023
- [c6]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 - [c5]Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, Jesse C. Cresswell:
Disparate Impact in Differential Privacy from Gradient Misalignment. ICLR 2023 - [c4]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 - [i14]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 abs/2306.04675 (2023) - [i13]Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell
, Franziska Boenisch, Nicolas Papernot:
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. CoRR abs/2306.08656 (2023) - [i12]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 abs/2310.07756 (2023) - [i11]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 abs/2312.10144 (2023) - 2022
- [j1]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) - [c3]Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini:
Denoising Deep Generative Models. ICBINB 2022: 41-50 - [i10]Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini:
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. CoRR abs/2204.07172 (2022) - [i9]Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, Jesse C. Cresswell:
Disparate Impact in Differential Privacy from Gradient Misalignment. CoRR abs/2206.07737 (2022) - [i8]Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell:
Neural Implicit Manifold Learning for Topology-Aware Generative Modelling. CoRR abs/2206.11267 (2022) - [i7]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 abs/2207.02862 (2022) - [i6]Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross, Jesse C. Cresswell:
Find Your Friends: Personalized Federated Learning with the Right Collaborators. CoRR abs/2210.06597 (2022) - [i5]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 abs/2211.15380 (2022) - [i4]Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini:
Denoising Deep Generative Models. CoRR abs/2212.01265 (2022) - 2021
- [c2]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 - [c1]Brendan Leigh Ross, Jesse C. Cresswell:
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows. NeurIPS 2021: 26635-26648 - [i3]Brendan Leigh Ross, Jesse C. Cresswell:
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows. CoRR abs/2106.05275 (2021) - [i2]Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh:
ProxyFL: Decentralized Federated Learning through Proxy Model Sharing. CoRR abs/2111.11343 (2021) - 2020
- [i1]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 abs/2011.12363 (2020)
Coauthor Index
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