Jesse C. Cresswell

According to our database, Jesse C. Cresswell authored at least 23 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Augment then Smooth: Reconciling Differential Privacy with Certified Robustness.

Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot

Trans. Mach. Learn. Res., 2024

Neural Implicit Manifold Learning for Topology-Aware Density Estimation.

Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell

Trans. Mach. Learn. Res., 2024

Conformal Prediction Sets Can Cause Disparate Impact.

Jesse C. Cresswell, Bhargava Kumar, Yi Sui, Mouloud Belbahri

CoRR, 2024

Scaling Up Diffusion and Flow-based XGBoost Models.

Jesse C. Cresswell, Taewoo Kim

CoRR, 2024

Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks.

Antoni Kowalczuk, Jan Dubinski, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic

CoRR, 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

CoRR, 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

CoRR, 2024

A Geometric Explanation of the Likelihood OOD Detection Paradox.

Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Conformal Prediction Sets Improve Human Decision Making.

Jesse C. Cresswell, Yi Sui, Bhargava Kumar, Noël Vouitsis

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Self-supervised Representation Learning from Random Data Projectors.

Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs

Proceedings of the Twelfth International Conference on Learning Representations, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Disparate Impact in Differential Privacy from Gradient Misalignment.

Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, Jesse C. Cresswell

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Verifying the Union of Manifolds Hypothesis for Image Data.

Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Diagnosing and Fixing Manifold Overfitting in Deep Generative Models.

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini

Trans. Mach. Learn. Res., 2022

CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds.

Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-González, Marco Letizia, Anthony L. Caterini

CoRR, 2022

Find Your Friends: Personalized Federated Learning with the Right Collaborators.

Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross, Jesse C. Cresswell

CoRR, 2022

The Union of Manifolds Hypothesis and its Implications for Deep Generative Modelling.

Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem

CoRR, 2022

Neural Implicit Manifold Learning for Topology-Aware Generative Modelling.

Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell

CoRR, 2022

Denoising Deep Generative Models.

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini

Proceedings of the Proceedings on "I Can't Believe It's Not Better!, 2022

2021

ProxyFL: Decentralized Federated Learning through Proxy Model Sharing.

Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh

CoRR, 2021

Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows.

Brendan Leigh Ross, Jesse C. Cresswell

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the 9th International Conference on Learning Representations, 2021