Cresswell, Jesse C.

32 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
NeurIPS 2025 CausalPFN: Amortized Causal Effect Estimation via In-Context Learning Vahid Balazadeh, Hamidreza Kamkari, Valentin Thomas, Junwei Ma, Bingru Li, Jesse C. Cresswell, Rahul Krishnan
ICLR 2025 Conformal Prediction Sets Can Cause Disparate Impact Jesse C. Cresswell, Bhargava Kumar, Yi Sui, Mouloud Belbahri
NeurIPS 2025 Document Summarization with Conformal Importance Guarantees Bruce Kuwahara, Chen-Yuan Lin, Xiao Shi Huang, Kin Kwan Leung, Jullian Arta Yapeter, Ilya Stanevich, Felipe Perez, Jesse C. Cresswell
NeurIPS 2025 TabDPT: Scaling Tabular Foundation Models on Real Data Junwei Ma, Valentin Thomas, Rasa Hosseinzadeh, Alex Labach, Jesse C. Cresswell, Keyvan Golestan, Guangwei Yu, Anthony L. Caterini, Maksims Volkovs
ICLRW 2025 Trustworthy AI Must Account for Intersectionality Jesse C. Cresswell
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
TMLR 2024 Augment Then Smooth: Reconciling Differential Privacy with Certified Robustness Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot
ICMLW 2024 Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks Antoni Kowalczuk, Jan Dubiński, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic
ICML 2024 Conformal Prediction Sets Improve Human Decision Making Jesse C. Cresswell, Yi Sui, Bhargava Kumar, Noël Vouitsis
NeurIPSW 2024 DRESS: Disentangled Representation-Based Self-Supervised Meta-Learning for Diverse Tasks Wei Cui, Yi Sui, Jesse C. Cresswell, Keyvan Golestan
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
NeurIPSW 2024 MSc-SQL: Multi-Sample Critiquing Small Language Models for Text-to-SQL Translation Satya Krishna Gorti, Ilan Gofman, Zhaoyan Liu, Jiapeng Wu, Noël Vouitsis, Guangwei Yu, Jesse C. Cresswell, Rasa Hosseinzadeh
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
ICMLW 2024 Scaling up Diffusion and Flow-Based XGBoost Models Jesse C. Cresswell, Taewoo Kim
ICLR 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
ICLR 2023 Disparate Impact in Differential Privacy from Gradient Misalignment Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, Jesse C Cresswell
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
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 Find Your Friends: Personalized Federated Learning with the Right Collaborators Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross, Jesse C Cresswell
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
ICMLW 2021 Conformal Embedding Flows: Tractable Density Estimation on Learned Manifolds Brendan Leigh Ross, Jesse C Cresswell