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 (ICLR, 2021)

Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows. - Brendan Leigh Ross; Jesse C. Cresswell (NeurIPS, 2021)

ProxyFL: Decentralized Federated Learning through Proxy Model Sharing. - Shivam Kalra; Junfeng Wen; Jesse C. Cresswell; Maksims Volkovs; Hamid R. Tizhoosh (CoRR, 2021)

Denoising Deep Generative Models. - Gabriel Loaiza-Ganem; Brendan Leigh Ross; Luhuan Wu; John P. Cunningham; Jesse C. Cresswell; Anthony L. Caterini (ICBINB, 2022)

Neural Implicit Manifold Learning for Topology-Aware Generative Modelling. - Brendan Leigh Ross; Gabriel Loaiza-Ganem; Anthony L. Caterini; 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)

Find Your Friends: Personalized Federated Learning with the Right Collaborators. - Yi Sui 001; Junfeng Wen; Yenson Lau; Brendan Leigh Ross; Jesse C. Cresswell (CoRR, 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)

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)

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 (ICLR, 2023)

Disparate Impact in Differential Privacy from Gradient Misalignment. - Maria S. Esipova; Atiyeh Ashari Ghomi; Yaqiao Luo; Jesse C. Cresswell (ICLR, 2023)

Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. - George Stein; Jesse C. Cresswell; Rasa Hosseinzadeh; Yi Sui 001; Brendan Leigh Ross; Valentin Villecroze; Zhaoyan Liu; Anthony L. Caterini; J. Eric T. Taylor; Gabriel Loaiza-Ganem (NeurIPS, 2023)

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 (CVPR, 2024)

Self-supervised Representation Learning from Random Data Projectors. - Yi Sui 001; Tongzi Wu; Jesse C. Cresswell; Ga Wu; George Stein; Xiao Shi Huang; Xiaochen Zhang; Maksims Volkovs (ICLR, 2024)

Conformal Prediction Sets Improve Human Decision Making. - Jesse C. Cresswell; Yi Sui 001; Bhargava Kumar; Noël Vouitsis (ICML, 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 (ICML, 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 (NeurIPS, 2024)

Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks. - Antoni Kowalczuk; Jan Dubinski; Atiyeh Ashari Ghomi; Yi Sui 001; George Stein; Jiapeng Wu; Jesse C. Cresswell; Franziska Boenisch; Adam Dziedzic (CoRR, 2024)

Scaling Up Diffusion and Flow-based XGBoost Models. - Jesse C. Cresswell; Taewoo Kim (CoRR, 2024)

TabDPT: Scaling Tabular Foundation Models. - Junwei Ma; Valentin Thomas; Rasa Hosseinzadeh; Hamidreza Kamkari; Alex Labach; Jesse C. Cresswell; Keyvan Golestan; Guangwei Yu; Maksims Volkovs; Anthony L. Caterini (CoRR, 2024)

CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation. - 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; et al. (CoRR, 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 (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 (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)

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)

Conformal Prediction Sets Can Cause Disparate Impact. - Jesse C. Cresswell; Bhargava Kumar; Yi Sui 001; Mouloud Belbahri (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 (ICLR, 2025)

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 (NAACL (Long Papers), 2025)

Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality. - Naihe Feng; Yi Sui 001; Shiyi Hou; Jesse C. Cresswell; Ga Wu (SIGIR, 2025)

Zero-Execution Retrieval-Augmented Configuration Tuning of Spark Applications. - Raunaq Suri; Ilan Gofman; Guangwei Yu; Jesse C. Cresswell (CoRR, 2025)

DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks. - Wei Cui; Tongzi Wu; Jesse C. Cresswell; Yi Sui 001; Keyvan Golestan (CoRR, 2025)

Trustworthy AI Must Account for Intersectionality. - Jesse C. Cresswell (CoRR, 2025)

CausalPFN: Amortized Causal Effect Estimation via In-Context Learning. - Vahid Balazadeh Meresht; Hamidreza Kamkari; Valentin Thomas; Benson Li; Junwei Ma; Jesse C. Cresswell; Rahul G. Krishnan (CoRR, 2025)

Textual Bayes: Quantifying Uncertainty in LLM-Based Systems. - Brendan Leigh Ross; Noël Vouitsis; Atiyeh Ashari Ghomi; Rasa Hosseinzadeh; Ji Xin; Zhaoyan Liu; Yi Sui 001; Shiyi Hou; Kin Kwan Leung; Gabriel Loaiza-Ganem; Jesse C. Cresswell (CoRR, 2025)