Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
Naihe Feng, Yi Sui, Shiyi Hou, Jesse C. Cresswell, Ga WuShort Paper
SIGIR 2025
Publications
2025
Short Paper
SIGIR 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 HosseinzadehOral, Top 20% of accepted papers
NAACL 2025 [Paper] [PDF] [Code]
Trustworthy AI Must Account for Intersectionality
Jesse C. CresswellICLR 2025 Workshop BiAlign [Paper] [PDF]
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-GanemSpotlight, Top 25% of accepted papers
ICLR 2025 [Paper] [PDF] [Code]
Conformal Prediction Sets Can Cause Disparate Impact
Jesse C. Cresswell, Bhargava Kumar, Yi Sui, Mouloud BelbahriSpotlight, Top 25% of accepted papers
ICLR 2025 [Paper] [PDF] [Code]
Zero-Execution Retrieval-Augmented Configuration Tuning of Spark Applications
Raunaq Suri, Ilan Gofman, Guangwei Yu, Jesse C. CresswellarXiv [Paper] [PDF] [Code]
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-GanemNeurIPS 2024 Workshop FITML [Paper] [PDF]
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks
Wei Cui, Yi Sui, Jesse C. Cresswell, Keyvan GolestanNeurIPS 2024 Workshop SSL [Paper] [PDF]
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-GanemSpotlight award
NeurIPS 2024 [Paper] [PDF] [Code]
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause et al.arXiv [Paper] [PDF]
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. CateriniarXiv [Paper] [PDF] [Code]
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,TMLR [Paper] [PDF] [Code]
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 DziedzicICML 2024 Workshop FMWild [Paper] [PDF] [Code]
Scaling Up Diffusion and Flow-based XGBoost Models
Jesse C. Cresswell, Taewoo KimICML 2024 Workshop AI4Science [Paper] [PDF] [Code]
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-GanemICML 2024 [Paper] [PDF] [Code]
Conformal Prediction Sets Improve Human Decision Making
Jesse C. Cresswell, Yi Sui, Bhargava Kumar, Noël VouitsisICML 2024 [Paper] [PDF] [Code]
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicholas PapernotTMLR [Paper] [PDF] [Code]
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 VolkovsHighlight, Top 3% of submitted papers
CVPR 2024 [Paper] [PDF] [Code]
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 VolkovsICLR 2024 [Paper] [PDF] [Code]
Neural Implicit Manifold Learning for Topology-Aware Generative Modelling
Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. CresswellTMLR [Paper] [PDF] [Code] [Video]
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, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-GanemNeurIPS 2023 [Paper] [PDF] [Code]
Decentralized Federated Learning through Proxy Model Sharing
*Shivam Kalra, *Junfeng Wen, *Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh* denotes equal contribution.
Nature Communications [Paper] [PDF] [Code]
Verifying the Union of Manifolds Hypothesis for Image Data
Bradley C.A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-GanemICLR 2023 [Paper] [PDF] [Code]
Disparate Impact in Differential Privacy from Gradient Misalignment
Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, Jesse C. CresswellSpotlight, Top 25% of accepted papers
ICLR 2023 [Paper] [PDF] [Code]
2022
Denoising Deep Generative Models
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, and Anthony L. CateriniNeurIPS 2022 Workshop ICBINB [Paper] [PDF] [Code]
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-Gonzalez, Marco Letizia, and Anthony L. CateriniNeurIPS 2022 Workshop ML4PS [Paper] [PDF] [Video] [Poster]
Find Your Friends: Personalized Federated Learning with the Right Collaborators
Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross, and Jesse C. CresswellNeurIPS 2022 Workshop on Federated Learning [Paper] [PDF]
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. CateriniTMLR [Paper] [PDF] [Code]
Federated Learning and Differential Privacy for Medical Image Analysis
Mohammed Adnan, Shivam Kalra, Jesse C. Cresswell, Graham W. Taylor, and Hamid R. TizhooshNature Scientific Reports [Paper] [PDF]
2021
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows
Brendan Leigh Ross, Jesse C. CresswellNeurIPS 2021 [Paper] [PDF] [Code]
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh GargICLR 2021 [Paper] [PDF] [Code]
2020
Operational symmetries of entangled states
*Ilan Tzitrin, *Aaron Z. Goldberg, and *Jesse C. CresswellFeatured Article. * denotes equal contribution.
J. Phys. A [Paper] [PDF]
2019
Quantum Information Approaches to Quantum Gravity
Jesse C. CresswellUniversity of Toronto Doctoral Thesis [Paper] [PDF]
Holographic relations for OPE blocks in excited states
Jesse C. Cresswell, Ian T. Jardine, and Amanda W. PeetJHEP [Paper] [PDF]
Perturbative expansion of entanglement negativity using patterned matrix calculus
Jesse C. Cresswell, Ilan Tzitrin, Aaron Z. GoldbergPhys. Rev. A [Paper] [PDF]
2018
Universal entanglement timescale for Rényi entropies
Jesse C. CresswellPhys. Rev. A [Paper] [PDF]
2017
Kinematic space for conical defects
Jesse C. Cresswell, Amanda W. PeetJHEP [Paper] [PDF]
2015
Lorenz gauge quantization in conformally flat spacetimes
Jesse C. Cresswell, Dan N. VollickPhys. Rev. D [Paper] [PDF]