Naihe Feng, Yi Sui, Shiyi Hou, Jesse C. Cresswell, Ga Wu. Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality. SIGIR 2025
Satya Krishna Gorti, Ilan Gofman, Zhaoyan Liu, Jiapeng Wu, Noël Vouitsis, Guangwei Yu, Jesse C. Cresswell, Rasa Hosseinzadeh. MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation. NAACL 2025
Jesse C. Cresswell. Trustworthy AI Must Account for Intersectionality. ICLR 2025 Workshop on Bidirectional Human-AI Alignment
Brendan Leigh Ross, Hamidreza Kamkari, Zhaoyan Liu, Tongzi Wu, George Stein, Gabriel Loaiza-Ganem, Jesse C. Cresswell. A Geometric Framework for Understanding Memorization in Generative Models. ICLR 2025
Jesse C. Cresswell, Bhargava Kumar, Yi Sui, and Mouloud Belbahri. Conformal Prediction Sets Can Cause Disparate Impact. ICLR 2025
Raunaq Suri, Ilan Gofman, Guangwei Yu, Jesse C. Cresswell. Zero-Execution Retrieval-Augmented Configuration Tuning of Spark Applications. arXiv preprint 2503.03826
Noël Vouitsis, Rasa Hosseinzadeh, Brendan Leigh Ross, Valentin Villecroze, Satya Krishna Gorti, Jesse C. Cresswell, Gabriel Loaiza-Ganem. Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples. NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning
Wei Cui, Yi Sui, Jesse C. Cresswell, Keyvan Golestan. DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks. NeurIPS 2024 Workshop on Self-Supervised Learning - Theory and Practice
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. In Advances in Neural Information Processing Systems, volume 37, 2024
Claudius Krause et al. CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation. arXiv preprint 2410.21611
Junwei Ma, Valentin Thomas, Rasa Hosseinzadeh, Hamidreza Kamkari, Alex Labach, Jesse C. Cresswell, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony L. Caterini. TabDPT: Scaling Tabular Foundation Models. arXiv preprint 2410.18164
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L. Caterini, esse C. Cresswell. Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections. TMLR 2024
Antoni Kowalczuk, Jan Dubiński, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic. Robust Self-Supervised Learning Across Diverse Downstream Tasks. ICML 2024 Workshop on Foundation Models in the Wild
Jesse C. Cresswell, Taewoo Kim. Scaling Up Diffusion and Flow-based XGBoost Models. ICML 2024 Workshop on AI for Science
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. International Conference on Machine Learning 2024
Jesse C. Cresswell, Yi Sui, Bhargava Kumar, and Noël Vouitsis. Conformal Prediction Sets Improve Human Decision Making. International Conference on Machine Learning 2024
Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, and Nicholas Papernot. Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. TMLR 2024
Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, and Maksims Volkovs. Data-Efficient Multimodal Fusion on a Single GPU. Computer Vision and Pattern Recognition Conference 2024
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. International Conference on Learning Representations 2024
Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, and Jesse C. Cresswell. Neural Implicit Manifold Learning for Topology-Aware Generative Modelling. TMLR 2024
George Stein, Jesse C. Cresswell, Rasa Hosseingzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-Ganem. Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. In Advances in Neural Information Processing Systems, volume 36, 2023
Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, and Hamid R. Tizhoosh. Decentralized federated learning through proxy model sharing. Nature Communications 14, 2899, 2023.
Bradley C.A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, and Gabriel Loaiza-Ganem. Verifying the Union of Manifolds Hypothesis for Image Data. International Conference on Learning Representations 2023.
Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, and Jesse C. Cresswell. Disparate Impact in Differential Privacy from Gradient Misalignment. International Conference on Learning Representations 2023
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, and Anthony L. Caterini. Denoising Deep Generative Models. NeurIPS 2022 Workshop on Understanding Deep Learning Through Empirical Falsification.
Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-Gonzalez, Marco Letizia, and Anthony L. Caterini. CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds. NeurIPS 2022 Workshop on Machine Learning and the Physical Sciences.
Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross, and Jesse C. Cresswell. Find Your Friends: Personalized Federated Learning with the Right Collaborators. NeurIPS 2022 Workshop on Federated Learning: Recent Advances and New Challanges
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, and Anthony L. Caterini. Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. TMLR 2022
Mohammed Adnan, Shivam Kalra, Jesse C. Cresswell, Graham W. Taylor, and Hamid R. Tizhoosh. Federated Learning and Differential Privacy for Medical Image Analysis. Nature Scientific Reports, 12, 1953, 2022
Brendan Leigh Ross and Jesse C. Cresswell. Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows. In Advances in Neural Information Processing Systems, volume 34, 2021
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. International Conference on Learning Representations
Ilan Tzitrin, Aaron Z. Goldberg, and Jesse C. Cresswell, Operational symmetries of entangled states. J. Phys. A: Math. Theor. 53 095304, 2021
Jesse C. Cresswell, Quantum Information Approaches to Quantum Gravity. University of Toronto Doctoral Thesis
Jesse C. Cresswell, Ian T. Jardine, and Amanda W. Peet. Holographic relations for OPE blocks in excited states. JHEP 2019 3, 58
Jesse C. Cresswell, Ilan Tzitrin, and Aaron Z. Goldberg. Perturbative expansion of entanglement negativity using patterned matrix calculus. Phys. Rev. A 99 012322, 2019
Jesse C. Cresswell. Universal entanglement timescale for Rényi entropies. Phys. Rev. A 97 022317, 2018
Jesse C. Cresswell, Amanda W. Peet. Kinematic space for conical defects. JHEP 11 (2017) 155
Jesse C. Cresswell and Dan N. Vollick. Lorenz gauge quantization in conformally flat spacetimes. Phys. Rev. D 91, 084008, 2015
Talk at ABA Risk and Compliance Conference, Seattle, USA
Talk at Roche Knowledge Sharing Workshop, Toronto, Ontario, Canada
Talk at Students in Data Science and Statistics Union, University of Toronto, Toronto, Ontario, Canada
Talk at University of Toronto, Toronto, Ontario, Canada
Talk at Toronto Machine Learning Summit, Toronto, Ontario, Canada
Talk at NeurIPS 2022 Workshop on Algorithmic Fairness through the Lens of Causality and Privacy, New Orleans, Louisiana, USA
Talk at Toronto Machine Learning Summit, Toronto, Ontario, Canada
Talk at ML4Jets, New Brunswick, New Jersey, USA
Talk at Big Data & AI Conference, Toronto, Ontario, Canada
Talk at Machine Learning Research Group, University of Guelph, Guelph, Ontario, Canada
Talk at Endless Summer School: Healthcare Roundup, Vector Institute, Toronto, Ontario, Canada
Talk at Big Data & AI Conference, Toronto, Ontario, Canada
Talk at Visitor Speaker Series, Vector Institute, Toronto, Ontario, Canada
Talk at Data Science Speaker Series, Canadian Statistical Sciences Institute, University of Toronto, Toronto, Ontario, Canada
Talk at It From Qubit Workshop 2019, Kyoto University, Kyoto, Japan
Talk at Prospects in Theoretical Physics 2018, Institute for Advanced Study, Princeton, New Jersey, USA
Talk at USU Strings and Black Holes Workshop, Utah State University, Logan, Utah, USA
Talk at Seoul National University, Seoul, South Korea
Talk at Strings 2017, Tel Aviv, Israel