Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark. - Manda Winlaw; Michael B. Hynes; Anthony L. Caterini; Hans De Sterck (ICPADS, 2015)

A Geometric Framework for Convolutional Neural Networks. - Anthony L. Caterini; Dong Eui Chang (CoRR, 2016)

A Novel Representation of Neural Networks. - Anthony L. Caterini; Dong Eui Chang (CoRR, 2016)

Hamiltonian Variational Auto-Encoder. - Anthony L. Caterini; Arnaud Doucet; Dino Sejdinovic (NeurIPS, 2018)

Deep Neural Networks in a Mathematical Framework - Anthony L. Caterini; Dong Eui Chang (Springer, 2018)

Localised Generative Flows. - Robert Cornish; Anthony L. Caterini; George Deligiannidis; Arnaud Doucet (CoRR, 2019)

Detecting anthropogenic cloud perturbations with deep learning. - Duncan Watson-Parris; Samuel Sutherland; Matthew Christensen; Anthony L. Caterini; Dino Sejdinovic; Philip Stier (CoRR, 2019)

Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows. - Robert Cornish; Anthony L. Caterini; George Deligiannidis; Arnaud Doucet (ICML, 2020)

Entropic Issues in Likelihood-Based OOD Detection. - Anthony L. Caterini; Gabriel Loaiza-Ganem (ICBINB@NeurIPS, 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 (ICLR, 2021)

Rectangular Flows for Manifold Learning. - Anthony L. Caterini; Gabriel Loaiza-Ganem; Geoff Pleiss; John P. Cunningham (NeurIPS, 2021)

Variational inference with continuously-indexed normalizing flows. - Anthony L. Caterini; Robert Cornish; Dino Sejdinovic; Arnaud Doucet (UAI, 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)

Relating Regularization and Generalization through the Intrinsic Dimension of Activations. - Bradley C. A. Brown; Jordan Juravsky; Anthony L. Caterini; Gabriel Loaiza-Ganem (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)

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)

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)

Retrieval & Fine-Tuning for In-Context Tabular Models. - Valentin Thomas; Junwei Ma; Rasa Hosseinzadeh; Keyvan Golestan; Guangwei Yu; Maksims Volkovs; Anthony L. Caterini (NeurIPS, 2024)

In-Context Data Distillation with TabPFN. - Junwei Ma; Valentin Thomas; Guangwei Yu; Anthony L. Caterini (CoRR, 2024)

TabPFGen - Tabular Data Generation with TabPFN. - Junwei Ma; Apoorv Dankar; George Stein; Guangwei Yu; Anthony L. Caterini (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)

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)