Funnels: Exact maximum likelihood with dimensionality reduction. - Samuel Klein; John A. Raine; Sebastian Pina-Otey; Slava Voloshynovskiy; Tobias Golling (CoRR, 2021)

SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics. - Atul Kumar Sinha; Daniele Paliotta; Bálint Máté; Sebastian Pina-Otey; John A. Raine; Tobias Golling; François Fleuret (CoRR, 2022)

ν-Flows: Conditional Neutrino Regression. - Matthew Leigh; John Andrew Raine; Knut Zoch; Tobias Golling (CoRR, 2022)

Flows for Flows: Training Normalizing Flows Between Arbitrary Distributions with Maximum Likelihood Estimation. - Samuel Klein; John Andrew Raine; Tobias Golling (CoRR, 2022)

SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics. - Atul Kumar Sinha; Daniele Paliotta; Bálint Máté; John A. Raine; Tobias Golling; François Fleuret (NeurIPS, 2023)

PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics. - Matthew Leigh; Debajyoti Sengupta; Guillaume Quétant; John Andrew Raine; Knut Zoch; Tobias Golling (CoRR, 2023)

Topological Reconstruction of Particle Physics Processes using Graph Neural Networks. - Lukas Ehrke; John Andrew Raine; Knut Zoch; Manuel Guth; Tobias Golling (CoRR, 2023)

Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting. - Malte Algren; Tobias Golling; Manuel Guth; Chris Pollard; John Andrew Raine (CoRR, 2023)

CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation. - Debajyoti Sengupta; Samuel Klein; John Andrew Raine; Tobias Golling (CoRR, 2023)

ν2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows. - John Andrew Raine; Matthew Leigh; Knut Zoch; Tobias Golling (CoRR, 2023)

Decorrelation using Optimal Transport. - Malte Algren; John Andrew Raine; Tobias Golling (CoRR, 2023)

PC-Droid: Faster diffusion and improved quality for particle cloud generation. - Matthew Leigh; Debajyoti Sengupta; John Andrew Raine; Guillaume Quétant; Tobias Golling (CoRR, 2023)

SuperCalo: Calorimeter shower super-resolution. - Ian Pang; John Andrew Raine; David Shih (CoRR, 2023)

Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation. - Tobias Golling; Samuel Klein; Radha Mastandrea; Benjamin Nachman; John Andrew Raine (CoRR, 2023)

EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion. - Erik Buhmann; Cedric Ewen; Darius A. Faroughy; Tobias Golling; Gregor Kasieczka; Matthew Leigh; Guillaume Quétant; John Andrew Raine; Debajyoti Sengupta; David Shih (CoRR, 2023)

Improving new physics searches with diffusion models for event observables and jet constituents. - Debajyoti Sengupta; Matthew Leigh; John Andrew Raine; Samuel Klein; Tobias Golling (CoRR, 2023)

PIPPIN: Generating variable length full events from partons. - Guillaume Quétant; John Andrew Raine; Matthew Leigh; Debajyoti Sengupta; Tobias Golling (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)

Variational inference for pile-up removal at hadron colliders with diffusion models. - Malte Algren; Chris Pollard; John Andrew Raine; Tobias Golling (CoRR, 2024)

CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals. - John Andrew Raine; Samuel Klein; Debajyoti Sengupta; Tobias Golling (Frontiers Big Data, 2024)

Masked particle modeling on sets: towards self-supervised high energy physics foundation models. - Tobias Golling; Lukas Heinrich; Michael Kagan; Samuel Klein; Matthew Leigh; Margarita Osadchy; John Andrew Raine (Mach. Learn. Sci. Technol., 2024)