publications

2024

1. 

   Generator Matching: Generative modeling with arbitrary Markov processes

   Peter Holderrieth, Marton Havasi, Jason Yim, and 6 more authors

   Preprint, 2024

   Bib

   @article{holderrieth2024gm,
     title = {Generator Matching: Generative modeling with arbitrary Markov processes},
     author = {Holderrieth, Peter and Havasi, Marton and Yim, Jason and Shaul, Neta and Gat, Itai and Jaakkola, Tommi and Karrer, Brian and Chen, Ricky and Lipman, Yaron},
     journal = {Preprint},
     url = {http://arxiv.org/abs/2410.20587},
     year = {2024},
   }

2. 

   Hamiltonian Score Matching and Generative Flows

   Peter Holderrieth, Yilun Xu, and Tommi Jaakkola

   NeurIPS, 2024

   Bib

   @article{holderrieth2024hsm,
     title = {Hamiltonian Score Matching and Generative Flows},
     author = {Holderrieth, Peter and Xu, Yilun and Jaakkola, Tommi},
     journal = {NeurIPS},
     url = {https://arxiv.org/abs/2410.20470},
     year = {2024},
   }

2022

1. 

   Transfer Learning for Neuroimaging via Re-use of Deep Neural Network Features

   Peter Holderrieth, Stephen Smith, and Han Peng

   medRxiv, 2022

2021

1. 

   Equivariant learning of stochastic fields: Gaussian processes and steerable conditional neural processes

   Peter Holderrieth, Michael J Hutchinson, and Yee Whye Teh

   In International Conference on Machine Learning, 2021

2020

1. 

   Cores for piecewise-deterministic Markov processes used in Markov chain Monte Carlo

   Peter Holderrieth

   Electronic Communications in Probability, 2020

   Bib

   @article{holderrieth2021cores,
     title = {Cores for piecewise-deterministic Markov processes used in Markov chain Monte Carlo},
     author = {Holderrieth, Peter},
     journal = {Electronic Communications in Probability},
     volume = {26},
     pages = {1--12},
     year = {2020},
     publisher = {The Institute of Mathematical Statistics and the Bernoulli Society},
   }