OxCSML : People : Tim G. J. Rudner

Probabilistic inference, reinforcement learning, Gaussian Processes

I am a DPhil student supervised by Yee Whye Teh and Yarin Gal. My research interests span Bayesian deep learning, reinforcement learning, and variational inference. I am particularly interested in uncertainty quantification in deep learning, reinforcement learning as probabilistic inference, and probabilistic meta learning. I obtained a master’s degree in statistics from the University of Oxford and an undergraduate degree in mathematics and economics from Yale University. I am a member of the Oxford Center for Doctoral Training in Autonomous Intelligent Machines & Systems, a Fellow of the German National Academic Foundation, and a Rhodes Scholar.

Publications

2019

T. G. J. Rudner , M. Rußwurm , J. Fil , R. Pelich , B. Bischke , V. Kopackova , P. Bilinski , Multi³Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery, in Proceedings of the Thirty-Three AAAI Conference on Artificial Intelligence (AAAI), 2019.

@inproceedings{Rudner:etal:2019,
  author = {Rudner, Tim G. J. and Rußwurm, Marc and Fil, Jakub and Pelich, Ramona and Bischke, Benjamin and Kopackova, Veronika and Bilinski, Piotr},
  title = {{M}ulti³{N}et: {S}egmenting {F}looded {B}uildings via {F}usion of {M}ultiresolution, {M}ultisensor, and {M}ultitemporal {S}atellite {I}magery},
  booktitle = {Proceedings of the Thirty-Three {AAAI} Conference on Artificial Intelligence (AAAI)},
  year = {2019}
}

2018

T. G. J. Rudner , V. Fortuin , Y. W. Teh , Y. Gal , On the Connection between Neural Processes and Approximate Gaussian Processes, NIPS 2018 Workshop on Bayesian Deep Learning, 2018.

@article{Rudner:etal:2018a,
  title = {{O}n the {C}onnection between {N}eural {P}rocesses and {A}pproximate {G}aussian {P}rocesses},
  author = {Rudner, Tim G. J. and Fortuin, Vincent and Teh, Yee Whye and Gal, Yarin},
  journal = {NIPS 2018 Workshop on Bayesian Deep Learning},
  year = {2018}
}

M. Fellows , A. Mahajan , T. G. J. Rudner , S. Whiteson , VIREL: A Variational Inference Framework for Reinforcement Learning, NIPS 2018 Workshop on Probabilistic Reinforcement Learning and Structured Control, 2018.

@article{Fellows:etal:2018b,
  title = {{VIREL}: {A} {V}ariational {I}nference {F}ramework for {R}einforcement {L}earning},
  author = {Fellows, Matthew and Mahajan, Anuj and Rudner, Tim G. J. and Whiteson, Shimon},
  journal = {NIPS 2018 Workshop on Probabilistic Reinforcement Learning and Structured Control},
  year = {2018}
}

@article{Rudner:etal:2018,
  title = {{M}ulti³{N}et: {S}egmenting {F}looded {B}uildings via {F}usion of {M}ultiresolution, {M}ultisensor, and {M}ultitemporal {S}atellite {I}magery},
  author = {Rudner, Tim G. J. and Rußwurm, Marc and Fil, Jakub and Pelich, Ramona and Bischke, Benjamin and Kopackova, Veronika and Bilinski, Piotr},
  journal = {NIPS 2018 Workshop on AI for Social Good},
  year = {2018}
}

2017

T. G. J. Rudner , D. Sejdinovic , Inter-domain Deep Gaussian Processes, NIPS 2017 Workshop on Bayesian Deep Learning, 2017.

@article{Rudner:Sejdinovic:2017,
  title = {{I}nter-domain {D}eep {G}aussian {P}rocesses},
  author = {Rudner, Tim G. J. and Sejdinovic, Dino},
  journal = {NIPS 2017 Workshop on Bayesian Deep Learning},
  year = {2017}
}