Tim G. J. Rudner

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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, variational inference, and reinforcement learning. I am particularly interested in uncertainty quantification in deep learning, reinforcement learning as probabilistic inference, and probabilistic transfer 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 an AI Fellow at Georgetown University’s Center for Security and Emerging Technology, a Fellow of the German Academic Scholarship Foundation, and a Rhodes Scholar.

Publications

2019

• M. Fellows , A. Mahajan , T. G. J. Rudner , S. Whiteson , VIREL: A Variational Inference Framework for Reinforcement Learning, in Advances in Neural Information Processing Systems 32, 2019.
  
  BibTeX

  arXiv

  @inproceedings{Fellows:etal:2019c,
    author = {Fellows, Matthew and Mahajan, Anuj and Rudner, Tim G. J. and Whiteson, Shimon},
    title = {{VIREL}: {A} {V}ariational {I}nference {F}ramework for {R}einforcement {L}earning},
    journal = {Advances in Neural Information Processing Systems 32},
    year = {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, 2019.
  
  BibTeX

  arXiv

  @inproceedings{Rudner:etal:2019b,
    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},
    journal = {Proceedings of the Thirty-Three {AAAI} Conference on Artificial Intelligence},
    year = {2019}
  }
  

• M. Samvelyan , T. Rashid , C. Witt , G. Farquhar , N. Nardelli , T. G. J. Rudner , C. Hung , P. H. S. Torr , J. Foerster , S. Whiteson , The StarCraft Multi-Agent Challenge, in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019.
  
  BibTeX

  arXiv

  @inproceedings{Samvelyan:etal:2019a,
    author = {Samvelyan, Mikayel and Rashid, Tabish and Schroeder de Witt, Christian and Farquhar, Gregory and Nardelli, Nantas and Rudner, Tim G. J. and Hung, Chia-Man and Torr, Philip H. S. and Foerster, Jakob and Whiteson, Shimon},
    title = {{T}he {S}tar{C}raft {M}ulti-{A}gent {C}hallenge},
    journal = {Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems},
    year = {2019}
  }
  

2018

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

  URL

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

2017

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

  URL

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