@inproceedings{
Hu2020Empirical,
title={Empirical Bayes Transductive Meta-Learning with Synthetic Gradients},
author={Shell Xu Hu and Pablo Moreno and Yang Xiao and Xi Shen and Guillaume Obozinski and Neil Lawrence and Andreas Damianou},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Hkg-xgrYvH}
}
@article{ORLbenchmarks,
title={ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems},
author={Balaji, Bharathan and Bell-Masterson, Jordan and Bilgin, Enes and Damianou, Andreas and Garcia Moreno, Pablo and Jain, Arpit and Luo, Runfei and Maggiar, Alvaro and Narayanaswamy, Balakrishnan and Ye, Chun},
journal={arXiv preprint arXiv:1911.10641,
year={2019}
}
@article{ahn2019variational,
title={Variational Information Distillation for Knowledge Transfer},
author={Ahn, Sungsoo and Hu, Shell Xu and Damianou, Andreas and Lawrence, Neil and Dai, Zhenwen},
journal={arXiv preprint arXiv:1904.05835,
year={2019}
}
@inproceedings{
flennerhag2018transferring,
title={Transferring Knowledge across Learning Processes},
author={Sebastian Flennerhag and Pablo Garcia Moreno and Neil Lawrence and Andreas Damianou},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=HygBZnRctX},
}
@article{flennerhag2018transferArxiv,
title={Transferring Knowledge across Learning Processes},
author={Flennerhag, Sebastian and Moreno, Pablo G and Lawrence, Neil D and Damianou, Andreas},
journal={arXiv preprint arXiv:1812.01054},
year={2018}
}
@article{multifidelityDGP,
title={Deep Gaussian Processes for Multi-fidelity Modeling},
author={Cutajar, Kurt and Pullin, Mark and Damianou, Andreas and Lawrence, Neil and Gonzalez, Javier},
journal={NeurIPS workshop on Bayesian deep learning},
year={2018}
}
@article{variationaldistillation,
Sungsoo Ahn, Shell X. Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai.
title={Variational Mutual Information Distillation for Transfer Learning},
author={Ahn, Sungsoo and Hu, Xu Shell and Damianou, Andreas and Lawrence, Neil D and Dai, Zhenwen},
journal={NeurIPS workshop on Continual Learning},
year={2018}
}
@article{betabnn,
title={beta-BNN: A Rate-Distortion Perspective on Bayesian Neural Networks},
author={Hu, Xu and Moreno, Pablo G and Lawrence, Neil D and Damianou, Andreas},
journal={NeurIPS workshop on Bayesian deep learning},
year={2018}
}
@article{CDGP18,
title={Deep {G}aussian Processes with Convolutional Kernels},
author={Kumar, Vinayak and Singh, Vaibhav and Srijith, P. K and Damianou, Andreas},
journal={arXiv preprint arXiv:1806.01655},
year={2018}
}
@inproceedings{yang2018leveraging,
title={Leveraging Crowdsourcing Data For Deep Active Learning An Application: Learning Intents in {A}lexa},
author={Yang, Jie and Drake, Thomas and Damianou, Andreas and Maarek, Yoelle},
booktitle={Proceedings of the 2018 World Wide Web Conference on World Wide Web},
pages={23--32},
year={2018},
organization={International World Wide Web Conferences Steering Committee}
}
@article{mattos2017deep,
title={Deep recurrent Gaussian processes for outlier-robust system identification},
author={Mattos, C{\'e}sar Lincoln C and Dai, Zhenwen and Damianou, Andreas and Barreto, Guilherme A and Lawrence, Neil D},
journal={Journal of Process Control},
year={2017},
publisher={Elsevier}
}
@article{dgpirl,
title={Inverse Reinforcement Learning via Deep Gaussian Process},
author={Jin, Ming and Damianou, Andreas and Abbeel, Pieter and Spanos, Costas},
journal={33rd Conference on Uncertainty in Artificial Intelligence (UAI)},
year={2017}
}
@article{onlineconstrained,
title={Online Constrained Model-based Reinforcement Learning},
author={Van Niekerk, Benjamin and Damianou, Andreas and Rosman, Benjamin},
journal={33rd Conference on Uncertainty in Artificial Intelligence (UAI)},
year={2017}
}
@article{gonzalez2017preferential,
title={Preferential Bayesian Optimization},
author={Gonzalez, Javier and Dai, Zhenwen and Damianou, Andreas and Lawrence, Neil D},
booktitle = {Proceedings of the 34th International Conference on Machine Learning (ICML-17)},
series = {ICML '17},
year={2017}
}
@inproceedings{perdikaris2017nonlinear,
title={Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling},
author={Perdikaris, P and Raissi, M and Damianou, A and Lawrence, ND and Karniadakis, GE},
booktitle={Proc. R. Soc. A},
volume={473},
number={2198},
pages={20160751},
year={2017},
organization={The Royal Society}
}
@article{damianou2017manifold,
title={Manifold Alignment Determination: finding correspondences across different data views},
author={Damianou, Andreas and Lawrence, Neil D and Ek, Carl Henrik},
journal={arXiv preprint arXiv:1701.03449},
year={2017}
}
@article{Gonzalez2016Pairwise,
title={{B}ayesian Optimisation with Pairwise Preferential Returns},
author={Gonzalez, J and Dai, Z and Damianou, A and Lawrence, N },
booktitle={{NIPS} workshop on {B}ayesian {O}ptimization},
year={2016}
}
@inproceedings{martinez2016integrated,
title={An integrated probabilistic framework for robot perception, learning and memory},
author={Martinez-Hernandez, U and Damianou, A and Camilleri, D and Boorman, LW and Lawrence, N and Prescott, AJ},
booktitle={2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)},
year={2016},
organization={IEEE}
}
@article{damianou2016IBFA,
title={Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis},
author={Damianou, Andreas and Lawrence, Neil D and Ek, Carl Henrik},
journal={arXiv preprint arXiv:1604.04939},
year={2016}
}
@article{Mattos:LatentAutoregressive16,
title={Latent Autoregressive {G}aussian Process Models for Robust System Identification},
author={Mattos, C{\'e}sar Lincoln C and Damianou, Andreas and Barreto, Guilherme A and Lawrence, Neil},
journal={11th IFAC Symposium on Dynamics and Control of Process System (DYCOPS)},
year={2016}
}
@article{Dai:VAEDGP16,
title={Variational Auto-encoded Deep {G}aussian Processes},
author={Dai, Zhenwen and Damianou, Andreas and Gonz{\'a}lez, Javier and Lawrence, Neil},
journal={International Conference on Learning Representations (ICLR)},
year={2016}
}
@article{mattos:RGP16,
title={Recurrent {G}aussian Processes},
author={Mattos, C{\'e}sar Lincoln C and Dai, Zhenwen and Damianou, Andreas and Forth, Jeremy and Barreto, Guilherme A and Lawrence, Neil D},
journal={International Conference on Learning Representations (ICLR)},
year={2016}
}
@inproceedings{bekiroglu2016probabilistic,
title={Probabilistic consolidation of grasp experience},
author={Bekiroglu, Yasemin and Damianou, Andreas and Detry, Renaud and Stork, Johannes A and Kragic, Danica and Ek, Carl Henrik},
booktitle={Robotics and Automation (ICRA), 2016 IEEE International Conference on},
pages={193--200},
year={2016},
organization={IEEE}
}
@article{damianou:MAD15,
title={Manifold Alignment Determination},
author={Damianou, Andreas and Lawrence, Neil D and Ek, Carl Henrik},
journal={NIPS workshop on Multi-Modal Machine Learning},
year={2015}
}
@article{damianou:thesis15,
title={Deep Gaussian Processes and Variational Propagation of Uncertainty},
author={Damianou, Andreas},
journal={PhD Thesis, University of Sheffield},
year={2015},
publisher={University of Sheffield}
}
@article{JMLR:v17:damianou16a,
author = {Andreas C. Damianou and Michalis K. Titsias and Neil D. Lawrence},
title = {Variational Inference for Latent Variables and Uncertain Inputs in {G}aussian Processes},
journal = {Journal of Machine Learning Research},
year = {2016},
volume = {17},
number = {42},
pages = {1-62},
url = {http://jmlr.org/papers/v17/damianou16a.html}
}
@article{Martinez:cognitiveIROS15,
title={Cognitive architecture for robot perception and learning based on human-robot interaction},
author={Uriel Martinez-Hernandez and Luke Boorman and Andreas Damianou and Tony Prescott},
journal={IEEE/RSJ IROS workshop on Learning Object Affordances: a fundamental step to allow prediction, planning and tool use?},
year={2015}
}
@inproceedings{Damianou:topdown15,
title={A top-down approach for a synthetic autobiographical memory system},
author={Damianou, Andreas and Ek, Carl Henrik and Boorman, Luke and Lawrence, Neil and Prescott, Tony},
booktitle={4th International Conference on Biomimetic and Biohybrid Systems (Living Machines)},
year={2015}
}
@inproceedings{Boorman:hippocampal15,
title={Extending a hippocampal model for navigation around a maze generated from real-world data},
author={Boorman, Luke and Damianou, Andreas and Martinez-Hernandez, Uriel and Prescott, Tony},
booktitle={4th International Conference on Biomimetic and Biohybrid Systems (Living Machines)},
year={2015}
}
@inproceedings{Damianou:semidescribed15,
title={Semi-described and semi-supervised learning with {G}aussian processes},
author={Damianou, Andreas and Lawrence, Neil},
booktitle={31st Conference on Uncertainty in Artificial Intelligence (UAI)},
year={2015}
}
@article{Dai:Parallel14,
title={Gaussian Process Models with Parallelization and {GPU} acceleration},
author={Dai, Zhenwen and Damianou, Andreas and Hensman, James and Lawrence, Neil},
journal={arXiv preprint arXiv:1410.4984},
year={2014}
}
@article{Damianou:variational14,
title={Variational Inference for Uncertainty on the Inputs of {G}aussian Process Models},
author={Andreas C. Damianou and Michalis K. and Titsias and Neil D. Lawrence},
journal={ar{X}iv preprint ar{X}iv:1409.2287},
year={2014}
}
@inproceedings{vasisht2014active,
title={Active learning for sparse bayesian multilabel classification},
author={Vasisht, Deepak and Damianou, Andreas and Varma, Manik and Kapoor, Ashish},
booktitle={Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining},
pages={472--481},
year={2014},
organization={ACM}
}
@article{hensman:deepGPLargeData,
title={Opening the way for deep {G}aussian processes on massive data},
author={Hensman, James and Damianou, Andreas and Lawrence, Neil},
url={http://staffwww.dcs.shef.ac.uk/people/A.Damianou/papers/deepGPsLargeDataAbstract_AISTATS14.pdf},
year={2014},
journal={AISTATS, Late Breaking Poster},
}
@InProceedings{Damianou:deepGPs13,
author = {Andreas Damianou and Neil Lawrence},
title = {Deep {G}aussian Processes},
booktitle = {Proceedings of the Sixteenth International Workshop on Artificial Intelligence and Statistics (AISTATS)},
series = {AISTATS '13},
year = {2013},
editor = {C. Carvalho and P. Ravikumar},
location = {Arizona, USA},
publisher = {JMLR W\&CP 31},
pages= {207--215},
}
@InProceedings{Damianou:mrd12,
author = {Andreas Damianou and Carl Ek and Michalis Titsias and Neil Lawrence},
title = {Manifold Relevance Determination},
booktitle = {Proceedings of the 29th International Conference on Machine Learning (ICML-12)},
series = {ICML '12},
year = {2012},
editor = {John Langford and Joelle Pineau},
location = {Edinburgh, Scotland, GB},
isbn = {978-1-4503-1285-1},
month = {July},
publisher = {Omnipress},
address = {New York, NY, USA},
pages= {145--152},
}
@article{DBLP:journals/corr/abs-1301-3461,
author = {Cheng Zhang and
Carl Henrik Ek and
Andreas Damianou and
Hedvig Kjellstr{\"o}m},
title = {Factorized Topic Models},
journal = {CoRR},
volume = {abs/1301.3461},
year = {2013},
ee = {http://arxiv.org/abs/1301.3461},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
@incollection{Damianou:vgpds11,
title ={Variational {G}aussian Process Dynamical Systems},
author={Andreas C. Damianou and Michalis Titsias and Neil D. Lawrence},
booktitle = {Advances in Neural Information Processing Systems 24},
editor = {J. Shawe-Taylor and R.S. Zemel and P. Bartlett and F.C.N. Pereira and K.Q. Weinberger},
pages = {2510--2518},
year = {2011}
}
@article{damianou2009visual,
title={Visual Object Categorization using Topic Models},
author={Damianou, Andreas},
journal={Master of Science Thesis, School of Informatics, University of Edinburgh},
year={2009},
publisher={School of Informatics, University of Edinburgh}
}