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<title>Bibliography - Andreas Damianou</title>
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<p class="margin">
<a name="SIB_ICLR"></a>
@inproceedings{<br>
Hu2020Empirical,<br>
title={Empirical Bayes Transductive Meta-Learning with Synthetic Gradients},<br>
author={Shell Xu Hu and Pablo Moreno and Yang Xiao and Xi Shen and Guillaume Obozinski and Neil Lawrence and Andreas Damianou},<br>
booktitle={International Conference on Learning Representations},<br>
year={2020},<br>
url={https://openreview.net/forum?id=Hkg-xgrYvH}<br>
}<br>
<br><br>
<a name="ORL_arxiv"></a>
@article{ORLbenchmarks,<br>
title={ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems},<br>
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},<br>
journal={arXiv preprint arXiv:1911.10641,<br>
year={2019}<br>
}<br>
<br><br>
<a name="VIDarxiv2019"></a>
@article{ahn2019variational,<br>
title={Variational Information Distillation for Knowledge Transfer},<br>
author={Ahn, Sungsoo and Hu, Shell Xu and Damianou, Andreas and Lawrence, Neil and Dai, Zhenwen},<br>
journal={arXiv preprint arXiv:1904.05835,<br>
year={2019}<br>
}<br>
<br><br>
<a name="LeapICLR"></a>
@inproceedings{<br>
flennerhag2018transferring,<br>
title={Transferring Knowledge across Learning Processes},<br>
author={Sebastian Flennerhag and Pablo Garcia Moreno and Neil Lawrence and Andreas Damianou},<br>
booktitle={International Conference on Learning Representations},<br>
year={2019},<br>
url={https://openreview.net/forum?id=HygBZnRctX},<br>
}<br>
<br><br>
<a name="LeapArxiv"></a>
@article{flennerhag2018transferArxiv,<br>
title={Transferring Knowledge across Learning Processes},<br>
author={Flennerhag, Sebastian and Moreno, Pablo G and Lawrence, Neil D and Damianou, Andreas},<br>
journal={arXiv preprint arXiv:1812.01054},<br>
year={2018}<br>
}<br>
<br><br>
<a name="betabnnworkshop"></a>
@article{multifidelityDGP,<br>
title={Deep Gaussian Processes for Multi-fidelity Modeling},<br>
author={Cutajar, Kurt and Pullin, Mark and Damianou, Andreas and Lawrence, Neil and Gonzalez, Javier},<br>
journal={NeurIPS workshop on Bayesian deep learning},<br>
year={2018}<br>
}<br>
<br><br>
<a name="infodistill"></a>
@article{variationaldistillation,<br>
Sungsoo Ahn, Shell X. Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai.
title={Variational Mutual Information Distillation for Transfer Learning},<br>
author={Ahn, Sungsoo and Hu, Xu Shell and Damianou, Andreas and Lawrence, Neil D and Dai, Zhenwen},<br>
journal={NeurIPS workshop on Continual Learning},<br>
year={2018}<br>
}<br>
<br><br>
<a name="multiDGPworkshop"></a>
@article{betabnn,<br>
title={beta-BNN: A Rate-Distortion Perspective on Bayesian Neural Networks},<br>
author={Hu, Xu and Moreno, Pablo G and Lawrence, Neil D and Damianou, Andreas},<br>
journal={NeurIPS workshop on Bayesian deep learning},<br>
year={2018}<br>
}<br>
<br><br>
<a name="CDGP18"></a>
@article{CDGP18,<br>
title={Deep {G}aussian Processes with Convolutional Kernels},<br>
author={Kumar, Vinayak and Singh, Vaibhav and Srijith, P. K and Damianou, Andreas},<br>
journal={arXiv preprint arXiv:1806.01655},<br>
year={2018}<br>
}<br>
<br><br>
<a name="yang2018leveraging"></a>
@inproceedings{yang2018leveraging,<br>
title={Leveraging Crowdsourcing Data For Deep Active Learning An Application: Learning Intents in {A}lexa},<br>
author={Yang, Jie and Drake, Thomas and Damianou, Andreas and Maarek, Yoelle},<br>
booktitle={Proceedings of the 2018 World Wide Web Conference on World Wide Web},<br>
pages={23--32},<br>
year={2018},<br>
organization={International World Wide Web Conferences Steering Committee}<br>
}<br>
<br><br>
<a name="2017RGPjournal"></a>
@article{mattos2017deep,<br>
title={Deep recurrent Gaussian processes for outlier-robust system identification},<br>
author={Mattos, C{\'e}sar Lincoln C and Dai, Zhenwen and Damianou, Andreas and Barreto, Guilherme A and Lawrence, Neil D},<br>
journal={Journal of Process Control},<br>
year={2017},<br>
publisher={Elsevier}<br>
}<br>
<br><br>
<a name="2017DGPIRL"></a>
@article{dgpirl,<br>
title={Inverse Reinforcement Learning via Deep Gaussian Process},<br>
author={Jin, Ming and Damianou, Andreas and Abbeel, Pieter and Spanos, Costas},<br>
journal={33rd Conference on Uncertainty in Artificial Intelligence (UAI)},<br>
year={2017}<br>
}<br>
<br><br>
<a name="2017OnlineConstrained"></a>
@article{onlineconstrained,<br>
title={Online Constrained Model-based Reinforcement Learning},<br>
author={Van Niekerk, Benjamin and Damianou, Andreas and Rosman, Benjamin},<br>
journal={33rd Conference on Uncertainty in Artificial Intelligence (UAI)},<br>
year={2017}<br>
}<br>
<br><br>
<a name="2017PBO"></a>
@article{gonzalez2017preferential,<br>
title={Preferential Bayesian Optimization},<br>
author={Gonzalez, Javier and Dai, Zhenwen and Damianou, Andreas and Lawrence, Neil D},<br>
booktitle = {Proceedings of the 34th International Conference on Machine Learning (ICML-17)},<br>
series = {ICML '17},<br>
year={2017}<br>
}<br>
<br><br>
<a name="2017multifidelity"></a>
@inproceedings{perdikaris2017nonlinear,<br>
title={Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling},<br>
author={Perdikaris, P and Raissi, M and Damianou, A and Lawrence, ND and Karniadakis, GE},<br>
booktitle={Proc. R. Soc. A},<br>
volume={473},<br>
number={2198},<br>
pages={20160751},<br>
year={2017},<br>
organization={The Royal Society}<br>
}<br>
<br><br>
<a name="MAD17"></a>
@article{damianou2017manifold,<br>
title={Manifold Alignment Determination: finding correspondences across different data views},<br>
author={Damianou, Andreas and Lawrence, Neil D and Ek, Carl Henrik},<br>
journal={arXiv preprint arXiv:1701.03449},<br>
year={2017}<br>
}<br>
<br><br>
<a name="2016Pairwise"></a>
@article{Gonzalez2016Pairwise,<br>
title={{B}ayesian Optimisation with Pairwise Preferential Returns},<br>
author={Gonzalez, J and Dai, Z and Damianou, A and Lawrence, N },<br>
booktitle={{NIPS} workshop on {B}ayesian {O}ptimization},<br>
year={2016}<br>
}<br>
<br><br>
<a name="2016ROBIO"></a>
@inproceedings{martinez2016integrated,<br>
title={An integrated probabilistic framework for robot perception, learning and memory},<br>
author={Martinez-Hernandez, U and Damianou, A and Camilleri, D and Boorman, LW and Lawrence, N and Prescott, AJ},<br>
booktitle={2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)},<br>
year={2016},<br>
organization={IEEE}<br>
}<br>
<br><br>
<a name="2016IBFA"></a>
@article{damianou2016IBFA,<br>
title={Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis},<br>
author={Damianou, Andreas and Lawrence, Neil D and Ek, Carl Henrik},<br>
journal={arXiv preprint arXiv:1604.04939},<br>
year={2016}<br>
}<br>
<br><br>
<a name="DYCOPS16"></a>
@article{Mattos:LatentAutoregressive16,<br>
title={Latent Autoregressive {G}aussian Process Models for Robust System Identification},<br>
author={Mattos, C{\'e}sar Lincoln C and Damianou, Andreas and Barreto, Guilherme A and Lawrence, Neil},<br>
journal={11th IFAC Symposium on Dynamics and Control of Process System (DYCOPS)},<br>
year={2016}<br>
}<br>
<br><br>
<a name="VAEDGP16"></a>
@article{Dai:VAEDGP16,<br>
title={Variational Auto-encoded Deep {G}aussian Processes},<br>
author={Dai, Zhenwen and Damianou, Andreas and Gonz{\'a}lez, Javier and Lawrence, Neil},<br>
journal={International Conference on Learning Representations (ICLR)},<br>
year={2016}<br>
}<br>
<br><br>
<a name="RGP16"></a>
@article{mattos:RGP16,<br>
title={Recurrent {G}aussian Processes},<br>
author={Mattos, C{\'e}sar Lincoln C and Dai, Zhenwen and Damianou, Andreas and Forth, Jeremy and Barreto, Guilherme A and Lawrence, Neil D},<br>
journal={International Conference on Learning Representations (ICLR)},<br>
year={2016}<br>
}<br>
<br><br>
<a name="bekiroglu2016probabilistic"></a>
@inproceedings{bekiroglu2016probabilistic,<br>
title={Probabilistic consolidation of grasp experience},<br>
author={Bekiroglu, Yasemin and Damianou, Andreas and Detry, Renaud and Stork, Johannes A and Kragic, Danica and Ek, Carl Henrik},<br>
booktitle={Robotics and Automation (ICRA), 2016 IEEE International Conference on},<br>
pages={193--200},<br>
year={2016},<br>
organization={IEEE}<br>
}<br>
<br><br>
<a name="MAD15"></a>
@article{damianou:MAD15,<br>
title={Manifold Alignment Determination},<br>
author={Damianou, Andreas and Lawrence, Neil D and Ek, Carl Henrik},<br>
journal={NIPS workshop on Multi-Modal Machine Learning},<br>
year={2015}<br>
}<br>
<br><br>
<a name="PhDThesis"></a>
@article{damianou:thesis15,<br>
title={Deep Gaussian Processes and Variational Propagation of Uncertainty},<br>
author={Damianou, Andreas},<br>
journal={PhD Thesis, University of Sheffield},<br>
year={2015},<br>
publisher={University of Sheffield}<br>
}<br>
<br><br>
<a name="variational15"></a>
@article{JMLR:v17:damianou16a,<br>
author = {Andreas C. Damianou and Michalis K. Titsias and Neil D. Lawrence},<br>
title = {Variational Inference for Latent Variables and Uncertain Inputs in {G}aussian Processes},<br>
journal = {Journal of Machine Learning Research},<br>
year = {2016},<br>
volume = {17},<br>
number = {42},<br>
pages = {1-62},<br>
url = {http://jmlr.org/papers/v17/damianou16a.html}<br>
}<br>
<br><br>
<a name="cognitiveArchitecture15"></a>
@article{Martinez:cognitiveIROS15,<br>
title={Cognitive architecture for robot perception and learning based on human-robot interaction},<br>
author={Uriel Martinez-Hernandez and Luke Boorman and Andreas Damianou and Tony Prescott},<br>
journal={IEEE/RSJ IROS workshop on Learning Object Affordances: a fundamental step to allow prediction, planning and tool use?},<br>
year={2015}<br>
}<br>
<br><br>
<a name="topdown15"></a>
@inproceedings{Damianou:topdown15,<br>
title={A top-down approach for a synthetic autobiographical memory system},<br>
author={Damianou, Andreas and Ek, Carl Henrik and Boorman, Luke and Lawrence, Neil and Prescott, Tony},<br>
booktitle={4th International Conference on Biomimetic and Biohybrid Systems (Living Machines)},<br>
year={2015}<br>
}<br>
<br><br>
<a name="hippocampal15"></a>
@inproceedings{Boorman:hippocampal15,<br>
title={Extending a hippocampal model for navigation around a maze generated from real-world data},<br>
author={Boorman, Luke and Damianou, Andreas and Martinez-Hernandez, Uriel and Prescott, Tony},<br>
booktitle={4th International Conference on Biomimetic and Biohybrid Systems (Living Machines)},<br>
year={2015}<br>
}<br>
<br><br>
<a name="semidescribed15"></a>
@inproceedings{Damianou:semidescribed15,<br>
title={Semi-described and semi-supervised learning with {G}aussian processes},<br>
author={Damianou, Andreas and Lawrence, Neil},<br>
booktitle={31st Conference on Uncertainty in Artificial Intelligence (UAI)},<br>
year={2015}<br>
}<br>
<br><br>
<a name="parallel_gpu"></a>
@article{Dai:Parallel14,<br>
title={Gaussian Process Models with Parallelization and {GPU} acceleration},<br>
author={Dai, Zhenwen and Damianou, Andreas and Hensman, James and Lawrence, Neil},<br>
journal={arXiv preprint arXiv:1410.4984},<br>
year={2014}<br>
}<br>
<br><br>
<a name="varInfereceForUncertainty"></a>
@article{Damianou:variational14,<br>
title={Variational Inference for Uncertainty on the Inputs of {G}aussian Process Models},<br>
author={Andreas C. Damianou and Michalis K. and Titsias and Neil D. Lawrence},<br>
journal={ar{X}iv preprint ar{X}iv:1409.2287},<br>
year={2014}<br>
}<br>
<br><br>
<a name="active14"></a>
@inproceedings{vasisht2014active,<br>
title={Active learning for sparse bayesian multilabel classification},<br>
author={Vasisht, Deepak and Damianou, Andreas and Varma, Manik and Kapoor, Ashish},<br>
booktitle={Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining},<br>
pages={472--481},<br>
year={2014},<br>
organization={ACM}<br>
}<br>
<br><br>
<a name="deepGPLargeLateBreaking14"></a>
@article{hensman:deepGPLargeData,<br>
title={Opening the way for deep {G}aussian processes on massive data},<br>
author={Hensman, James and Damianou, Andreas and Lawrence, Neil},<br>
url={http://staffwww.dcs.shef.ac.uk/people/A.Damianou/papers/deepGPsLargeDataAbstract_AISTATS14.pdf},<br>
year={2014},<br>
journal={AISTATS, Late Breaking Poster},<br>
}<br>
<br><br>
<a name="deepGPs"></a>
@InProceedings{Damianou:deepGPs13,<br>
author = {Andreas Damianou and Neil Lawrence},<br>
title = {Deep {G}aussian Processes},<br>
booktitle = {Proceedings of the Sixteenth International Workshop on Artificial Intelligence and Statistics (AISTATS)},<br>
series = {AISTATS '13},<br>
year = {2013},<br>
editor = {C. Carvalho and P. Ravikumar},<br>
location = {Arizona, USA},<br>
publisher = {JMLR W\&CP 31},<br>
pages= {207--215},<br>
}<br>
<br><br>
<a name="mrd"></a>
@InProceedings{Damianou:mrd12,<br>
author = {Andreas Damianou and Carl Ek and Michalis Titsias and Neil Lawrence},<br>
title = {Manifold Relevance Determination},<br>
booktitle = {Proceedings of the 29th International Conference on Machine Learning (ICML-12)},<br>
series = {ICML '12},<br>
year = {2012},<br>
editor = {John Langford and Joelle Pineau},<br>
location = {Edinburgh, Scotland, GB},<br>
isbn = {978-1-4503-1285-1},<br>
month = {July},<br>
publisher = {Omnipress},<br>
address = {New York, NY, USA},<br>
pages= {145--152},<br>
}<br>
<br><br>
<a name="factorizedTopicModels"></a>
@article{DBLP:journals/corr/abs-1301-3461, <br>
author = {Cheng Zhang and <br>
Carl Henrik Ek and <br>
Andreas Damianou and <br>
Hedvig Kjellstr{\"o}m},<br>
title = {Factorized Topic Models}, <br>
journal = {CoRR}, <br>
volume = {abs/1301.3461}, <br>
year = {2013}, <br>
ee = {http://arxiv.org/abs/1301.3461}, <br>
bibsource = {DBLP, http://dblp.uni-trier.de} <br>
}<br>
<br><br>
<a name="vgpds"></a>
<!--<PRE> <p class="margin">-->
@incollection{Damianou:vgpds11,<br>
title ={Variational {G}aussian Process Dynamical Systems},<br>
author={Andreas C. Damianou and Michalis Titsias and Neil D. Lawrence},<br>
booktitle = {Advances in Neural Information Processing Systems 24},<br>
editor = {J. Shawe-Taylor and R.S. Zemel and P. Bartlett and F.C.N. Pereira and K.Q. Weinberger},<br>
pages = {2510--2518},<br>
year = {2011}<br>
}<br>
<!--</p></PRE>-->
<br><br>
<a name="MScThesis"></a>
@article{damianou2009visual,<br>
title={Visual Object Categorization using Topic Models},<br>
author={Damianou, Andreas},<br>
journal={Master of Science Thesis, School of Informatics, University of Edinburgh},<br>
year={2009},<br>
publisher={School of Informatics, University of Edinburgh}<br>
}<br>
<br><br>
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