Publications
See also my Google Scholar page.
F. Tonolini, P. G. Moreno, A. Damianou, R. MurraySmith (2021)
"Tomographic AutoEncoder: Unsupervised Bayesian Recovery of Corrupted Data."
ICLR [PDF] 
W. Maddox, S. Tang, P. Moreno, A. Wilson, A. Damianou (2021)
"Fast Adaptation with Linearized Neural Networks."
AISTATS 
S. Tang, W. Maddox, C. Dickens, T. Diethe, A. Damianou (2020)
"Similarity of Neural Networks with Gradients."
arXiv [PDF] 
X. Hu, P. Moreno, X. Shen, Y. Xiao, N. Lawrence, G. Obozinski, A. Damianou (2020)
"Empirical Bayes Transductive MetaLearning with Synthetic Gradients."
ICLR [PDF] [bib] 
B. Balaji, J. BellMasterson, E. Bilgin, A. Damianou, P. Moreno, A. Jain, R. Luo, A. Maggiar, B. Narayanaswamy, C. Ye (2019)
"ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems."
arXiv [PDF] [bib] 
W. Maddox, S. Tang, P. Moreno, A. Wilson, A. Damianou (2019)
"On Transfer Learning via Linearized Neural Networks."
NeurIPS metalearning workshop. [PDF] [bib] 
X. Hu, P. Moreno, X. Shen, Y. Xiao, N. Lawrence, G. Obozinski, A. Damianou (2019)
"Empirical Bayes MetaLearning with Synthetic Gradients."
NeurIPS metalearning workshop. [PDF] [bib] 
S. Ahn, X. Hu, A. Damianou, N. Lawrence, Z. Dai (2019)
"Variational information distillation for knowledge transfer."
CVPR [PDF] [bib] 
S. Flennerhag, P. Moreno, N. Lawrence, A. Damianou (2018)
"Transferring Knowledge across Learning Processes."
International Conference on Learning Representations (ICLR) [Oral presentation]. [PDF] [bib] 
X. Hu, P. Moreno, N. Lawrence, A. Damianou (2018)
"βBNN: A RateDistortion Perspective on Bayesian Neural Networks."
NeurIPS workshop on Bayesian deep learning. [PDF] [bib] 
S. Flennerhag, P. Moreno, N. Lawrence, A. Damianou (2018)
"Transferring Knowledge across Learning Processes."
NeurIPS workshop on metalearning. 
S. Ahn, X. Hu, A. Damianou, N. Lawrence, Z. Dai (2018)
"Variational Mutual Information Distillation for Transfer Learning."
NeurIPS workshop on continual learning. [PDF] [bib] 
K. Cutajar, M. Pullin, A. Damianou, N. Lawrence, K. Gonzalez (2018)
"Deep Gaussian Processes for Multifidelity Modeling."
NeurIPS workshop on Bayesian deep learning. [PDF] [bib] 
V. Kumar, V. Singh, P. K. Srijith, A. Damianou (2018)
"Deep Gaussian Processes with Convolutional Kernels."
UAI workshop on uncertainty in deep learning. arXiv version: [PDF] [bib] 
A. J. Prescott, D. Camilleri, U. MartinezHernandez, A. Damianou, N. Lawrence (2019)
"Memory and mental time travel in humans and social robots."
Philosophical Transactions B: Biological Sciences 
J. Yang, T. Drake, A. Damianou, Y. Maarek (2018)
"Leveraging Crowdsourcing Data For Deep Active Learning  An Application: Learning Intents in Alexa."
The Web Conference (WWW 2018) [PDF] [bib] 
C. L. Mattos, Z. Dai, A. Damianou, G. Barreto, N. Lawrence (2017)
"Deep recurrent Gaussian processes for outlierrobust system identification."
Journal of Process Control [PDF] [bib] 
M. Jin, A. Damianou, P. Abbeel, C. Spanos (2017)
"Inverse Reinforcement Learning via Deep Gaussian Process."
Uncertainty in Artificial Intelligence (UAI), 2017. Preprint: arXiv:1512.08065 [PDF] [bib] 
B. Van Niekerk, A. Damianou, B. Rosman (2017)
"Online Constrained Modelbased Reinforcement Learning."
Uncertainty in Artificial Intelligence (UAI) [Oral Presentation], 2017. [PDF] [bib] 
J. Gonzalez, Z. Dai, A. Damianou, N. Lawrence (2017)
"Preferential Bayesian Optimization."
International Conference on Machine Learning (ICML) [PDF] [bib] 
P. Perdikaris, M. Raissi, A. Damianou, N. Lawrence, G. E. Karniadakis (2017)
"Nonlinear information fusion algorithms for robust multifidelity modeling."
Proceedings of the Royal Society A. [PDF] [bib] 
C. MoulinFrier*, T. Fischer*, M. Petit, G. Pointeau, J. Puigbo, U. Pattacini, S. Low, D. Camilleri, P. Nguyen, M. Hoffmann, H. Chang, M. Zambelli, A. Mealier, A. Damianou, G. Metta, T. Prescott, Y. Demiris, P. Dominey, P. Verschure (2017)
"DACh3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self."
IEEE Transactions on Cognitive and Developmental Systems. Preprint: arXiv:1706.03661 [PDF] 
J. Gonzalez, Z. Dai, A. Damianou, N. Lawrence (2016)
"Bayesian Optimisation with Pairwise Preferential Returns."
NIPS workshop on Bayesian Optimization. [PDF] [bib] 
A. Damianou, N. Lawrence, C. Ek. (2017)
"Manifold Alignment Determination: finding correspondences across different data views."
arXiv [PDF] [bib] 
A. Damianou, N. Lawrence, C. H. Ek. (2016)
"Multiview Learning as a Nonparametric Nonlinear InterBattery Factor Analysis."
arXiv. [PDF] [bib] 
C. wa Maina, M. Smith, E. Mwebaze, A. Damianou, M. Mubangizi, J. Quinn, N. Lawrence (2016)
"Data Science Africa  An Initiative to Bridge the Data Science Skills Gap in Africa."
SciDataCon 
D. Camilleri, A. Damianou, H. Jackson, N. Lawrence, T. Prescott. (2016)
"iCub Visual Memory Inspector: Visualising the iCub's Thoughts."
5th International Conference on Biomimetic and Biohybrid Systems (Living Machines). 
D. Camilleri, L. Boorman, U. Martinez, A. Damianou, T. Prescott. (2016)
"A Bioinspired Approach to Vision."
The 17th Towards Autonomous Robotic Systems conference (TAROS). 
U. MartinezHernandez, A. Damianou, D. Camilleri, L. W. Boorman, N. Lawrence, T. J. Prescott. (2016)
"Integrated probabilistic framework for robot perception, learning and memory."
2015 IEEE International Conference on Robotics and Biomimetics (ROBIO). [bib] 
C. L. Mattos, A. Damianou, G. Barreto, N. Lawrence. (2016)
"Latent Autoregressive Gaussian Process Models for Robust System Identification."
11th IFAC Symposium on Dynamics and Control of Process System [Oral presentation] (DYCOPS).
[PDF] [bib] 
Z. Dai, A. Damianou, J. Gonzalez, N. Lawrence. (2016)
"Variational Autoencoded Deep Gaussian Processes."
International Conference on Learning Representations (ICLR). [PDF] [bib] 
C. Mattos, Z. Dai, A. Damianou, N. Lawrence. (2016)
"Recurrent Gaussian Processes."
International Conference on Learning Representations (ICLR). [PDF] [bib] 
A. Damianou*, M. Titsias* and N. Lawrence. (2016)
"Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes."
Journal of Machine Learning Research (JMLR). [PDF] [bib]
(Previous version entitled: "Variational Inference for Uncertainty on the Inputs of Gaussian Process Models")

Y. Bekiroglu, A. Damianou, R. Detry, J. A. Stork, D. Kragic, C. H. Ek. (2016)
"Probabilistic Consolidation of Grasp Experience."
IEEE Conference on Robotics and Automation (ICRA). [PDF] [bib] 
A. Damianou, N. Lawrence, C. Ek. (2015)
"Manifold Alignment Determination."
NIPS workshop on MultiModal Machine Learning [PDF] [bib] 
U. MartinezHernandez, L. Boorman, A. Damianou, and T. Prescott. (2015)
"Cognitive architecture for robot perception and learning based on humanrobot interaction"
IEEE/RSJ IROS 2015 Workshops [PDF] [bib] 
A. Damianou and N. Lawrence. (2015)
"Semidescribed and semisupervised learning with Gaussian processes."
Uncertainty in Artificial Intelligence (UAI), 2015. [PDF] [Suppl. PDF] [bib] 
A. Damianou, C. H. Ek, L. Boorman, N. Lawrence, T. Prescott. (2015)
"A topdown approach for a synthetic autobiographical memory system."
4th International Conference on Biomimetic and Biohybrid Systems (Living Machines)
(Oral presentation). [PDF preprint] [bib] 
L. Boorman, A. Damianou, U. MartinezHernandez, T. Prescott. (2015)
"Extending a hippocampal model for navigation around a maze generated from realworld data."
4th International Conference on Biomimetic and Biohybrid Systems (Living Machines). [PDF] [bib] 
A. Damianou and N. Lawrence. (2014)
"Uncertainty Propagation in Gaussian Process Pipelines."
NIPS workshop on modern nonparametrics. [PDF] [bib (conference version)] 
Z. Dai, A. Damianou, J. Hensman and N. Lawrence. (2014)
"Gaussian Process Models with Parallelization and GPU acceleration."
NIPS workshop on Software Engineering for Machine Learning. [arXiv] [bib] 
D. Vasisht, A. Damianou, M. Varma and A. Kapoor. (2014)
"Active Learning for Sparse Bayesian Multilabel Classification."
ACM SIGKDD conference on knowledge discovery and data mining, KDD. [PDF] [bib] 
A. Damianou and N. D. Lawrence. (2013)
"Deep Gaussian Processes."
AISTATS, 2013. [Abstract] [PDF] [Python] [MATLAB] [Details] [bib] 
C. Zhang, C. H. Ek, A. Damianou, H. Kjellstrom. (2013)
"Factorized Topic Models."
International Conference on Learning Representations (ICLR). [http] [bib] 
A. Damianou and N. D. Lawrence. (2012)
"Deep Gaussian Processes (workshop version)."
NIPS workshop on deep learning. 
A. Damianou, C. H. Ek, M. K. Titsias and N. D. Lawrence. (2012)
"Manifold Relevance Determination."
International Conference on Machine Learning (ICML).
[Abstract] [PDF] [Suppl. Videos] [Suppl. PDF] [Talk] [Code] [Details] [bib] 
A. Damianou, M. K. Titsias and N. D. Lawrence. (2011)
"Variational Gaussian Process Dynamical Systems."
Advances in Neural Information Processing Systems (NIPS).
[Abstract] [PDF & Appendix] [Suppl. Videos] [Code] [Details] [bib]
Other Publications

J. Hensman, A. Damianou and N. Lawrence. (2014)
"Deep Gaussian Processes for Large Datasets."
Late Breaking Poster, AISTATS. [PDF [Poster] [bib]
* Joint first author.
Theses

A. Damianou. (2015)
"Deep Gaussian Processes and Variational Propagation of Uncertainty."
PhD Thesis, The University of Sheffield. Supervised by Prof. Neil Lawrence.
[PDF] [Details] [bib]

A. Damianou. (2009)
"Visual Object Categorization using Topic Models."
M.Sc. Thesis, School of Informatics, The University of Edinburgh. Supervised by Dr. Frank Keller.
[PDF] [bib]