Publications

See also my Google Scholar page.

  • E. Palumbo, G. Penha, A. Damianou, H. Bouchard, A. Wang, F. Tomasi, J. Redondo Garcia (2024)
    "Systems and Methods for Generating a Custom Playlist based on an Input to a Machine-Learning Model"
    US Patent.

  • E. Palumbo, R. Ferreira, A. Damianou, A. Wang, G. Fazelnia, P. Ravichandran, C. Hauff, H. Bouchard, H. Pampin (2024)
    "Systems and methods for suggesting queries using a graph"
    US Patent.

  • E. Palumbo, G. Penha, A. Damianou, J. García, T. Heath, A. Wang, H. Bouchard, M. Lalmas (2024)
    "Text2Tracks: Generative Track Retrieval for Prompt-based Music Recommendation."
    ROEGEN @ RecSys 2024. [PDF]

  • A. Bacciu, E. Palumbo, A. Damianou, N. Tonellotto, F. Silvestri (2024)
    "Generating Query Recommendations via LLMs."
    IR-RAG @ SIGIR24. [PDF]

  • M. De Nadai, F. Fabbri, P. Gigioli, A. Wang, A. Li, F. Silvestri, L. Kim, S. Lin, V. Radosavljevic, S. Ghael, D. Nyhan, H. Bouchard, M. Lalmas-Roelleke, A. Damianou (2024)
    "Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks."
    The Web Conferece. [Oral presentation] [PDF][Blog]

  • A. Damianou, F. Fabbri, P. Gigioli, M. De Nadai, A. Wang, E. Palumbo, M. Lalmas (2024)
    "Towards Graph Foundation Models for Personalization."
    Graph Foundation Models Workshop @ The Web Conference. [PDF][Blog]

  • E. Palumbo, A. Damianou, A. Wang, A. Liu, G. Fazelnia, F. Fabbri, R. Ferreira, F. Silvestri, H. Bouchard, C. Hauff, M. Lalmas, B. Carterette, P. Chandar, D. Nyhan (2023)
    "Graph Learning for Exploratory Query Suggestions in an Instant Search System."
    CIKM industry track.

  • S. Kleinegesse, Z. Dai, A. Damianou, K. Ciosek, F. Tomasi. (2021)
    "Efficient Automated Online Experimentation with Multi-Fidelity."
    NeurIPS Meta-Learning workshop. [PDF]

  • A. Damianou, N. Lawrence, C. H. Ek. (2021)
    "Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis."
    Journal of Machine Learning Research (JMLR). [PDF] [bib]

  • F. Tonolini, P. G. Moreno, A. Damianou, R. Murray-Smith (2021)
    "Tomographic Auto-Encoder: 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 [PDF]

  • 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 Meta-Learning with Synthetic Gradients."
    ICLR [PDF] [bib]

  • B. Balaji, J. Bell-Masterson, 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 meta-learning workshop. [PDF] [bib]

  • X. Hu, P. Moreno, X. Shen, Y. Xiao, N. Lawrence, G. Obozinski, A. Damianou (2019)
    "Empirical Bayes Meta-Learning with Synthetic Gradients."
    NeurIPS meta-learning 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)
    "β-BNN: A Rate-Distortion 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 meta-learning.

  • 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 Multi-fidelity 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. Martinez-Hernandez, 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 outlier-robust 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 Model-based 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 multi-fidelity modeling."
    Proceedings of the Royal Society A. [PDF] [bib]

  • C. Moulin-Frier*, 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)
    "DAC-h3: 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]

  • 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. Martinez-Hernandez, 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 Auto-encoded 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 Multi-Modal Machine Learning [PDF] [bib]

  • U. Martinez-Hernandez, L. Boorman, A. Damianou, and T. Prescott. (2015)
    "Cognitive architecture for robot perception and learning based on human-robot interaction"
    IEEE/RSJ IROS 2015 Workshops [PDF] [bib]

  • A. Damianou and N. Lawrence. (2015)
    "Semi-described and semi-supervised 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 top-down 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. Martinez-Hernandez, T. Prescott. (2015)
    "Extending a hippocampal model for navigation around a maze generated from real-world 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 non-parametrics. [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]