Google Scholar profile

Submitted/Arxived

C. Dellaporta*, P. O’Hara*, T. Damoulas, Distributionally Robust Optimisation with Bayesian Ambiguity Sets, https://arxiv.org/abs/2409.03492 

C. Dellaporta, T. Damoulas, Robust Bayesian Inference for Berkson and Classical Measurement Error Models, https://arxiv.org/abs/2306.01468

A. Boustati, T. Damoulas, R. Savage, Non-linear Multitask Learning with Deep Gaussian Processes, https://arxiv.org/abs/1905.12407 

D. J. Tait, T. Damoulas, Variational Autoencoding of PDE Inverse Problems, https://arxiv.org/abs/2006.15641

P. O'Hara, M.S. Ramanujan, T. Damoulas, On the Constrained Least-cost Tour Problem, https://arxiv.org/abs/1906.07754

Accepted/Published

[79] O. Hamelijnck, A. Solin, T. Damoulas, Physics-Informed Variational State-Space Gaussian Processes, NeurIPS 2024.
https://arxiv.org/abs/2409.13876 

[78] I. Zachos, M. Girolami, T. Damoulas, Generating Origin-Destination Matrices in Neural Spatial Interaction Models, NeurIPS 2024. https://arxiv.org/abs/2410.07352

[77] Dyer, J., Bishop, N., Felekis, Y., Zennaro, F. M., Calinescu, A., Damoulas, T., & Wooldridge, M. (2023). Interventionally Consistent Surrogates for Agent-based Simulators. NeurIPS 2024.
https://arxiv.org/abs/2312.11158.

[76] Ayyangatu Kuzhiyil, J., Damoulas, T. and Widanage, W.D., Neural Equivalent Circuit Models: Universal Differential Equations for Battery Modelling, Applied Energy, 2024.

[75] F. M. Zennaro, N. Bishop, J. Dyer, Y. Felekis, A. Calinescu, M. Wooldridge, T. Damoulas, Causally Abstracted Multi-armed Bandits, UAI 2024 (oral), https://arxiv.org/abs/2404.17493

74] Klami, Arto; Damoulas, Theodoros; Engkvist, Ola; Rinke, Patrick; Kaski, Samuel, Virtual Laboratories: Transforming research with AI. Data Centric Engineering, 2024, https://doi.org/10.36227/techrxiv.20412540.v1Link opens in a new window

[73] Y. Felekis, F.M. Zennaro, N. Branchini, and T. Damoulas. Causal Optimal Transport of Abstractions, Causal Learning and Reasoning, (CLeaR 2024).

[72] I. Zachos, T. Damoulas, M. Girolami, Table Inference for Combinatorial Origin-Destination Choices in Agent-based Population Synthesis, https://arxiv.org/abs/2307.02184, to appear, Stat 2024

[71] F. Zennaro, P. Turrini, T. Damoulas, Quantifying Consistency and Information Loss for Causal Abstraction Learning, 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023).

[70] N. Branchini, V. Aglietti, N. Dhir, T. Damoulas, Causal Entropy Optimization, 26th International Conference on Artificial Intelligence and Statistics, (AISTATS 2023).

[69] F. Zennaro, M. Dravucz, G. Apachitei, D. Widanage, T. Damoulas, Jointly learning consistent causal abstractions over multiple interventional distributions, Causal Learning and Reasoning, (CLeaR 2023). Accepted as Oral.

[68] S. Perera, V. Aglietti, T. Damoulas, On the Competitive Facility Location problem with a Bayesian Spatial Interaction Model, Journal of the Royal Statistical Society, Series C, (JRSS-C), 2023.

[67] Y. Zhang, O. Akyildiz, T. Damoulas, S. Sabanis, Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization, Journal of Applied Mathematics and Optimization, 2022, https://arxiv.org/abs/1910.02008 

[66] F. Zennaro, P. Turrini, T. Damoulas, Towards computing an optimal abstraction for structural causal models, Causal Representation Learning workshop at UAI 2022.

[65] Jaskari J, Sahlsten J, Damoulas T, Knoblauch J, Särkkä S, Kärkkäinen L, Hietala K, Kaski KK. Uncertainty-aware deep learning methods for robust diabetic retinopathy classification. IEEE Access. 2022 Jul 18;10:76669-81.

[64] J. Walsh, O. Kesa, A. Wang, M. Ilas, P. O'Hara, O. Giles, N. Dhir, M. Girolami, T. Damoulas, Near Real-Time Social Distancing in London, https://arxiv.org/abs/2012.07751, The Computer Journal, 2022. Best Paper Award

[63] C. Dellaporta, J. Knoblauch, T. Damoulas, F-X Briol, Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap, 25th International Conference on Artificial Intelligence and Statistics, (AISTATS 2022). Best Paper Award

[62] G. Felekis, T. Damoulas, B. Paige, Probabilistic Deep Learning with Generalised Variational Inference, 4th Symposium on Advances in Approximate Bayesian Inference, (AABI 2022).

[61] J. Knoblauch, J. Jewson, T. Damoulas, Generalized Variational Inference, Journal of Machine Learning Research, Journal of Machine Learning Research (JMLR). 23(132):1-09, 2022.

[60] H. Dellaporta, J. Knoblauch, T. Damoulas, F-X Briol, Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap, Your Model is Wrong: Robustness and misspecification in probabilistic modeling, Neural Information Processing Systems, (WP @ NeurIPS 2021).

[59] V. Aglietti, N. Dhir, J. Gonzalez, T. Damoulas, Dynamic Causal Bayesian OptimisationLink opens in a new window, Thirty-fifth Conference on Neural Information Processing Systems, (NeurIPS 2021).

[58] O. Hamelijnck, W. J. Wilkinson, N. A. Loppi, A. Solin, T. Damoulas, Spatio-Temporal Variational Gaussian ProcessesLink opens in a new window, Thirty-fifth Conference on Neural Information Processing Systems, (NeurIPS 2021).

[57] C. Salvi, M. Lemercier, C. Liu, B. Horvath, T. Damoulas, T. Lyons, Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes, Thirty-fifth Conference on Neural Information Processing Systems, (NeurIPS 2021).

[56] M. Lemercier, C. Salvi, T. Cass, E. Bonilla, T. Damoulas, T. Lyons, Scaling Sparse Gaussian Processes on Sequential DataLink opens in a new window, Thirty-eighth International Conference on Machine Learning, (ICML 2021).

[55] J. Maronas, O. Hamelijnck, J. Knoblauch, T. Damoulas, Transforming Gaussian Processes With Normalizing Flows, 24th International Conference on Artificial Intelligence and Statistics, (AISTATS 2021).

[54] Ö. D. Akyildiz, GJJ van den Burg, T. Damoulas, MFJ Steel, Probabilistic sequential matrix factorisation, 24th International Conference on Artificial Intelligence and Statistics, (AISTATS 2021).

[53] M. Lemercier, C. Salvi, T. Damoulas, EV Bonilla, T. Lyons, Distribution Regression for Continuous-Time Processes via the Expected Signature, 24th International Conference on Artificial Intelligence and Statistics, (AISTATS 2021).

[52] D. Tait, F. Planella, T. Damoulas, D. Widanalage, Scalable Multitask Latent Force Models with Applications to Predicting Lithium-ion Concentration, Machine Learning for Engineering Modeling, Simulation, and Design workshop, Neural Information Processing Systems, (ML4Eng @ NeurIPS 2020).

[51] C. Haycock, E. Thorpe-Woods, J. Walsh, P. O'Hara, O. Giles, N. Dhir, T. Damoulas An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System, Machine Learning in Public Health workshop, Neural Information Processing Systems, (ML4PH @ NeurIPS 2020).

[50] D. Armstrong, J. Gamper, T. Damoulas, Exoplanet Validation with Machine Learning: 50 new validated Kepler planets, Monthly Notices of the Royal Astronomical Society, 2020.

[49] H. McKay, N. Griffiths, P Taylor, T. Damoulas, Z. Xu, Bi-directional online transfer learning: a framework, Annals of Telecommunications 75 (9), 523-547, 2020.

[48] V. Aglietti, T. Damoulas, M. Alvarez, J. Gonzalez, Multitask Causal Learning with Gaussian ProcessesLink opens in a new window, Thirty-fourth Conference on Neural Information Processing Systems, (NeurIPS 2020).

[47] A. Boustati, O. Akyildiz, T. Damoulas, A. Johansen, Generalised Bayesian Filtering with Sequential Monte CarloLink opens in a new window, Thirty-fourth Conference on Neural Information Processing Systems, (NeurIPS 2020).

[46] K. Wang, O. Hamelijnck, T. Damoulas, M. Steel, Nonstationary Nonseparable Random FieldsLink opens in a new window, Thirty-seventh International Conference on Machine Learning, (ICML 2020).

[45] K. Monterrubio-Gómez, L. Roininen, S. Wade, T. Damoulas, M. Girolami, Posterior Inference for Sparse Hierarchical Non-stationary Models, https://arxiv.org/abs/1804.01431, Computational Statistics and Data Analysis, 106954 (148), (2020).

[44] H. Crosby, T. Damoulas, S.A. Jarvis, Road and travel time cross-validation for urban modelling, International Journal of Geographical Information Science, 34 (1), 98-118, (IJGIS 2020).

[43] O. Hamelijnck, T. Damoulas, K. Wang, M. A. Girolami, Multi-resolution Multi-task Gaussian ProcessesLink opens in a new window, Thirty-third Conference on Neural Information Processing Systems, (NeurIPS 2019).

[42] V. Aglietti, E. Bonilla, T. Damoulas, S. Cripps, Structured Variational Inference in Continuous Cox Process Models, Thirty-third Conference on Neural Information Processing Systems, (NeurIPS 2019).

[41] Aglietti, V., Damoulas, T., Bonilla, E., Efficient Inference in Multi-task Cox Process Models, The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019).

[40] Perera, S., Damoulas, T., Davis, P., & Jarvis, S. (2019). Modelling Business Rates in England with Big Spatial Data. In Proceedings of SIGKDD ’19: International Workshop on Urban Computing, Alaska, USA. (WP@SIGKDD 2019)

[39] Meagher, J.P., Damoulas, T., Jones, K.E. and Girolami, M., 2018. Discussion of ''The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages'' by D. Pigoli, P.Z. Hadjipantelis, J.S. Coleman and J.A.D. Aston. To appear in the Journal of the Royal Statistical Society: Series C.

[38] J. Knoblauch, J. Jewson, T. Damoulas, Doubly Robust Bayesian Inference for Non-stationary Streaming Data with β-Divergences, Thirty-Second Annual Conference on Neural Information Processing Systems (NeurIPS 2018).

[37] A. Tsakalidis, M. Liakata, T. Damoulas, A. Cristea, Can we assess mental health through social media and smart devices? Addressing bias in methodology and evaluation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML 2018).  

[36] J. Knoblauch, T. Damoulas, Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection, 35th International Conference on Machine Learning (ICML 2018).  

[35] H. Crosby, T. Damoulas, S.A. Jarvis, Embedding road networks and travel time into distance metrics for urban modelling, International Journal of Geographical Information Science, 1-25, (IJGIS 2018).

[34] J. Meagher, T. Damoulas, K. Jones, M. Girolami, Phylogenetic Gaussian processes for the ancestral reconstruction of bat echolocation calls. In Statistical Data Science, Chapter 7, 111-124, (2018).

[33] Chuah, E., Jhumka, A., Alt, S., Damoulas, T., Gurumdimma, N., Sawley, M.-C., Barth, W. L., Minyard, T., and Browne, J. C. (2017). Enabling dependability-driven resource use and message log-analysis for cluster system diagnosis. In 2017 IEEE 24th International Conference on High Performance Computing, pp. 317-327, (HiPC 2017).

[32] Crosby, H., Damoulas, T., Caton, A., Jarvis, S., and Davis, P. (2017). Road distance and travel time for an improved house price Kriging predictor. Geo-spatial Information Science 21 (3), 185-194, 2018. Preliminary version appeared at the Royal Geographical Society (RGS-IBG 2017).

[31] F. Chirigati, H. Doraiswamy, T. Damoulas, J. Freire, Data polygamy: The many-many relationships among urban spatio-temporal data sets, Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data, (SIGMOD 2016).

[30] A. Tsakalidis, M. Liakata, T. Damoulas, B. Jellinek, W. Guo, A. Cristea, Combining Heterogeneous User Generated Data to Sense Well-being, 26th International Conference on Computational Linguistics, (COLING 2016).

[29] H. Crosby, P. Davis, T. Damoulas, S. A. Jarvis, A Spatio-Temporal, Gaussian Process Regression, Real-Estate Price Predictor, Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, short paper, (SIGSPATIAL 2016).

[28] A Chohlas-Wood*, A Merali*, W Reed*, T Damoulas, Mining 911 Calls in New York City: Temporal Patterns, Detection, and Forecasting, AI for Cities Workshop in the 29th AAAI Conference on Artificial Intelligence, (WP @ AAAI 2015).

[27] S Ermon, Y Xue, R Toth, B Dilkina, R Bernstein, T Damoulas, P Clark, S. DeGloria, A. Mude, C. Barrett, C Gomes, Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa, 29th AAAI Conference on Artificial Intelligence, (AAAI 2015).

[26] T Damoulas, J He, R Bernstein, C Gomes, A Arora, String Kernels for Complex Time-Series: Counting Targets from Sensed Movement, 22nd International Conference on Pattern Recognition (ICPR 2014).

[25] H Doraiswamy, N Ferreira, T Damoulas, J Freire, C Silva, Using Topological Analysis to Support Event-Guided Exploration in Urban Data, IEEE Transactions on Visualization and Computer Graphics, 20:12, 2014.

[24] D Fink, T Damoulas, NE Bruns*, FA La Sorte, WM Hochachka, CP Gomes, S Kelling, Crowdsourcing meets ecology: hemisphere-wide spatiotemporal species distribution models, AI Magazine 35 (2), 19-30, 2014.

[23] BL Sullivan, JL Aycrigg, JH Barry, RE Bonney, N Bruns, CB Cooper, T Damoulas, AA Dhondt, T Dietterich, A Farnsworth, D Fink, JW Fitzpatrick, T Fredericks, J Gerbracht, C Gomes, WM Hochachka, MJ Iliff, C Lagoze, FA La Sorte, M Merrifield, W Morris, TB Phillips, M Reynolds, AD Rodewald, KV Rosenberg, NM Trautmann, A Wiggins, DW Winkler, WK Wong, CL Wood, J Yu, S Kelling, The eBird enterprise: an integrated approach to development and application of citizen science, Biological Conservation, 169, 31-40, 2014.

[22] D Fink, T Damoulas, J Dave*, Adaptive Spatio-Temporal Exploratory Models: Hemisphere-wide species distributions from massively crowdsourced eBird data, 27th AAAI Conference on Artificial Intelligence (AAAI 2013).

[21] RG Pearson, SJ Phillips, MM Loranty, PSA Beck, T Damoulas, SJ Knight, Scott J Goetz, Shifts in Arctic vegetation and associated feedbacks under climate change, Nature Climate Change, 3:7, 673-677, 2013. Front Cover.

Publicity: Scientific American, Smithsonian, dailymail

[20] Y Xue, B Dilkina, T Damoulas, D Fink, C Gomes, S Kelling, Improving Your Chances: Boosting Citizen Science Discovery, 1st AAAI Conference on Human Computation and Crowdsourcing, (HCOMP 2013).

[19] S Kelling, J Gerbracht, D Fink, C Lagoze, WK Wong, J Yu, T Damoulas, C Gomes, eBird: A Human/Computer Learning Network for Biodiversity Conservation and Research, (AAAI-IAAI 2012).  Best Deployed Application Award.

[18] S Kelling, J Gerbracht, D Fink, C Lagoze, WK Wong, J Yu, T Damoulas, C Gomes, A human/computer learning network to improve biodiversity conservation and research, AI magazine, 34 (1), 2012. Front Cover.

[17] R LeBras, T Damoulas, JM Gregoire, A Sabharwal, C Gomes, RB Van Dover, Constraint reasoning and kernel clustering for pattern decomposition with scaling, Principles and Practice of Constraint Programming, (CP 2011).

[16] B Dilkina, T Damoulas, C Gomes, D Fink, AL2: learning for active learning, Machine Learning for Sustainability workshop in the 25th Conference of Neural Information Processing Systems (WP @ NIPS 2011).

[15] T Polajnar, T Damoulas, M Girolami, Protein interaction sentence detection using multiple semantic kernels, Journal of Biomedical Semantics 2, 1, 2011.

[14] S Kelling, R Cook, T Damoulas, D Fink, J Freire, W Hochachka, W Michener, K Rosenberg, C Silva, Estimating Species Distributions, Across Space Through Time and with Features of the Environment, Data Intensive Research, Editors: M. Atkinson and P. Brezany, 2011.

[13] T Damoulas, S Henry*, A Farnsworth, M Lanzone, C Gomes, Bayesian classification of flight calls with a novel dynamic time warping kernel, 9th International Conference on Machine Learning and Applications (ICMLA 2010). Best Paper Award

[12] R Le Bras, T Damoulas, J Gregoire, A Sabharwal, C Gomes, RB van Dover, Computational Thinking for Material Discovery: Bridging Constraint Reasoning and Learning, 2nd International Workshop on Constraint Reasoning and Optimization for Computational Sustainability, (CROCS 2010).

[11] I Psorakis*, T Damoulas, MA Girolami, Multiclass relevance vector machines: sparsity and accuracy, IEEE Transactions on Neural Networks, 21 (10), 1588-1598, 2010.

[10] T Damoulas, Probabilistic multiple kernel learningLink opens in a new window, PhD thesis, Classification Society Distinguished Dissertation Award, 2009. Codes

[9] T Damoulas, MA Girolami, Combining feature spaces for classification, Pattern Recognition 42 (11), 2671-2683, 2009.

[8] T Damoulas, MA Girolami, Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection, Bioinformatics 24 (10), 1264-1270, 2009.  

[7] T Damoulas, MA Girolami, Pattern recognition with a Bayesian kernel combination machine, Pattern Recognition Letters 30 (1), 46-54, 2009.

[6] T. Damoulas, M. A. Girolami and S. Rogers, Preliminary Analysis of Multiple Kernel Learning: Flat Maxima, Diversity and Fisher Information, Understanding Multiple Kernel Learning Methods workshop, (WP @ NIPS 2009).

[5] C. He, T. Damoulas and M. A. Girolami, Self - Service Terminals, US Patent 7,942,315, 2009.

[4] T Damoulas, Y Ying, MA Girolami, C Campbell, Inferring sparse kernel combinations and relevance vectors: an application to subcellular localization of proteins, 7th International Conference on Machine Learning and Applications, 2008. (ICMLA 2008).

[3] T Damoulas, MA Girolami, Combining Information with a Bayesian Multi-class Multi-kernel Pattern Recognition Machine, Machine Interpretation of Patterns: Image Analysis, Data Mining and Bioinformatics. Editors: R. K. De, D. P. Mandal and A. Ghosh, Indian Statistical Institute, World Scientific Review, 2008.

[2] T Damoulas, I Cos-Aguilera, G Hayes, Valency as a mechanism for agent adaptation, Proceedings of Towards Autonomous Robotic Systems (TAROS 2005).

[1] T Damoulas, I Cos-Aguilera, GM Hayes, T Taylor, Valency for adaptive homeostatic agents: Relating evolution and learning, Proc. Adv. Artif. Life: 8th Eur. Conf., Berlin, Germany, 2005, vol. 3636, LNAI, pp. 936–945 (ECAL 2005).

* = Supervised Master's student at the time of submission.