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publications
Undersmoothing Causal Estimators With Generative Trees
Published in IEEE Access, 2024
Using Generative Trees to generate new samples and undersmooth downstream causal estimators.
Recommended citation: Machlanski, D., Samothrakis, S., Clarke, P. (2024). "Undersmoothing Causal Estimators With Generative Trees." IEEE Access. vol. 12, pp. 38562-38574.
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Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice
Published in Third Conference on Causal Learning and Reasoning (CLeaR), 2024
Investigating robustness of causal discovery methods to (bad) hyperparameter selection.
Recommended citation: Machlanski, D., Samothrakis, S., Clarke, P. (2024). "Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice." Proceedings of the Third Conference on Causal Learning and Reasoning. PMLR 236:703-739.
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Rethinking Chronological Causal Discovery with Signal Processing
Published in 59th Asilomar Conference on Signals, Systems, and Computers (2025), 2025
Diffusion-based causal discovery for temporal data by integrating multiple causal orderings.
Recommended citation: Butler, K., Machlanski, D., Dimitrakopoulos, P., & Tsaftaris, S. A. (2025, November). "Rethinking Chronological Causal Discovery with Signal Processing." In 2025 59th Asilomar Conference on Signals, Systems, and Computers. Institute of Electrical and Electronics Engineers.
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Causal Ordering for Structure Learning from Time Series
Published in Transactions on Machine Learning Research, 2025
Diffusion-based causal discovery for temporal data by integrating multiple causal orderings.
Recommended citation: Sanchez, P., Machlanski, D., McDonagh, S., & Tsaftaris, S. A. (2025). "Causal Ordering for Structure Learning from Time Series." Transactions on Machine Learning Research.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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