Recent publications
Article
Eryilmaz, OB, Katar, C & Little, MA 2026, 'Flow-aware ellipsoidal filtration for persistent homology of recurrent signals', Chaos, vol. 36, no. 3, 033140. https://doi.org/10.1063/5.0317749
Evers, LJW, Raykov, YP, Heskes, TM, Krijthe, JH, Bloem, BR & Little, MA 2025, 'Passive Monitoring of Parkinson Tremor in Daily Life: A Prototypical Network Approach', Sensors, vol. 25, no. 2, 366. https://doi.org/10.3390/s25020366
Post, E, Laarhoven, TV, Raykov, YP, Little, MA, Nonnekes, J, Heskes, TM, Bloem, BR & Evers, LJW 2025, 'Quantifying arm swing in Parkinson’s disease: a method accounting for arm activities during free-living gait', Journal of neuroengineering and rehabilitation, vol. 22, no. 1, 22. https://doi.org/10.1186/s12984-025-01578-z
Farooq, A, Raykov, YP, Raykov, P & Little, MA 2024, 'Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction', Journal of Machine Learning Research, vol. 25, no. 135, pp. 1-42. <http://jmlr.org/papers/v25/21-0146.html>
Little, MA, He, X & Kayas, U 2024, 'Polymorphic dynamic programming by algebraic shortcut fusion', Formal Aspects of Computing, vol. 36, no. 2, 11. https://doi.org/10.1145/3664828
Little, MA 2022, 'GRAPL: A computational library for nonparametric structural causal modelling, analysis and inference', Journal of Open Source Software, vol. 7, no. 76, 4534. https://doi.org/10.21105/joss.04534
Conference contribution
He, X & Little, M 2026, An efficient, provably optimal algorithm for the 0-1 loss linear classification problem. in The Fourteenth International Conference on Learning Representations: ICLR 2026. International Conference on Learning Representations, ICLR, Fourteenth International Conference on Learning Representations, Rio de Janeiro, Brazil, 23/04/26.
He, X, Miao, Y & Little, M 2026, Deep-ICE: The first globally optimal algorithm for empirical risk minimization of two-layer maxout and ReLU networks. in The Fourteenth International Conference on Learning Representations: ICLR 2026. International Conference on Learning Representations, ICLR, Fourteenth International Conference on Learning Representations, Rio de Janeiro, Brazil, 23/04/26.
Eryilmaz, O, Katar, C & Little, M 2025, Ellipsoidal Filtration for Topological Denoising of Recurrent Signals. in 2025 International Symposium on Nonlinear Theory and Its Applications. IEICE proceeding series, Institute of Electronics, Information and Communication Engineers, pp. 610-613, The 2025 International Symposium on Nonlinear Theory and Its Applications , Okinawa, Japan, 27/10/25. <https://arxiv.org/abs/2510.16682>
Poster
Mao, J & Little, M 2026, 'A Deconfounding Method for Reverse Causal Inference Using Causally Weighted Gaussian Mixture Models', HDR UK Early Career Researcher (ECR) Conference 2026, London, United Kingdom, 21/04/26 - 21/04/26.
Mao, J & Little, M 2026, 'A Weighted Resampling Framework for Causal Transportability', EUROPEAN CAUSAL INFERENCE MEETING 2026, Oxford, United Kingdom, 15/04/26 - 17/04/26.
Zakar, N, Aloyayri, A & Little, M 2026, 'Causal Identification via DAG and ADMG Simplification in Wearable Parkinson’s Disease Studies', EUROPEAN CAUSAL INFERENCE MEETING 2026, Oxford, United Kingdom, 15/04/26 - 17/04/26.
Eryilmaz, O, Katar, C & Little, M 2025, 'Ellipsoidal Filtration for Topological Denoising of Quasi-Periodic Signals', Dynamics Days Europe 2025, Thessaloniki, Greece, 23/06/25 - 27/06/25.
Preprint
Mao, J & Little, MA 2025 'Front-door Reducibility: Reducing ADMGs to the Standard Front-door Setting via a Graphical Criterion' arXiv. https://doi.org/10.48550/arXiv.2511.15679
Mao, J & Little, MA 2024 'Mechanism Learning: reverse causal inference in the presence of multiple unknown confounding through causally weighted Gaussian mixture models' arXiv. https://doi.org/10.48550/arXiv.2410.20057
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