Published in 43rd International Conference on Machine Learning (ICML 2026), 2026
Recommended citation: (2026). "From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints." Fourty-third International Conference on Machine Learning (ICML 2026)
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Published in ICLR-New Frontiers in Associative Memories workshop, 2026
Learning to learn input output associations.
Recommended citation: Maoutsa, D.(2026). "Learning to learn dynamical associations with reward-gated local plasticity." International Conference for Learning Representations-New Frontiers in Associative Memories workshop.
Published in ICLR-Geometry-grounded Representation Learning and Generative Modeling workshop, 2026
Geometric motivation of the sparse SDE inference method.
Recommended citation: Maoutsa, D.(2026). "Learning to learn dynamical associations with reward-gated local plasticity." International Conference for Learning Representations-Geometry-grounded Representation Learning and Generative Modeling workshop.
Published in NeurIPS-WiML workshop, 2025
Proposed a method for biologically plaussible training of RNNs in reinforcement learning settings.
Recommended citation: Maoutsa, D.(2025). "Discovering neo-Hebbian plasticity rules for reward-driven training of RNNs." NeurIPS-WiML workshop.
Published in NeurIPS 2025 - Symmetry and Geometry in Neural Representations workshop, 2025
Proposed a method for meta-learning three-factor plasticity rules for biologically plaussible training of RNNs in reinforcement learning settings.
Recommended citation: Maoutsa, D.(2025). "Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback." NeurIPS-NeurReps workshop.
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Published in (in preparation), 2024
Recommended citation: Maoutsa, D; Gjorgjieva, J. (in preparation). "Identifying plasticity mechanisms underlying experience-driven adaptation in cortical circuits." (in preparation). .
Published in ICLR workshop Physics for Machine Learning, 2023
Recommended citation: Maoutsa, D. (2022). "Geometric constraints improve inference of sparsely observed stochastic dynamics." ICLR workshop Physics for Machine Learning.
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Published in Technical University of Berlin, 2023
PhD dissertation.
Recommended citation: Maoutsa, D. (2023). "Deterministic particle flows for stochastic nonlinear systems: Simulation, Control, and Inference." Technical University of Berlin. 1(3).
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Published in NeurIPS workshop Machine Learning for the physical sciences, 2022
Recommended citation: Maoutsa, D. (2022). "Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems." NeurIPS workshop Machine Learning for the physical sciences. 4.4 (2022): 043035.
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Published in Physical Review Research, 2022
Recommended citation: Maoutsa, D; Opper, M. (2022). "Deterministic particle flows for constraining stochastic nonlinear systems." Physical Review Research. 4.4 (2022): 043035.
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Published in NeurIPS workshop Machine Learning for the physical sciences, 2021
Non-iterative stochastic control framework based on deterministic particle dynamics.
Recommended citation: Maoutsa, D; Opper, M. (2021). "Deterministic particle flows for constraining SDEs." NeurIPS workshop Machine Learning for the physical sciences. 1(3).
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Published in Entropy, 2020
Introduced an interacting particle system with purely deterministic dynamics that provides accurate Fokker–Planck solutions for diffusive systems.
Recommended citation: Maoutsa, D; Reich, S; Opper, M. (2020). "Interacting particle solutions of Fokker–Planck equations through gradient–log–density estimation." Entropy. 22.8 (2020): 802.
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Published in Physical Review Letters, 2018
Inference method for identifying synaptic interactions from spiking patterns based on a mapping of the spiking activity onto event spaces, where the connectivity can be estimated locally by approximating the interspike interval generating function.
Recommended citation: Casadiego*, J; Maoutsa*, D; Timme, M. (2018). "Inferring network connectivity from event timing patterns." Physical Review Letters. 121.5 (2018): 054101.
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Published in (in preparation), 2024
Recommended citation: Adell Segarra,R; Festa, D; Maoutsa, D; Gjorgjieva, J. (in preparation). "Plasticity-driven circuit self-organization on spiking stabilized supralinear networks." (in preparation).
Published in Neuron, 2021
Brainhack community paper.
Recommended citation: Rémi Gau*, Stephanie Noble*, Katja Heuer*, Katherine L Bottenhorn*, Isil P Bilgin*, Yu-Fang Yang*, Julia M Huntenburg*, Johanna MM Bayer*, Richard AI Bethlehem*, et al. (The Brainhack Community). (2021). "Brainhack: Developing a culture of open, inclusive, community-driven neuroscience." Neuron. 1(3).
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