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Maxime Wabartha

  1. Wabartha, M., Wilson, K., Evans, D., Sharifi-Noghabi, H. & Sylvain, T. (2025). “Investigating Action Embeddings for More Efficient Off-Policy Evaluation”. RecSys workshop on Causality, Counterfactuals & Sequential Decision-Making (CONSEQUENCES ’25).
    [paper]

  2. Wabartha, M. & Pineau, J. (2025). “Object-Centric Concept Representation and Use in RL Agents. Under revision.”

  3. Danesh, M., Wabartha, M., Pineau, J. & Lin, H. C. (2025). “Mitigating Distribution Shifts: Uncertainty-aware Offline-to-online Reinforcement Learning”. Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM ’25).
    [paper]

  4. Wabartha, M. & Pineau, J. (2024). “Piecewise Linear Parametrization of Policies for Interpretable Deep Reinforcement Learning”. International Conference on Learning Representations (ICLR ’24).
    [paper]

  5. Mangeat, G., Ouellette, R., Wabartha, M., De Leener, B., Platt ́en, M., Danylait ́e Karrenbauer, V., … & Granberg, T. (2020). “Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis”. Journal of Neuroimaging.
    [paper]

  6. Wabartha, M., Durand, A., Francois-Lavet, V., & Pineau, J. (2020). “Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks”. International Joint Conference on Artificial Intelligence, 2140-2147.
    [paper][code]

  7. Wabartha, M., Durand, A., Francois-Lavet, V., & Pineau, J. (2019). “Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks”. NeurIPS Workshop on Safety and Robustness in Decision Making.
    [code]

  8. Wabartha, M., Durand, A., Francois-Lavet, V., & Pineau, J. (2018). “Sampling diverse neural networks for exploration in reinforcement learning”. NeurIPS Workshop on Bayesian Deep Learning.
    [paper]

  9. Zaimi, A.*, Wabartha, M.*, Herman, V., Antonsanti, P. L., Perone, C. S., & Cohen-Adad, J. (2018). “AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks”. Nature Scientific reports, 8(1), 1-11.
    [paper][code]