G. Bareilles, A. Gehret, J. Aspman, J. Lepšová, J. Mareček: Deep Learning as the Disciplined Construction of Tame Objects. arXiv. 2025.
A. Kliachkin, J. Lepšová, G. Bareilles, J. Mareček: Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networks. arXiv. 2025.
G. Bareilles, J. Fageot, L.-N. Hoang, P. Blanchard, W. Bouaziz, S. Rouault, E.-M. El-Mhamdi: On Monotonicity in AI Alignment. arXiv. 2025.
J. Fageot, P. Blanchard, G. Bareilles, L.-N. Hoang: Generalizing while preserving monotonicity in comparison-based preference learning models. Accepted at NeurIPS. 2025.
G. Bareilles, F. Iutzeler, J. Malick: Harnessing structure in composite nonsmooth minimization. SIAM Journal on Optimization. 2023.
G. Bareilles, F. Iutzeler, J. Malick: Newton acceleration on manifolds identified by proximal-gradient methods. Mathematical Programming. 2022.
G. Bareilles, Y. Laguel, D. Grishchenko, F. Iutzeler, J. Malick. Randomized Progressive Hedging methods for Multi-stage Stochastic Programming. Annals of Operations Research. 2020.
G. Bareilles, F. Iutzeler. On the Interplay between Acceleration and Identification for the Proximal Gradient algorithm. Computational Optimization and Applications. 2020.
A. Kliachkin, J. Lepšová, G. Bareilles, J. Mareček: humancompatible.train: Implementing Optimization Algorithms for Stochastically-Constrained Stochastic Optimization Problems. Accepted at Neurips COML Workshop 2025
G. Bareilles, J. Aspman, J. Němeček, J. Mareček: Piecewise Polynomial Regression of Tame Functions via Integer Programming. ICLR Workshop XAI4Science. 2025
G. Bareilles. Structured nonsmooth optimization: proximal identification, fast local convergence, and applications. Ph.D. Thesis, Univ. Grenoble Alpes, defended December 2nd, 2022. Manuscript
J. Aspman, G. Bareilles, V. Kungurtsev, J. Mareček, M. Takáč: Hybrid Methods in Polynomial Optimisation. arXiv. 2023.