Supervised Clustering in the Data Cube. - Vincent Roulet; Fajwel Fogel; Alexandre d'Aspremont; Francis R. Bach (CoRR, 2015)

Integration Methods and Optimization Algorithms. - Damien Scieur; Vincent Roulet; Francis R. Bach; Alexandre d'Aspremont (NIPS, 2017)

Sur la géométrie de problèmes d'optimisation et leur structure. (On the geometry of optimization problems and their structure). - Vincent Roulet (2017)

A Smoother Way to Train Structured Prediction Models. - Venkata Krishna Pillutla; Vincent Roulet; Sham M. Kakade; Zaïd Harchaoui (NeurIPS, 2018)

An Elementary Approach to Convergence Guarantees of Optimization Algorithms for Deep Networks. - Vincent Roulet; Zaïd Harchaoui (Allerton, 2019)

Iterative Linearized Control: Stable Algorithms and Complexity Guarantees. - Vincent Roulet; Dmitriy Drusvyatskiy; Siddhartha S. Srinivasa; Zaïd Harchaoui (ICML, 2019)

Kernel-based Translations of Convolutional Networks. - Corinne Jones; Vincent Roulet; Zaïd Harchaoui (CoRR, 2019)

End-to-end Learning, with or without Labels. - Corinne Jones; Vincent Roulet; Zaïd Harchaoui (CoRR, 2019)

On the Convergence of the Iterative Linear Exponential Quadratic Gaussian Algorithm to Stationary Points. - Vincent Roulet; Maryam Fazel; Siddhartha S. Srinivasa; Zaïd Harchaoui (ACC, 2020)

Sharpness, Restart, and Acceleration. - Vincent Roulet; Alexandre d'Aspremont (SIAM J. Optim., 2020)

On the Smoothing of Deep Networks. - Vincent Roulet; Zaïd Harchaoui (CISS, 2021)

Target Propagation via Regularized Inversion. - Vincent Roulet; Zaïd Harchaoui (CoRR, 2021)

Differentiable Programming A La Moreau. - Vincent Roulet; Zaïd Harchaoui (ICASSP, 2022)

Discriminative clustering with representation learning with any ratio of labeled to unlabeled data. - Corinne Jones; Vincent Roulet; Zaïd Harchaoui (Stat. Comput., 2022)

Stochastic Optimization for Spectral Risk Measures. - Ronak Mehta; Vincent Roulet; Krishna Pillutla; Lang Liu; Zaïd Harchaoui (AISTATS, 2023)

Modified Gauss-Newton Algorithms under Noise. - Krishna Pillutla; Vincent Roulet; Sham M. Kakade; Zaïd Harchaoui (SSP, 2023)

Dual Gauss-Newton Directions for Deep Learning. - Vincent Roulet; Mathieu Blondel (CoRR, 2023)

On the Interplay Between Stepsize Tuning and Progressive Sharpening. - Vincent Roulet; Atish Agarwala; Fabian Pedregosa (CoRR, 2023)

Revisiting Convolutional Neural Networks from the Viewpoint of Kernel-Based Methods. - Corinne Jones; Vincent Roulet; Zaïd Harchaoui (J. Comput. Graph. Stat., 2023)

Target Propagation via Regularized Inversion for Recurrent Neural Networks. - Vincent Roulet; Zaïd Harchaoui (Trans. Mach. Learn. Res., 2023)

Distributionally Robust Optimization with Bias and Variance Reduction. - Ronak Mehta; Vincent Roulet; Krishna Pillutla; Zaïd Harchaoui (ICLR, 2024)

Stepping on the Edge: Curvature Aware Learning Rate Tuners. - Vincent Roulet; Atish Agarwala; Jean-Bastien Grill; Grzegorz Swirszcz; Mathieu Blondel; Fabian Pedregosa (NeurIPS, 2024)

The Elements of Differentiable Programming. - Mathieu Blondel; Vincent Roulet (CoRR, 2024)

Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates. - Vincent Roulet; Siddhartha S. Srinivasa; Maryam Fazel; Zaïd Harchaoui (Open J. Math. Optim., 2024)

How far away are truly hyperparameter-free learning algorithms? - Priya Kasimbeg; Vincent Roulet; Naman Agarwal; Sourabh Medapati; Fabian Pedregosa; Atish Agarwala; George E. Dahl (Trans. Mach. Learn. Res., 2025)

Joint Learning of Energy-based Models and their Partition Function. - Michael E. Sander; Vincent Roulet; Tianlin Liu; Mathieu Blondel (CoRR, 2025)

Loss Functions and Operators Generated by f-Divergences. - Vincent Roulet; Tianlin Liu; Nino Vieillard; Michael E. Sander; Mathieu Blondel (CoRR, 2025)