End-to-End Learning for Stochastic Optimization: A Bayesian Perspective Y Rychener, D Kuhn, T Sutter arXiv preprint arXiv:2306.04174, 2023 | 6 | 2023 |
On the Granularity of Explanations in Model Agnostic NLP Interpretability Y Rychener, X Renard, D Seddah, P Frossard, M Detyniecki Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 6* | 2022 |
Metrizing fairness Y Rychener, B Taskesen, D Kuhn arXiv preprint arXiv:2205.15049, 2022 | 5 | 2022 |
Quackie: A NLP classification task with ground truth explanations Y Rychener, X Renard, D Seddah, P Frossard, M Detyniecki arXiv preprint arXiv:2012.13190, 2020 | 3 | 2020 |
Wasserstein Distributionally Robust Optimization with Heterogeneous Data Sources Y Rychener, A Esteban-Perez, JM Morales, D Kuhn arXiv preprint arXiv:2407.13582, 2024 | | 2024 |
A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set MC Yue, Y Rychener, D Kuhn, VA Nguyen arXiv preprint arXiv:2405.20124, 2024 | | 2024 |
RAO Y Hu, W Jongeneel, C Kocyigit, D Kuhn, D Lauinger, M Li, ... | | |