End-to-End Learning for Stochastic Optimization: A Bayesian Perspective von Rychener, Yves, Kuhn, Daniel, Sutter, Tobias

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Wasserstein Distributionally Robust Optimization with Heterogeneous Data Sources von Rychener, Yves, Esteban-Perez, Adrian, Morales, Juan M, Kuhn, Daniel

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A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set von Yue, Man-Chung, Rychener, Yves, Kuhn, Daniel, Nguyen, Viet Anh

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Metrizing Fairness von Rychener, Yves, Taskesen, Bahar, Kuhn, Daniel

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Metrizing Fairness von Rychener, Yves, Taskesen, Bahar, Kuhn, Daniel

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End-to-End Learning for Stochastic Optimization: A Bayesian Perspective von Rychener, Yves, Kuhn, Daniel, Sutter, Tobias

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QUACKIE: A NLP Classification Task With Ground Truth Explanations von Rychener, Yves, Renard, Xavier, Seddah, Djamé, Frossard, Pascal, Detyniecki, Marcin

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On the Granularity of Explanations in Model Agnostic NLP Interpretability von Rychener, Yves, Renard, Xavier, Seddah, Djamé, Frossard, Pascal, Detyniecki, Marcin

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Wasserstein Distributionally Robust Optimization with Heterogeneous Data Sources von Rychener, Yves, Esteban-Perez, Adrian, Morales, Juan M, Kuhn, Daniel

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On the Granularity of Explanations in Model Agnostic NLP Interpretability von Rychener, Yves, Renard, Xavier, Seddah, Djamé, Frossard, Pascal, Detyniecki, Marcin

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QUACKIE: A NLP Classification Task With Ground Truth Explanations von Rychener, Yves, Renard, Xavier, Seddah, Djamé, Frossard, Pascal, Detyniecki, Marcin

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