2 citations · 3 across the 6 of their papers we have counts for
7 papers
Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning
Alireza Abdollahpoorrostam, Ehsan Sharifian, Buse Şen +2
Distributionally robust optimization (DRO) provides a principled framework for learning under distribution shift, but its practical use is hindered by the difficulty of evaluating…
Robust Markov Decision Processes on Continuous State Spaces
Mengmeng Li, Yifan Hu, Daniel Kuhn +1
We study infinite-horizon robust Markov decision processes (MDPs) on continuous state spaces with structured rectangular ambiguity set. The proposed ambiguity set falls within the…
Multistage Conditional Compositional Optimization
Buse Şen, Yifan Hu, Daniel Kuhn
We introduce Multistage Conditional Compositional Optimization (MCCO) as a new paradigm for decision-making under uncertainty that combines aspects of multistage stochastic program…
Global Group Fairness in Federated Learning via Function Tracking
Yves Rychener, Daniel Kuhn, Yifan Hu
We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training p…
Wasserstein Distributionally Robust Optimization with Heterogeneous Data Sources
Yves Rychener, Adrian Esteban-Perez, Juan M. Morales +1
We study decision problems under uncertainty, where the decision-maker has access to data sources that carry {\em biased} information about the underlying risk factors. The bia…
A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set
Man-Chung Yue, Yves Rychener, Daniel Kuhn +1
The state-of-the-art methods for estimating high-dimensional covariance matrices all shrink the eigenvalues of the sample covariance matrix towards a data-insensitive shrinkage tar…
Contextual Stochastic Bilevel Optimization
Yifan Hu, Jie Wang, Yao Xie +2
We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on…