220 citations · 724 across the 45 of their papers we have counts for
8 papers · 1 filter
Metrizing Fairness
Yves Rychener, Bahar Taskesen, Daniel Kuhn
We study supervised learning problems that have significant effects on individuals from two demographic groups, and we seek predictors that are fair with respect to a group fairnes…
Robust Generalization despite Distribution Shift via Minimum Discriminating Information
Tobias Sutter, Andreas Krause, Daniel Kuhn
Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to train…
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen, Man-Chung Yue, Jose Blanchet +2
Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additi…
Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution
Bahar Taskesen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn
Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and a generic (possibly non-discrete) probability measure, are believed to be c…
A Statistical Test for Probabilistic Fairness
Bahar Taskesen, Jose Blanchet, Daniel Kuhn +1
Algorithms are now routinely used to make consequential decisions that affect human lives. Examples include college admissions, medical interventions or law enforcement. While algo…
A Distributionally Robust Approach to Fair Classification
Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn +1
We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnic…
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue +2
The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications.…
RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical Systems
Ekaterina Abramova, Luke Dickens, Daniel Kuhn +1
Nonlinear optimal control problems are often solved with numerical methods that require knowledge of system's dynamics which may be difficult to infer, and that carry a large compu…