20 citations · 29 across the 11 of their papers we have counts for
12 papers
A Mathematical Theory of Understanding
Bahar Taşkesen
Generative AI has transformed the economics of information production, making explanations, proofs, examples, and analyses available at very low cost. Yet the value of information…
Optimality of Linear Policies in Distributionally Robust Linear Quadratic Control
Bahar Taşkesen, Dan A. Iancu, Çağıl Koçyiğit +1
We study a generalization of the classical discrete-time, Linear-Quadratic-Gaussian (LQG) control problem where the noise distributions affecting the states and observations are un…
Optimal Transport on Lie Group Orbits
Bahar Taskesen
In its most general form, the optimal problem is an infinite-dimensional optimization problem, yet certain notable instances admit closed-form solutions. We identify the common sou…
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits
Mengmeng Li, Daniel Kuhn, Bahar Taşkesen
Follow-The-Regularized-Leader (FTRL) algorithms often enjoy optimal regret for adversarial as well as stochastic bandit problems and allow for a streamlined analysis. Nonetheless,…
Unifying Distributionally Robust Optimization via Optimal Transport Theory
Jose Blanchet, Daniel Kuhn, Jiajin Li +1
In recent years, two prominent paradigms have shaped distributionally robust optimization (DRO), modeling distributional ambiguity through -divergences and Wasserstein distances…
Distributionally Robust Linear Quadratic Control
Bahar Taşkesen, Dan A. Iancu, Çağıl Koçyiğit +1
Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It i…
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…
Discrete Optimal Transport with Independent Marginals is #P-Hard
Bahar Taşkesen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn +1
We study the computational complexity of the optimal transport problem that evaluates the Wasserstein distance between the distributions of two K-dimensional discrete random vector…
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…