Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Taşkesen, Bahar"'
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, FTRL algorithms require the solution of an optimization problem in eve
Externí odkaz:
http://arxiv.org/abs/2409.20440
In the past few years, there has been considerable interest in two prominent approaches for Distributionally Robust Optimization (DRO): Divergence-based and Wasserstein-based methods. The divergence approach models misspecification in terms of likeli
Externí odkaz:
http://arxiv.org/abs/2308.05414
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 involves controlling a system with linear dynamics and imperfect observ
Externí odkaz:
http://arxiv.org/abs/2305.17037
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 fairness criterion such as statistical parity (SP). A predictor is SP-fair if
Externí odkaz:
http://arxiv.org/abs/2205.15049
We study the computational complexity of the optimal transport problem that evaluates the Wasserstein distance between the distributions of two K-dimensional discrete random vectors. The best known algorithms for this problem run in polynomial time i
Externí odkaz:
http://arxiv.org/abs/2203.01161
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 additional labeled training samples from a source distribution that is close
Externí odkaz:
http://arxiv.org/abs/2106.00322
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 computationally hard. Even though such problems are ubiquitous in stati
Externí odkaz:
http://arxiv.org/abs/2103.06263
Algorithms are now routinely used to make consequential decisions that affect human lives. Examples include college admissions, medical interventions or law enforcement. While algorithms empower us to harness all information hidden in vast amounts of
Externí odkaz:
http://arxiv.org/abs/2012.04800
We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity. This model is equivalent to a tractable convex optimization probl
Externí odkaz:
http://arxiv.org/abs/2007.09530