170 citations · 351 across the 11 of their papers we have counts for
11 papers
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…
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…
Conic Mixed-Binary Sets: Convex Hull Characterizations and Applications
Fatma Kılınç-Karzan, Simge Küçükyavuz, Dabeen Lee +1
We consider a general conic mixed-binary set where each homogeneous conic constraint involves an affine function of independent continuous variables and an epigraph variable as…
Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn +1
We introduce a distributionally robust minimium mean square error estimation model with a Wasserstein ambiguity set to recover an unknown signal from a noisy observation. The propo…
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.…
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue +2
A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these…
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
Daniel Kuhn, Peyman Mohajerin Esfahani, Viet Anh Nguyen +1
Many decision problems in science, engineering and economics are affected by uncertain parameters whose distribution is only indirectly observable through samples. The goal of data…
Wasserstein Distributionally Robust Kalman Filtering
Soroosh Shafieezadeh-Abadeh, Viet Anh Nguyen, Daniel Kuhn +1
We study a distributionally robust mean square error estimation problem over a nonconvex Wasserstein ambiguity set containing only normal distributions. We show that the optimal es…
Regularization via Mass Transportation
Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani
The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the predictio…
Data-driven Inverse Optimization with Imperfect Information
Peyman Mohajerin Esfahani, Soroosh Shafieezadeh-Abadeh, Grani Adiwena Hanasusanto +1
In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the o…
Distributionally Robust Logistic Regression
Soroosh Shafieezadeh-Abadeh, Peyman Mohajerin Esfahani, Daniel Kuhn
This paper proposes a distributionally robust approach to logistic regression. We use the Wasserstein distance to construct a ball in the space of probability distributions centere…