Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is only observable through a set of training samples. In data-driven decision-making, the goal is to find a decision from the training samples that will perform equally well on unseen test samples. In this thesis, we leverage techniques from distributionally robust optimization to address decision-making problems in statistical learning, behavioral economics and estimation problems. In particular, Wasserstein distributionally robust optimization is studied where the decision-maker learns decisions that perform well under the most adverse distribution within a certain Wasserstein distance from a nominal distribution constructed from the training samples.

In the first part of the thesis we study regression and classification methods in supervised learning from the distributionally robust perspective. In the classical setting the goal is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, overfitting is typically mitigated by adding regularization terms to the objective that penalize hypothesis complexity. We introduce new regularization techniques using ideas from distributionally robust optimization, and we give new probabilistic interpretations to existing techniques.

In the second part of the thesis we consider data-driven inverse optimization problems where an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the observer seeks the agent's objective function that best explains a historical sequence of signals and corresponding optimal actions. We focus here on situations where the observer has imperfect information, that is, where the agent's true objective function is not contained in the search space of candidate objectives, where the agent suffers from bounded rationality or implementation errors, or where the observed signal-response pairs are corrupted by measurement noise. We formalize this inverse optimization problem as a distributionally robust program minimizing the worst-case risk that the predicted decision (i.e., the decision implied by a particular candidate objective) differs from the agent's actual response to a random signal.

In the final part of the thesis 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 estimator and the least favorable distribution form a Nash equilibrium. Despite the nonconvex nature of the ambiguity set, we prove that the estimation problem is equivalent to a tractable convex program. We further devise a Frank-Wolfe algorithm for this convex program whose direction-searching subproblem can be solved in a quasi-closed form. Using these ingredients, we introduce a distributionally robust Kalman filter that hedges against model risk.