arXiv research

This paper compares different DRO formulations for pension fund management.

Construction of ambiguity set in robust optimization relies on the choice of divergences between probability distributions. In distribution learning, choosing appropriate probability distributions based on observed data is critical for approximating the true distribution. To improve the performance of machine learning …

As opposed to standard empirical risk minimization (ERM), distributionally robust optimization aims to minimize the worst-case risk over a larger ambiguity set containing the original empirical distribution of the training data. In this work, we describe a minimax framework for statistical learning with ambiguity sets …

Study improves adversarial classification using distributionally robust models.

Expands newsvendor model with moment constraints using Wasserstein distance.

Study examines insurance demand under ambiguity aversion.

This paper tackles cost-sensitive portfolio optimization under ambiguous return distributions.

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 non-convex nature of the ambiguity set, we prove that the …

The paper uses EVT to improve tail risk measures under ambiguity sets.

Bayesian approach to portfolio selection reduces pessimism in frequent trading.

Unified framework for DRO using OT with constraints.

This paper examines how data affects risk measures in uncertain distributions.

The paper explores arbitrage in financial markets under uncertainty using Wasserstein distance.

Proposes a fair classification model using Wasserstein ambiguity sets.

Proposes a method to learn adaptive ambiguity sets for robust optimization.

Develops a method to estimate rare-event probabilities under distributional uncertainty.

We solve robust optimization problems using Wasserstein balls and apply it to mean-CVaR optimization.

This paper tackles robust control of noisy systems with uncertain distributions.

Framework for robust control under model uncertainty, improving financial derivatives hedging.

ELD compares graphs by their embedded Laplacian eigenvectors, resolving ambiguities.

New algorithm solves uncertain Markov decision processes using Wasserstein uncertainty.

DRO-NPE improves neural posterior estimation by reducing overconfidence and overfitting.

FDR-SVM improves classification robustness in federated learning with uncertain data.

Flexible framework integrates machine learning and DRO for uncertain parameter prediction.

Distortion (Denneberg 1990) is a well known premium calculation principle for insurance contracts. In this paper, we study sensitivity properties of distortion functionals w.r.t. the assumptions for risk aversion as well as robustness w.r.t. ambiguity of the loss distribution. Ambiguity is measured by the Wasserstein d…

Optimal financial strategies minimize risk under uncertain models.

This paper investigates WDRO for nonparametric regression, achieving robustness against distributional uncertainty.

Proves hardness of semi-discrete optimal transport and proposes regularization methods.

New estimator robust to adversarial noise and data heterogeneity.

Building on a recent framework for distributionally robust optimization, we consider estimation of the inverse covariance matrix for multivariate data. We provide a novel notion of a Wasserstein ambiguity set specifically tailored to this estimation problem, leading to a tractable class of regularized estimators. Speci…

This paper investigates calculations of robust funding valuation adjustment (FVA) for over the counter (OTC) derivatives under distributional uncertainty using Wasserstein distance as the ambiguity measure. Wrong way funding risk can be characterized via the robust FVA formulation. The simpler dual formulation of the r…

The paper proves generalization bounds and stopping rules for self-selected data in reciprocal learning.

This paper improves conformal prediction for robust interval estimation under distribution shifts.

This paper investigates calculations of robust XVA, in particular, credit valuation adjustment (CVA) and funding valuation adjustment (FVA) for over-the-counter derivatives under distributional uncertainty using Wasserstein distance as the ambiguity measure. Wrong way counterparty credit risk and funding risk can be ch…

Expands robust profit opportunities to include distributional uncertainty.

Bayesian optimization tackles uncertainty in context variables.

Improved robustness in multivariate regression and classification with DRO under Wasserstein metric.

This paper presents a distributionally robust Q-Learning algorithm (DrQ) which leverages Wasserstein ambiguity sets to provide idealistic probabilistic out-of-sample safety guarantees during online learning. First, we follow past work by separating the constraint functions from the principal objective to create a hiera…

A new portfolio model considers investor aversion to loss and risk.

Enhances survival analysis predictions with a robust learning approach.

The paper analyzes insurance contracts under distributional uncertainty using Bregman-Wasserstein divergence.

Paper tackles robust online learning with worst-case distributions.

Paper shows how noisy data can improve robust decision-making.

DRIVE improves IV estimation by accounting for distributional uncertainties.

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 proposed model can be viewed as a zero-sum game between a statistician choosing an estimator -- that is, a measurable function of the observation…

EDRBO optimizes Bayesian optimization with continuous contexts using ensemble models and robust methods.

New method improves PCA robustness using Wasserstein distances.

KG-WDRO optimizes transfer learning with external knowledge.