Publications related to Distributionally Robust Multi-Item Newsvendor Problems with Multimodal Demand Distributions

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Pierre Dillenbourg, Richard Lee Davis, Kevin Gonyop Kim, Thiemo Wambsganss, Wei Jiang

This paper investigates the potential impact of deep generative models on the work of creative professionals, specifically focusing on fashion design. We argue that current generative modeling tools lack critical features that would make them useful creati ...

Association for Computing Machinery2023

Optimal Matching of Random Parts

Thomas Alois Weber

This paper examines the minimization of the cost for an expected random production output, given an assembly of finished goods from two random inputs, matched in two categories. We describe the optimal input portfolio, first using the standard normal appro ...

2022

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Daniel Kuhn, Zhi Chen, Wolfram Wiesemann

Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that the probability distribution of the uncertain problem parameters affec ...

2022

Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution

Daniel Kuhn, Soroosh Shafieezadeh Abadeh, Bahar Taskesen

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 statistics, ...

2021

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Kilian Schindler

Stochastic optimization is a popular modeling paradigm for decision-making under uncertainty and has a wide spectrum of applications in management science, economics and engineering. However, the stochastic optimization models one faces in practice are int ...

EPFL2020

Distributional Robustness in Mechanism Design

Cagil Kocyigit

Mechanism design theory examines the design of allocation mechanisms or incentive systems involving multiple rational but self-interested agents and plays a central role in many societally important problems in economics. In mechanism design problems, agen ...

EPFL2020

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

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 onl ...

EPFL2020

Distributionally Robust Mechanism Design

Daniel Kuhn, Cagil Kocyigit, Wolfram Wiesemann

We study a mechanism design problem where an indivisible good is auctioned to multiple bidders, for each of whom it has a private value that is unknown to the seller and the other bidders. The agents perceive the ensemble of all bidder values as a random v ...

2020

Statistical Aspects of Wasserstein Distances

Victor Panaretos, Yoav Zemel

Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover th ...

ANNUAL REVIEWS2019

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Fei Pu

In this thesis, we study systems of linear and/or non-linear stochastic heat equations and fractional heat equations in spatial dimension

1

driven by space-time white noise. The main topic is the study of hitting probabilities for the solutions to these ...

EPFL2018

Distributionally Robust Multi-Item Newsvendor Problems with Multimodal Demand Distributions

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Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Optimal Matching of Random Parts

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Distributional Robustness in Mechanism Design

Wasserstein Distributionally Robust Learning

Distributionally Robust Mechanism Design

Statistical Aspects of Wasserstein Distances

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Wasserstein Distributionally Robust Learning

Distributional Robustness in Mechanism Design

Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Optimal Matching of Random Parts

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Distributionally Robust Mechanism Design

Statistical Aspects of Wasserstein Distances

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Scalable Stochastic Optimization: Scenario Reduction with Guarantees