This page contains the summary of the paper "Fairness Without Demographics in Repeated Loss Minimization" by Hashimoto, T. B., Srivastava, M., Namkoong, H., & Liang, P. which was published at the International Conference of Machine Learning (ICML) in 2018. In the following, an

Overview of the Paper

Introduction

Fairness

Example and Problem Setup

Why Empirical Risk Minimization (ERM) does not work

Why Distributonally Robust Optimization (DRO) does work

Fairness Without Demographics in Repeated Loss Minimization

Contents

Overview of the Paper

Introduction

Fairness

Example and Problem Setup

Why Empirical Risk Minimization (ERM) does not work

Why Distributonally Robust Optimization (DRO) does work

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Fairness Without Demographics in Repeated Loss Minimization

Overview of the Paper

Introduction

Fairness

Example and Problem Setup

Why Empirical Risk Minimization (ERM) does not work

Why Distributonally Robust Optimization (DRO) does work

Navigation menu

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