Concept

Wald's maximin model

Related publications (10)

When do Minimax-fair Learning and Empirical Risk Minimization Coincide?

Minimax-fair machine learning minimizes the error for the worst-off group. However, empirical evidence suggests that when sophisticated models are trained with standard empirical risk minimization (ERM), they often have the same performance on the worst-of ...

2023

Relatively robust decisions

Thomas Alois Weber

It is natural for humans to judge the outcome of a decision under uncertainty as a percentage of an ex-post optimal performance. We propose a robust decision-making framework based on a relative performance index. It is shown that if the decision maker's p ...

SPRINGER2022

A Catalyst Framework for Minimax Optimization

Negar Kiyavash

We introduce a generic two-loop scheme for smooth minimax optimization with strongly-convex-concave objectives. Our approach applies the accelerated proximal point framework (or Catalyst) to the associated dual problem and takes full advantage of existing ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2020

A Catalyst Framework for Minimax Optimization

Negar Kiyavash

We introduce a generic \emph{two-loop} scheme for smooth minimax optimization with strongly-convex-concave objectives. Our approach applies the accelerated proximal point framework (or Catalyst) to the associated \emph{dual problem} and takes full advantag ...

2020

Hydropower design under uncertainties

Felix Oberrauch

Hydropower plants are still the most important renewable energy resource worldwide. Hydropower is also the most efficient electricity production and has a very high flexibility in combination with reservoirs. Nevertheless, the construction of hydropower pl ...

EPFL-LCH2017

Hydropower Design under Uncertainties

Felix Oberrauch

The design of hydropower is determined by estimates and long-term forecasts. These forecasts and estimates are highly uncertain and make performance evaluation and design choices challenging. Performance evaluation of hydropower projects is affected by a n ...

EPFL2017

Incentivizing Truthful Responses with the Logarithmic Peer Truth Serum

Boi Faltings, Goran Radanovic

We consider a participatory sensing scenario where a group of private sensors observes the same phenomenon, such as air pollution. We design a novel payment mechanism that incentivizes participation and honest behavior using the peer prediction approach, i ...

2015

Incentive Schemes for Participatory Sensing

Boi Faltings, Goran Radanovic

We consider a participatory sensing scenario where a group of private sensors observes the same phenomenon, such as air pollution. Since sensors need to be installed and maintained, owners of sensors are inclined to provide inaccurate or random data. We de ...

2015

A cutting-plane method for Mixed-Logical Semidefinite Programs with an application to multi-vehicle robust path planning

Daniel Kuhn

The usual approach to dealing with Mixed Logical Semidefinite Programs (MLSDPs) is through the “Big-M” or the convex hull reformulation. The Big-M approach is appealing for its ease of modeling, but it leads to weak convex relaxations when used in a Branch ...

IEEE2010

Worst-Case Bounds for Gaussian Process Models

Matthias Seeger

We present a competitive analysis of some non-parametric Bayesian algorithms in a worst-case online learning setting, where no probabilistic assumptions about the generation of the data are made. We consider models which use a Gaussian process prior (over ...

2006

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