Concept

Regret (decision theory)

Related publications (39)

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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

Regret Minimization and Separation in Multi-Bidder Multi-Item Auctions

Daniel Kuhn, Napat Rujeerapaiboon, Cagil Kocyigit

We study a robust auction design problem with a minimax regret objective, where a seller seeks a mechanism for selling multiple items to multiple anonymous bidders with additive values. The seller knows that the bidders' values range over a box uncertainty ...

2020

No-Regret Learning from Partially Observed Data in Repeated Auctions

Maryam Kamgarpour, Orcun Karaca

We study a general class of repeated auctions, such as the ones found in electricity markets, as multi-agent games between the bidders. In such a repeated setting, bidders can adapt their strategies online using no-regret algorithms based on the data obser ...

2020

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The planning of national power systems is traditionally based on long-term forecasts of electricity demand and fuel prices. However, over such long time horizons (20-50 years), these forecasts often prove to be inaccurate. As an example, wrong projections ...

ELSEVIER SCI LTD2020

No-Regret Learning in Unknown Games with Correlated Payoffs

Maryam Kamgarpour, Andreas Krause, Ilija Bogunovic

We consider the problem of learning to play a repeated multi-agent game with an unknown reward function. Single player online learning algorithms attain strong regret bounds when provided with full information feedback, which unfortunately is unavailable i ...

Curran Associates, Inc.2019

We consider online convex optimizations in the bandit setting. The decision maker does not know the time- varying cost functions, or their gradients. At each time step, she observes the value of the cost function for her chosen action. The objective is to ...

IEEE2018

Information-gap decision theory (IGDT): a new promising method for management of uncertainties in hydropower projects: case study

Anton Schleiss, 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. Effective values deviating from the estimates can have a signifi ...

Hydropower&Dams, Aqua Media International2018

Robust Multidimensional Pricing: Separation without Regret

Daniel Kuhn, Napat Rujeerapaiboon, Cagil Kocyigit

We study a robust monopoly pricing problem with a minimax regret objective, where a seller endeavors to sell multiple goods to a single buyer, only knowing that the buyer's values for the goods range over a rectangular uncertainty set. We interpret this pr ...

2018

Reduced Leakage Synchrophasor Estimation: Hilbert Transform Plus Interpolated DFT

Mario Paolone, Asja Derviskadic, Guglielmo Frigo

Synchrophasor estimation is typically performed by means of spectral analysis based on the discrete Fourier transform (DFT). Traditional DFT approaches, though, suffer from several uncertainty contributions due to the stationarity assumption, spectral leak ...

2018