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Concept

Mean absolute percentage error

Publications associées (27)

Relaxed Wyner's Common Information

Michael Christoph Gastpar, Erixhen Sula

In the problem of coded caching for media delivery, two separate coding opportunities have been identified. The first opportunity is a multi-user advantage and crucially hinges on a public broadcast link in the delivery phase. This has been explored in a p ...

IEEE2019

Regularization via Mass Transportation

Daniel Kuhn, Peyman Mohajerin Esfahani, Soroosh Shafieezadeh Abadeh

The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, ...

2019

Calibration Aspects of INS Navigation

Philipp Clausen

The use of a Bayesian filter (e.g., Kalman filter) for the fusion of information from satellite positioning and inertial navigation is a common approach in many applications, where the knowledge of position, velocity, and attitude in space are of great int ...

EPFL2019

Data-Driven Inverse Optimization with Incomplete Information

Daniel Kuhn, Peyman Mohajerin Esfahani, Soroosh Shafieezadeh Abadeh, Grani Adiwena Hanasusanto

In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the observer seeks the agent's objective function that best explains a historical ...

2018

A statistical shape model to predict the premorbid glenoid cavity

Alexandre Terrier, Alain Farron, Philippe Büchler, Steve Michel Berger

Background: This study proposes a method for inferring the premorbid glenoid shape and orientation of scapulae affected by glenohumeral osteoarthritis (OA) to inform restorative surgery. ...

MOSBY-ELSEVIER2018

Time course of prediction errors in sequential decision making

Michael Herzog, He Xu

In reinforcement learning, an agent makes sequential decisions to maximize reward. During learning, the actual and expected outcome are compared to tell whether a decision was good or bad. The difference between the actual outcome and expected outcome is t ...

2018

Constrained expectation-maximization algorithm for stochastic inertial error modeling: study of feasibility

Jan Skaloud, Stéphane Guerrier, Yannick Stebler

Stochastic modeling is a challenging task for low-cost sensors whose errors can have complex spectral structures. This makes the tuning process of the INS/GNSS Kalman filter often sensitive and difficult. For example, first-order Gauss–Markov processes are ...

IOP Publishing2011