Practical Bandits: An Industry Perspective

no code implementations • 2 Feb 2023 • Bram van den Akker, Olivier Jeunen, Ying Li, Ben London, Zahra Nazari, Devesh Parekh

The research literature on these topics is broad and vast, but this can overwhelm practitioners, whose primary aim is to solve practical problems, and therefore need to decide on a specific instantiation or approach for each project.

Decision Making Decision Making Under Uncertainty

Extending Open Bandit Pipeline to Simulate Industry Challenges

no code implementations • 9 Sep 2022 • Bram van den Akker, Niklas Weber, Felipe Moraes, Dmitri Goldenberg

Bandit algorithms are often used in the e-commerce industry to train Machine Learning (ML) systems when pre-labeled data is unavailable.

Machine Learning for Fraud Detection in E-Commerce: A Research Agenda

no code implementations • 5 Jul 2021 • Niek Tax, Kees Jan de Vries, Mathijs de Jong, Nikoleta Dosoula, Bram van den Akker, Jon Smith, Olivier Thuong, Lucas Bernardi

We derive 6 research topics and 12 practical challenges for fraud detection from this operational model.

BIG-bench Machine Learning Fraud Detection

ViTOR: Learning to Rank Webpages Based on Visual Features

no code implementations • 7 Mar 2019 • Bram van den Akker, Ilya Markov, Maarten de Rijke

The visual appearance of a webpage carries valuable information about its quality and can be used to improve the performance of learning to rank (LTR).

General Classification Image Classification +2