1 code implementation • 8 Sep 2023 • Olivier Jeunen, Ben London
Because the data collection policy is typically under the practitioner's control, the unconfoundedness assumption is often left implicit, and its violations are rarely dealt with in the existing literature.
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.
no code implementations • 15 Oct 2022 • Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Thorsten Joachims
A critical need for industrial recommender systems is the ability to evaluate recommendation policies offline, before deploying them to production.
no code implementations • 1 Aug 2022 • Ben London, Levi Lu, Ted Sandler, Thorsten Joachims
We propose the first boosting algorithm for off-policy learning from logged bandit feedback.
no code implementations • 29 Jun 2018 • Ben London, Ted Sandler
We present a Bayesian view of counterfactual risk minimization (CRM) for offline learning from logged bandit feedback.
1 code implementation • NeurIPS 2017 • Ben London
This inspires an adaptive sampling algorithm for SGD that optimizes the posterior at runtime.
no code implementations • 26 Sep 2013 • Stephen Bach, Bert Huang, Ben London, Lise Getoor
Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable.
no code implementations • 7 Mar 2013 • Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor
For the typical cases of real-valued functions and binary relations, we propose several loss functions and derive the associated parameter gradients.
no code implementations • 21 Feb 2013 • Ben London, Bert Huang, Lise Getoor
We investigate the generalizability of learned binary relations: functions that map pairs of instances to a logical indicator.