82 citations · 170 across the 10 of their papers we have counts for
8 papers · 1 filter
Practical Bandits: An Industry Perspective
Bram van den Akker, Olivier Jeunen, Ying Li +3
The bandit paradigm provides a unified modeling framework for problems that require decision-making under uncertainty. Because many business metrics can be viewed as rewards (a.k.a…
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model
Alexander Buchholz, Ben London, Giuseppe di Benedetto +1
A critical need for industrial recommender systems is the ability to evaluate recommendation policies offline, before deploying them to production. Unfortunately, widely used off-p…
Boosted Off-Policy Learning
Ben London, Levi Lu, Ted Sandler +1
We propose the first boosting algorithm for off-policy learning from logged bandit feedback. Unlike existing boosting methods for supervised learning, our algorithm directly optimi…
Bayesian Counterfactual Risk Minimization
Ben London, Ted Sandler
We present a Bayesian view of counterfactual risk minimization (CRM) for offline learning from logged bandit feedback. Using PAC-Bayesian analysis, we derive a new generalization b…
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent
Ben London
We study the generalization error of randomized learning algorithms -- focusing on stochastic gradient descent (SGD) -- using a novel combination of PAC-Bayes and algorithmic stabi…
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
Stephen Bach, Bert Huang, Ben London +1
Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require appro…
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss
Ben London, Theodoros Rekatsinas, Bert Huang +1
We propose a modular framework for multi-relational learning via tensor decomposition. In our learning setting, the training data contains multiple types of relationships among a s…
Graph-based Generalization Bounds for Learning Binary Relations
Ben London, Bert Huang, Lise Getoor
We investigate the generalizability of learned binary relations: functions that map pairs of instances to a logical indicator. This problem has application in numerous areas of mac…