82 citations · 170 across the 10 of their papers we have counts for
10 papers
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
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…
Train and Test Tightness of LP Relaxations in Structured Prediction
Ofer Meshi, Mehrdad Mahdavi, Adrian Weller +1
Structured prediction is used in areas such as computer vision and natural language processing to predict structured outputs such as segmentations or parse trees. In these settings…
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…