Collective Activity Detection Using Hinge-loss Markov Random Fields. - Ben London 001; Sameh Khamis; Stephen H. Bach; Bert Huang; Lise Getoor; Larry S. Davis (CVPR Workshops, 2013)

Collective Stability in Structured Prediction: Generalization from One Example. - Ben London 001; Bert Huang; Ben Taskar; Lise Getoor (ICML (3), 2013)

Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. - Stephen H. Bach; Bert Huang; Ben London 001; Lise Getoor (UAI, 2013)

Graph-based Generalization Bounds for Learning Binary Relations - Ben London 001; Bert Huang; Lise Getoor (CoRR, 2013)

Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss - Ben London 001; Theodoros Rekatsinas; Bert Huang; Lise Getoor (CoRR, 2013)

Collective Classification of Network Data. - Ben London 001; Lise Getoor (Data Classification: Algorithms and Applications, 2014)

PAC-Bayesian Collective Stability. - Ben London 001; Bert Huang; Ben Taskar; Lise Getoor (AISTATS, 2014)

The Benefits of Learning with Strongly Convex Approximate Inference. - Ben London 001; Bert Huang; Lise Getoor (ICML, 2015)

Budgeted Online Collective Inference. - Jay Pujara; Ben London 001; Lise Getoor (UAI, 2015)

On the Stability of Structured Prediction. - Benjamin Alexei London (2015)

Stability and Generalization in Structured Prediction. - Ben London 001; Bert Huang; Lise Getoor (J. Mach. Learn. Res., 2016)

Collective Graph Identification. - Galileo Mark S. Namata Jr.; Ben London 001; Lise Getoor (ACM Trans. Knowl. Discov. Data, 2016)

A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent. - Ben London 001 (NIPS, 2017)

Sustainability at scale: towards bridging the intention-behavior gap with sustainable recommendations. - Sabina Tomkins; Steven Isley; Ben London 001; Lise Getoor (RecSys, 2018)

Bayesian Counterfactual Risk Minimization. - Ben London 001; Ted Sandler (ICML, 2019)

Train and Test Tightness of LP Relaxations in Structured Prediction. - Ofer Meshi; Ben London 001; Adrian Weller; David A. Sontag (J. Mach. Learn. Res., 2019)

Recommendations as Treatments. - Thorsten Joachims; Ben London 001; Yi Su; Adith Swaminathan; Lequn Wang (AI Mag., 2021)

Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model. - Alexander Buchholz; Ben London 001; Giuseppe Di Benedetto; Thorsten Joachims (CoRR, 2022)

Boosted Off-Policy Learning. - Ben London 001; Levi Lu; Ted Sandler; Thorsten Joachims (AISTATS, 2023)

Contextual Position Bias Estimation Using a Single Stochastic Logging Policy. - Giuseppe Di Benedetto; Alexander Buchholz; Ben London 001; Matej Jakimov; Yannik Stein; Jan Malte Lichtenberg; Vito Bellini; Matteo Ruffini; Thorsten Joachims (LERI@RecSys, 2023)

Tutorials at The Web Conference 2023. - Valeria Fionda; Olaf Hartig; Reyhaneh Abdolazimi; Sihem Amer-Yahia; Hongzhi Chen; Xiao Chen 016; Peng Cui 001; Jeffrey Dalton 001; Xin Luna Dong; Lisette Espín-Noboa; Wenqi Fan; Manuela Fritz; et al. (WWW (Companion Volume), 2023)

Double Clipping: Less-Biased Variance Reduction in Off-Policy Evaluation. - Jan Malte Lichtenberg; Alexander Buchholz; Giuseppe Di Benedetto; Matteo Ruffini; Ben London 001 (CoRR, 2023)

Offline Recommender System Evaluation under Unobserved Confounding. - Olivier Jeunen; Ben London 001 (CoRR, 2023)

Counterfactual Ranking Evaluation with Flexible Click Models. - Alexander Buchholz; Ben London 001; Giuseppe Di Benedetto; Jan Malte Lichtenberg; Yannik Stein; Thorsten Joachims (SIGIR, 2024)

Practical Bandits: An Industry Perspective. - Bram van den Akker; Olivier Jeunen; Ying Li 012; Ben London 001; Zahra Nazari; Devesh Parekh (WSDM, 2024)