@conference {335, title = {Sustainability at Scale: Bridging the Intention-Behavior Gap with Sustainable Recommendations}, booktitle = {Recommender Systems (RecSys)}, year = {2018}, abstract = {

Finding sustainable products and evaluating their claims is a significant barrier facing sustainability-minded customers. Tools that reduce both these burdens are likely to boost the sale of sustainable products. However, it is difficult to determine the sustainability characteristics of these products {\textemdash} there are a variety of certifications and definitions of sustainability, and quality labeling requires input from domain experts. In this paper, we propose a flexible probabilistic framework that uses domain knowledge to identify sustainable products and customers, and uses these labels to predict customer purchases. We evaluate our approach on grocery items from the Amazon catalog. Our proposed approach outperforms established recommender system models in predicting future purchases while jointly inferring sustainability scores for customers and products.

}, author = {Tomkins, Sabina and Isley, Steve and London, Ben and Lise Getoor} } @conference {pujara:starai15, title = {Online Inference for Knowledge Graph Construction.}, booktitle = {Workshop on Statistical Relational AI}, year = {2015}, author = {Pujara, Jay and London, Ben and Lise Getoor and Cohen, William} } @article {london:stability15, title = {Stability and Generalization in Structured Prediction}, journal = {{\textendash}}, year = {2015}, note = {preprint}, keywords = {PAC-Bayes, generalization bounds, learning theory, structured prediction}, author = {London, Ben and Huang, Bert and Lise Getoor} } @mastersthesis {london:thesis15, title = {On the Stability of Structured Prediction}, year = {2015}, school = {University of Maryland}, type = {phd}, author = {London, Ben} } @conference {london:aistats14, title = {PAC-Bayesian Collective Stability}, booktitle = {Proceedings of the 17th International Conference on Artificial Intelligence and Statistics}, year = {2014}, author = {London, Ben and Huang, Bert and Benjamin Taskar and Lise Getoor} } @conference {london:nips14ws, title = {On the Strong Convexity of Variational Inference}, booktitle = {NIPS Workshop on Advances in Variational Inference}, year = {2014}, author = {London, Ben and Huang, Bert and Lise Getoor} } @unpublished {london:arxiv13a, title = {Graph-based Generalization Bounds for Learning Binary Relations}, year = {2013}, note = {http://arxiv.org/abs/1302.5348}, publisher = {University of Maryland College Park}, author = {London, Ben and Huang, Bert and Lise Getoor} } @conference {bach:uai13, title = {Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction}, booktitle = {Uncertainty in Artificial Intelligence}, year = {2013}, abstract = {

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. Instead of working in a combinatorial space, we use hinge-loss Markov random fields (HL-MRFs), an expressive class of graphical models with log-concave density functions over continuous variables, which can represent confidences in discrete predictions. This paper demonstrates that HLMRFs are general tools for fast and accurate structured prediction. We introduce the first inference algorithm that is both scalable and applicable to the full class of HL-MRFs, and show how to train HL-MRFs with several learning algorithms. Our experiments show that HL-MRFs match or surpass the predictive performance of state-of-the-art methods, including discrete models, in four application domains.

}, author = {Bach, Stephen H. and Huang, Bert and London, Ben and Lise Getoor} } @unpublished {london:arxiv13b, title = {Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss}, year = {2013}, note = {http://arxiv.org/abs/1303.1733}, publisher = {University of Maryland College Park}, author = {London, Ben and Rekatsinas, Theodoros and Huang, Bert and Lise Getoor} } @conference {london:nips13ws, title = {PAC-Bayes Generalization Bounds for Randomized Structured Prediction}, booktitle = {NIP Workshop on Perturbation, Optimization and Statistics}, year = {2013}, author = {London, Ben and Huang, Bert and Benjamin Taskar and Lise Getoor} } @conference {london:nips12asalsn, title = {Improved Generalization Bounds for Large-scale Structured Prediction}, booktitle = {NIPS Workshop on Algorithmic and Statistical Approaches for Large Social Networks}, year = {2012}, author = {London, Ben and Huang, Bert and Lise Getoor} } @conference {pujara:icmlws11, title = {Reducing Label Cost by Combining Feature Labels and Crowdsourcing}, booktitle = {ICML Workshop on Combining Learning Strategies to Reduce Label Cost}, year = {2011}, author = {Pujara, Jay and London, Ben and Lise Getoor} }