A general framework for reconciling multiple weak segmentations of an image. - Soumya Ghosh; Joseph J. Pfeiffer III; Jane Mulligan (WACV, 2009)

Methods to Determine Node Centrality and Clustering in Graphs with Uncertain Structure. - Joseph J. Pfeiffer III; Jennifer Neville (ICWSM, 2011)

Using Bayesian Filtering to Localize Flexible Materials During Manipulation. - Robert Platt Jr. 001; Frank Permenter; Joel Pfeiffer (IEEE Trans. Robotics, 2011)

Fast Generation of Large Scale Social Networks While Incorporating Transitive Closures. - Joseph J. Pfeiffer III; Timothy La Fond; Sebastián Moreno; Jennifer Neville (SocialCom/PASSAT, 2012)

Fast Generation of Large Scale Social Networks with Clustering - Joseph J. Pfeiffer III; Timothy La Fond; Sebastián Moreno; Jennifer Neville (CoRR, 2012)

Active Exploration in Networks: Using Probabilistic Relationships for Learning and Inference. - Joseph John Pfeiffer III; Jennifer Neville; Paul N. Bennett (CIKM, 2014)

A Scalable Method for Exact Sampling from Kronecker Family Models. - Sebastián Moreno; Joseph J. Pfeiffer III; Jennifer Neville; Sergey Kirshner (ICDM, 2014)

Composite Likelihood Data Augmentation for Within-Network Statistical Relational Learning. - Joseph J. Pfeiffer III; Jennifer Neville; Paul N. Bennett (ICDM, 2014)

Assortativity in Chung Lu Random Graph Models. - Stephen Mussmann; John Moore; Joseph J. Pfeiffer III; Jennifer Neville (SNAKDD, 2014)

Attributed graph models: modeling network structure with correlated attributes. - Joseph J. Pfeiffer III; Sebastián Moreno; Timothy La Fond; Jennifer Neville; Brian Gallagher (WWW, 2014)

Incorporating Assortativity and Degree Dependence into Scalable Network Models. - Stephen Mussmann; John Moore; Joseph John Pfeiffer III; Jennifer Neville (AAAI, 2015)

Modeling Website Topic Cohesion at Scale to Improve Webpage Classification. - Dhivya Eswaran; Paul N. Bennett; Joseph J. Pfeiffer III (SIGIR, 2015)

Overcoming Relational Learning Biases to Accurately Predict Preferences in Large Scale Networks. - Joseph J. Pfeiffer III; Jennifer Neville; Paul N. Bennett (WWW, 2015)

Overcoming uncertainty for within-network relational machine learning - Joseph J. Pfeiffer III (2015)

Optimizing the Effectiveness of Incentivized Social Sharing. - Joseph J. Pfeiffer III; Elena Zheleva (ASONAM, 2017)

Modeling and Simultaneously Removing Bias via Adversarial Neural Networks. - John Moore; Joel Pfeiffer; Kai Wei; Rishabh K. Iyer; Denis Charles; Ran Gilad-Bachrach; Levi Boyles; Eren Manavoglu (CoRR, 2018)

Unbiased Estimation of the Value of an Optimized Policy. - Elon Portugaly; Joseph J. Pfeiffer III (CoRR, 2018)

Scalable and exact sampling method for probabilistic generative graph models. - Sebastián Moreno; Joseph J. Pfeiffer III; Jennifer Neville (Data Min. Knowl. Discov., 2018)

Incentivized Social Sharing: Characteristics and Optimization. - Joseph J. Pfeiffer III; Elena Zheleva (Influence and Behavior Analysis in Social Networks and Social Media, 2019)

Causal Inference in the Presence of Interference in Sponsored Search Advertising. - Razieh Nabi; Joel Pfeiffer; Murat Ali Bayir; Denis Charles; Emre Kiciman (CoRR, 2020)

Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction. - Shuxi Zeng; Murat Ali Bayir; Joseph J. Pfeiffer III; Denis Charles; Emre Kiciman (WSDM, 2021)

Masked LARk: Masked Learning, Aggregation and Reporting worKflow. - Joseph J. Pfeiffer III; Denis Charles; Davis Gilton; Young Hun Jung; Mehul Parsana; Erik Anderson (CoRR, 2021)

Causal Inference in the Presence of Interference in Sponsored Search Advertising. - Razieh Nabi; Joel Pfeiffer; Denis Charles; Emre Kiciman (Frontiers Big Data, 2022)