activity
20132022
most citedDynamic Poisson Factorization

95 citations · 196 across the 9 of their papers we have counts for

collaborators

9 papers

stat.ML2022

The Implicit Delta Method

Nathan Kallus, James McInerney

Epistemic uncertainty quantification is a crucial part of drawing credible conclusions from predictive models, whether concerned about the prediction at a given point or any downst…

cs.LG2021

Residual Overfit Method of Exploration

James McInerney, Nathan Kallus

Exploration is a crucial aspect of bandit and reinforcement learning algorithms. The uncertainty quantification necessary for exploration often comes from either closed-form expres…

cs.LG2020★ 47 cited

Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions

James McInerney, Brian Brost, Praveen Chandar +2

Users of music streaming, video streaming, news recommendation, and e-commerce services often engage with content in a sequential manner. Providing and evaluating good sequences of…

stat.ML2015★ 4 cited

Modeling User Exposure in Recommendation

Dawen Liang, Laurent Charlin, James McInerney +1

Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feed…

cs.LG2015★ 95 cited

Dynamic Poisson Factorization

Laurent Charlin, Rajesh Ranganath, James McInerney +1

Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically…

stat.ML2015★ 2 cited

The Population Posterior and Bayesian Inference on Streams

James McInerney, Rajesh Ranganath, David M. Blei

Many modern data analysis problems involve inferences from streaming data. However, streaming data is not easily amenable to the standard probabilistic modeling approaches, which a…

stat.ML2014★ 20 cited

Variational Tempering

Stephan Mandt, James McInerney, Farhan Abrol +2

Variational inference (VI) combined with data subsampling enables approximate posterior inference over large data sets, but suffers from poor local optima. We first formulate a det…

cs.AI2013★ 8 cited

Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries

James McInerney, Alex Rogers, Nicholas R. Jennings

In many developing countries, half the population lives in rural locations, where access to essentials such as school materials, mosquito nets, and medical supplies is restricted.…

physics.soc-ph2013★ 20 cited

Targeted Social Mobilisation in a Global Manhunt

Alex Rutherford, Manuel Cebrian, Iyad Rahwan +8

Social mobilization, the ability to mobilize large numbers of people via social networks to achieve highly distributed tasks, has received significant attention in recent times. Th…