jmcinerney.bib

@article{mcinerney2015population,
  title={The Population Posterior and Bayesian Inference on Streams},
  author={McInerney, James and Ranganath, Rajesh and Blei, David M},
  journal={Neural Information Processing (NIPS)},
  year={2015}
}
@inproceedings{charlin2015dynamic,
  title={Dynamic Poisson Factorization},
  author={Charlin, Laurent and Ranganath, Rajesh and McInerney, James and Blei, David M},
  booktitle={Proceedings of the 9th ACM Conference on Recommender Systems},
  pages={155--162},
  year={2015},
  organization={ACM}
}
@inproceedings{mandt2016variational,
  title={Variational tempering},
  author={Mandt, Stephan and McInerney, James and Abrol, Farhan and Ranganath, Rajesh and Blei, David M},
  booktitle={International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year={2016},
  organization={ACM}
@inproceedings{liang2016modeling,
  title={Modeling user exposure in recommendation},
  author={Liang, Dawen and Charlin, Laurent and McInerney, James and Blei, David M},
  booktitle={International World Wide Web Conference (WWW)},
  year={2016},
}
@inproceedings{mcinerney2013modelling,
  title={Modelling heterogeneous location habits in human populations for location prediction under data sparsity},
  author={McInerney, James and Zheng, Jiangchuan and Rogers, Alex and Jennings, Nicholas R},
  booktitle={International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)},
  year={2013},
}
@inproceedings{mcinerney2014discovering,
  title={Discovering newsworthy tweets with a geographical topic model},
  author={McInerney, James and Blei, David M},
  booktitle={Workshop on Data Science for News Publishing, in conjunction with Conference on Knowledge Discovery and Data Mining (KDD)},
  year={2014},
}
@article{eps358226,
  title = {Intelligent agents for mobile location services},
  school = {University of Southampton},
  author = {James McInerney},
  year = {2014},
  url = {http://eprints.soton.ac.uk/365495/}
}
@article{eps358226,
  volume = {10},
  number = {4},
  month = {August},
  author = {J. McInerney and A. Rogers and N. R. Jennings},
  title = {Bus, bike and random journeys: crowdsourcing aid distribution in Ivory Coast},
  journal = {Significance},
  pages = {4--9},
  year = {2013},
  //url = {http://eprints.soton.ac.uk/358226/},
  pdf = {http://eprints.soton.ac.uk/358226/7/j.1740-9713.2013.00673.x.pdf},
  abstract = {Delivering supplies in poor rural areas is difficult and expensive. But people travel; and statistics can piggyback aid supplies on to the network of everyday journeys. James McInerney, Alex Rogers and Nicholas R. Jennings explore an imaginative solution to getting aid to the countryside.}
}
@article{eps356175,
  volume = {9},
  number = {6},
  title = {Breaking the habit: measuring and predicting departures from
routine in individual human mobility},
  author = {J. McInerney and S. Stein and A Rogers and N. R. Jennings},
  month = {July},
  year = {2013},
  pages = {808--822},
  journal = {Pervasive and Mobile Computing},
  //url = {http://eprints.soton.ac.uk/356175/},
  pdf = {http://eprints.soton.ac.uk/356175/7/1-s2.0-S1574119213000989-main.pdf},
  abstract = {Researchers studying daily life mobility patterns have recently shown that humans are typically highly predictable in their movements. However, no existing work has examined the boundaries of this predictability, where human behaviour transitions temporarily from routine patterns to highly unpredictable states. To address this shortcoming, we tackle two interrelated challenges. First, we develop a novel information-theoretic metric, called instantaneous entropy, to analyse an individual?s mobility patterns and identify temporary departures from routine. Second, to predict such departures in the future, we propose the first Bayesian framework that explicitly models breaks from routine, showing that it outperforms current state-of-the-art predictors}
}
@inproceedings{eps339940,
  booktitle = {Mobile Data Challenge by Nokia},
  title = {Exploring periods of low predictability in
daily life mobility},
  author = {J. McInerney and S. Stein and A. Rogers and N. R. Jennings},
  year = {2012},
  month = {July},
  //url = {http://eprints.soton.ac.uk/339940/},
  pdf = {http://eprints.soton.ac.uk/339940/1/paper_extended_past2.pdf}
}
@inproceedings{eps342584,
  booktitle = {4th International Workshop on Location-Based Social Networks },
  month = {September},
  title = {Improving location prediction services for new users with probabilistic latent semantic analysis},
  author = {J. McInerney and A. Rogers and N. R. Jennings},
  year = {2012},
  keywords = {knowledge representation and reasoning, geometric, spatial,
and temporal reasoning, machine learning, data mining,
machine learning, machine learning, reasoning under uncertainty, uncertainty in ai, machine learning, unsupervised learning},
  //url = {http://eprints.soton.ac.uk/342584/},
  pdf = {http://eprints.soton.ac.uk/342584/1/paper_lbsn.pdf},
  abstract = {Location prediction systems that attempt to determine the mobility patterns of individuals in their daily lives have become increasingly common in recent years. Approaches to this prediction task include eigenvalue decomposition [5], non-linear time series analysis of arrival times [10], and variable order Markov models [1]. However, these approaches
all assume sufficient sets of training data. For new users, by definition, this data is typically not available, leading to poor predictive performance. Given that mobility is a highly personal behaviour, this represents a significant barrier to entry. Against this background, we present a novel framework to enhance prediction using information about the mobility habits of existing users. At the core of the framework is a hierarchical Bayesian model, a type of probabilistic semantic analysis [7], representing the intuition that the temporal features of the new user?s location habits are likely to be similar to those of an existing user in the system. We evaluate this framework on the real life location habits of 38 users in the Nokia Lausanne dataset, showing that accuracy is improved by 16\%, relative to the state of the art, when predicting the next location of new users.}
}
@inproceedings{eps352319,
  booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI)},
  title = {Learning periodic human behaviour models from sparse data for crowdsourcing aid delivery in developing countries},
  author = {J. McInerney and A. Rogers and N. R. Jennings},
  year = {2013},
  month = {July},
  pages = {401--410},
  //url = {http://eprints.soton.ac.uk/352319/},
  pdf = {http://eprints.soton.ac.uk/352319/7/uai.pdf},
  abstract = {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. We propose an alternative method of distribution (to standard road delivery) in which the existing mobility habits of a local population are leveraged to deliver aid, which raises two technical challenges in the areas optimisation and learning. For optimisation, a standard Markov decision process applied to this problem is intractable, so we provide an exact formulation that takes advantage of the periodicities in human location behaviour. To learn such behaviour models from sparse data (i.e., cell tower observations), we develop a Bayesian model of human mobility. Using real cell tower data of the mobility behaviour of 50,000 individuals in Ivory Coast, we find that our model outperforms the state of the art approaches in mobility prediction by at least 25\% (in held-out data likelihood). Furthermore, when incorporating mobility prediction with our MDP approach, we find a 81.3\% reduction in total delivery time versus routine planning that minimises just the number of participants in the solution path.}
}
@inproceedings{eps354656,
  booktitle = {International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)},
  month = {September},
  title = {Modelling heterogeneous location habits in human populations for location prediction under data sparsity},
  author = {J. McInerney and J. Zheng and A. Rogers and N. R. Jennings},
  year = {2013},
  month = {September},
  pages = {469--478},
  keywords = {human behavior learning, mobile phone sensing, human
activity inference, graphical models},
  //url = {http://eprints.soton.ac.uk/354656/},
  pdf = {http://eprints.soton.ac.uk/354656/1/paper143.pdf},
  abstract = {In recent years, researchers have sought to capture the daily life location behaviour of groups of people for exploratory, inference, and predictive purposes. However, development of such approaches has been limited by the requirement of personal semantic labels for locations or social/spatial overlap between individuals in the group. To address this shortcoming, we present a Bayesian model of mobility in populations (i.e., groups without spatial or social interconnections) that is not subject to any of these requirements. The model intelligently shares temporal parameters between people, but keeps the spatial parameters speci?c to individuals. To illustrate the advantages of population modelling, we apply our model to the dif?cult problem of overcoming data sparsity in location prediction systems, using the Nokia dataset comprising 38 individuals, and ?nd a factor of 2.4 improvement in location prediction performance against a state-of-the-art model when training on only 20 hours of observations.}
}
@article{eps342509,
  volume = {46},
  number = {4},
  title = {Global manhunt pushes the limits of social mobilization},
  author = {I. Rahwan and S. Dsouza and A. Rutherford and V. Naroditskiy and J. McInerney and M. Venanzi and N. R. Jennings and M. Cebrian},
  year = {2013},
  month = {April},
  pages = {68--75},
  journal = {IEEE Computer},
  //url = {http://eprints.soton.ac.uk/342509/},
  pdf = {http://eprints.soton.ac.uk/342509/4/06297969.pdf}
}
@article{eps356015,
  volume = {8},
  number = {9},
  title = {Targeted social mobilization in a global manhunt},
  author = {A. Rutherford and M. Cebrian and I. Rahwan and S. Dsouza and J. McInerney and V. Naroditskiy and M. Venanzi and N. R. Jennings and J. R. deLara and E. Wahlstedt and S. U. Miller},
  year = {2013},
  month = {September},
  pages = {e74628},
  journal = {PLoS ONE},
  //url = {http://eprints.soton.ac.uk/356015/},
  pdf = {http://eprints.soton.ac.uk/356015/7/fetchObject%5B1%5D.0074628%26representation%3DPDF.pdf},
  abstract = {Social mobilization, the ability to mobilize large numbers of people via social networks to achieve highly
distributed tasks, has received signifcant attention in recent times. This growing capability, facilitated by
modern communication technology, is highly relevant to endeavors which require the search for individuals
that possess rare information or skills, such as finding medical doctors during disasters, or searching for
missing people. An open question remains, as to whether in time-critical situations, people are able
to recruit in a targeted manner, or whether they resort to so-called blind search, recruiting as many
acquaintances as possible via broadcast communication. To explore this question, we examine data
from our recent success in the U.S. State Department's Tag Challenge, which required locating and
photographing 5 target persons in 5 different cities in the United States and Europe in under 12
hours based only on a single mug-shot. We find that people are able to consistently route information
in a targeted fashion even under increasing time pressure. We derive an analytical model for social-
media fueled global mobilization and use it to quantify the extent to which people were targeting their
peers during recruitment. Our model estimates that approximately 1 in 3 messages were of targeted
fashion during the most time-sensitive period of the challenge. This is a novel observation at such short
temporal scales, and calls for opportunities for devising viral incentive schemes that provide distance
or time-sensitive rewards to approach the target geography more rapidly. This observation of `12 hours
of separation' between individuals has applications in multiple areas from emergency preparedness, to
political mobilization.}
}
@inproceedings{eps351242,
  booktitle = {23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)},
  month = {April},
  title = {Forecasting multi-appliance usage for smart home energy management},
  author = {N. Cuong Truong and J. McInerney and L. Tran-Thanh and E. Costanza and S. D. Ramchurn},
  year = {2013},
  //url = {http://eprints.soton.ac.uk/351242/},
  pdf = {http://eprints.soton.ac.uk/351242/3/ijcai2013_camera.pdf}
}

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