@conference{sun2015message,

title = {Message Passing for Collective Graphical Models},

author = {Tao Sun and Dan Sheldon and Akshat Kumar},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the 32nd International Conference on Machine Learning (ICML-15)},

pages = {853–861},

abstract = {<p>n/a</p>},

note = {<p>n/a</p>},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{saleheen2015puffmarker,

title = {puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation},

author = {Nazir Saleheen and Hillol Sarker and Syed Monowar Hossain and Amin Ahsan Ali and Soujanya Chatterjee and Benjamin Marlin and Santosh Kumar and Mustafa al textquoteright and Emre Ertin},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},

pages = {999–1010},

publisher = {ACM},

organization = {ACM},

abstract = {<p>n/a</p>},

note = {<p>n/a</p>},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{238,

title = {Collaborative Multi-Output Gaussian Processes for Collections of Sparse Multivariate Time Series,},

author = {Steven Cheng-Xian Li and Benjamin M Marlin},

year  = {2015},

date = {2015-00-01},

booktitle = {NIPS Time Series Workshop},

abstract = {<p>Collaborative Multi-Output Gaussian Processes (COGPs) are a flexible tool for modeling multivariate time series. They induce correlation across outputs through the use of shared latent processes. While past work has focused on the computational challenges that result from a single multivariate time series with many observed values, this paper explores the problem of fitting the COGP model to collections of many sparse and irregularly sampled multivariate time series. This work is motivated by applications to modeling physiological data (heart rate, blood pressure, etc.) in Electronic Health Records (EHRs).</p>},

note = {<p>n/a</p>},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{239,

title = {Hierarchical Nested CRFs for Segmentation and Labeling of Physiological Time Series},

author = {Roy J Adams and Edison Thomaz and Benjamin M Marlin},

year  = {2015},

date = {2015-00-01},

booktitle = {NIPS Workshop on Machine Learning in Healthcare},

abstract = {<p>In this paper, we address the problem of nested hierarchical segmentation<br /> and labeling of time series data. We present a hierarchical<br /> span-based conditional random field framework for this problem that<br /> leverages higher-order factors to enforce the nesting constraints. The framework can<br /> incorporate a variety of additional factors including higher order cardinality<br /> factors. This research is motivated by hierarchical activity recognition problems<br /> in the field of mobile Health (mHealth). We show that the specific model of interest in the mHealth setting supports exact MAP inference in quadratic time. Learning is accomplished in the structured support vector machine framework. We show positive results on real and synthetic data sets.</p>},

note = {<p>n/a</p>},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{237,

title = {iProgram: Inferring Smart Schedules for Dumb Thermostats},

author = {Srinivasan Iyengar and Sandeep Kalra and Anushree Ghosh and David Irwin and Prashant Shenoy and Benjamin Marlin},

year  = {2015},

date = {2015-00-01},

booktitle = {10th Annual Women in Machine Learning Workshop},

abstract = {<p>Heating, ventilation, and air conditioning (HVAC) accounts for over 50% of a typical hometextquoterights energy usage. A thermostat generally controls HVAC usage in a home to ensure user comfort. In this paper, we focus on making existing "dumb" programmable thermostats smart by applying energy analytics on smart meter data to infer home occupancy patterns and compute an optimized thermostat schedule. Utilities with smart meter deployments are capable of immediately applying our approach, called iProgram, to homes across their customer base. iProgram addresses new challenges in inferring home occupancy from smart meter data where i) training data is not available and ii) the thermostat schedule may be misaligned with occupancy, frequently resulting in high power usage during unoccupied periods. iProgram translates occupancy patterns inferred from opaque smart meter data into a custom schedule for existing types of programmable thermostats, e.g., 1-day, 7-day, etc. We implement iProgram as a web service and show that it reduces the mismatch time between the occupancy pattern and the thermostat schedule by a median value of 44.28 minutes (out of 100 homes) when compared to a default 8am-6pm weekday schedule, with a median deviation of 30.76 minutes off the optimal schedule. Further, iProgram yields a daily energy saving of 0.42kWh on average across the 100 homes. Utilities may use iProgram to recommend thermostat schedules to customers and provide them estimates of potential energy savings in their energy bills.</p>},

note = {<p>n/a</p>},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}