Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts
of sensing data. However, there are significant challenges in converting this large-scale …

We consider a novel problem called Activity Prediction, where the goal is to predict the future
activity occurrence times from sensor data. In this paper, we make three main contributions …

Structured prediction is the problem of learning a function that maps structured inputs to
structured outputs. Prototypical examples of structured prediction include part-of-speech …

We consider a framework for structured prediction based on search in the space of complete
structured outputs. Given a structured input, an output is produced by running a …

We propose a novel search-based approach for greedy coreference resolution, where the
mentions are processed in order and added to previous coreference clusters. Our method is …

The mainstream approach to structured prediction problems in computer vision is to learn an
energy function such that the solution minimizes that function. At prediction time, this …

Multi-label learning concerns learning multiple, overlapping, and correlated classes. In this
paper, we adapt a recent structured prediction framework called HC-Search for multi-label …

Human activity prediction is a challenging problem which poses a number of machine
learning challenges. Predicting occurrences of activities is valuable in understanding human …

Prediction of human activities is important in a wide variety of fields. However, developing
activity predictors that work well with the uncertainties surrounding activity data is a …

We are witnessing the rise of the data-driven science paradigm, in which massive amounts
of data-much of it collected as a side-effect of ordinary human activity-can be analyzed to …