Research

Peer-reviewed publications

Collaborative Place Models
Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara
International Joint Conference on Artificial Intelligence (IJCAI), 2015.
Paper – Supplement 1 – Supplement 2

A fundamental problem underlying location-based tasks is to construct a complete profile of users’ spatiotemporal patterns. In many real-world settings, the sparsity of location data makes it difficult to construct such a profile. As a remedy, we describe a Bayesian probabilistic graphical model, called Collaborative Place Model (CPM), which infers similarities across users to construct complete and time-dependent profiles of users’ whereabouts from unsupervised location data. We apply CPM to both sparse and dense datasets, and demonstrate how it both improves location prediction performance and provides new insights into users’ spatiotemporal patterns.

Collaborative Ranking for Local Preferences
Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara
Artificial Intelligence and Statistics (AISTATS), 2014.
Paper – Supplement – Poster

For many collaborative ranking tasks, we have access to relative preferences among subsets of items, but not to global preferences among all items. To address this, we introduce a matrix factorization framework called Collaborative Local Ranking (CLR). We justify CLR by proving a bound on its generalization error, the first such bound for collaborative ranking that we know of. We then derive a simple alternating minimization algorithm and prove that its running time is independent of the number of training examples. We apply CLR to a novel venue recommendation task and demonstrate that it outperforms state-of-the-art collaborative ranking methods on real-world data sets.

Combining Spatial and Telemetric Features for Learning Animal Movement Models
Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick
Uncertainty in Artificial Intelligence (UAI), 2010.
Paper – Poster

We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of user-defined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to data and demonstrate its effectiveness via asymptotic analysis and synthetic experiments. We also apply our model to real datasets, and show that it outperforms the most popular radio telemetry software package used in ecology. We conclude that integration of different data sources under a single statistical framework, coupled with appropriate parameter and state estimation procedures, produces both accurate location estimates and an interpretable statistical model of animal movement.

Agent-Based Modeling of the Evolution of Vowel Harmony
K. David Harrison, Mark Dras, Berk Kapicioglu
North East Linguistic Society (NELS), 2002.
Paper

Ph.D. thesis

Applications of Machine Learning to Location Data
Berk Kapicioglu
Princeton University, Department of Computer Science, 2013.
PDF

Workshops and symposiums

Place Models for Sparse Location Prediction
Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara
New York Academy of Sciences (NYAS), Machine Learning Symposium, 2012.
Website

Place Recommendation with Implicit Spatial Feedback
Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara
New York Academy of Sciences (NYAS), Machine Learning Symposium, 2011.
Website – Video

Learning Animal Movement Models and Location Estimates Using HMMs
Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick
Neural Information Processing Systems (NIPS), Stochastic Models of Behaviour Workshop, 2008.
Website

Learning Animal Movement Models and Location Estimates Using HMMs
Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick
New York Academy of Sciences (NYAS), Machine Learning Symposium, 2008.
Website

Patents

Venue Prediction Based on Ranking
Berk Kapicioglu and David Rosenberg
Publication Number: US20130325855 A1.
Website

Method for Analyzing and Ranking Venues
Berk Kapicioglu and David Rosenberg
Publication Number: US20130325746 A1.
Website

Collaborators