About the Lab

The MLDS lab focused on the development of machine learning models and algorithms for addressing a variety of challenging problems in the areas of computational social science, computational ecology, computational behavioral science and computational medicine.

The MLDS lab’s research continues in multiple labs within the the College of Information and Computer Sciences including the REML lab, the SLANG lab, and Prof. Sheldon’s research group.

Publications

38 entries « 7 of 8 »

2013

Sheldon, Daniel; Sun, Tao; Kumar, Akshat; Dietterich, Thomas G

Approximate Inference in Collective Graphical Models Conference

In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013., 2013.

BibTeX

Natarajan, Annamalai; Parate, Abhinav; Gaiser, Edward; Angarita, Gustavo; Malison, Robert; Marlin, Benjamin; Ganesan, Deepak

Detecting Cocaine Use with Wearable Electrocardiogram Sensors Proceeding

Zurich, Switzerland, 2013.

BibTeX

Marlin, Benjamin M; Adams, Roy J; Sadasivam, Rajani; Houston, Thomas K

Towards Collaborative Filtering Recommender Systems for Tailored Health Communications Proceeding

Washington D.C., 2013.

BibTeX

2012

Sheldon, D; Dietterich, T G

Collective Graphical Models Conference

Advances in Neural Information Processing Systems (NIPS 2011), 2012, (<p>n/a</p>).

Abstract | BibTeX

Hochachka, Wesley M; Fink, Daniel; Hutchinson, Rebecca A; Sheldon, Daniel; Wong, Weng-Keen; Kelling, Steve

Data Intensive Science Applied to Broad-Scale Citizen Science Booklet

2012, (<p>n/a</p>).

Abstract | BibTeX

38 entries « 7 of 8 »