This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant...
We present a machine learning methodology (models, algorithms, and experimental data) to discovering the agent dynamics that drive the evolution of the social groups in a communit...
Hung-Ching Chen, Mark K. Goldberg, Malik Magdon-Is...
The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. In silico prediction of interactions between GPCRs and small molecules is ...
The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several m...
Darrin P. Lewis, Tony Jebara, William Stafford Nob...