We present a general Bayesian framework for hyperparameter tuning in L2-regularized supervised learning models. Paradoxically, our algorithm works by first analytically integratin...
The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments a...
We describe a framework and equations used to model and predict the behavior of multi-agent systems (MASs) with learning agents. A difference equation is used for calculating the ...
This paper presents a Bayesian approach for Gaussian mixture model (GMM)-based speaker identification. Some approaches evaluate the speaker score of a test speech utterance using ...
Prefrontal cortex (PFC) has been implicated in the ability to switch behavioral strategies in response to changes in reward contingencies. A recent experimental study has shown tha...