Abstract: Substance misusers, including adolescent smokers, often have reduced reward system activity during processing of non-drug rewards. Using a psychophysiological interaction approach, we examined functional connectivity with the ventral striatum during reward anticipation in a large (N = 206) sample of adolescent smokers. Increased smoking frequency was associated with (1) increased connectivity with regions involved in saliency and valuation, including the orbitofrontal cortex and (2) reduced connectivity between the ventral striatum and regions associated with inhibition and risk aversion, including the right inferior frontal gyrus. These results demonstrate that functional connectivity during reward processing is relevant to adolescent addiction.
Abstract: Symbolic regression (SR) is one the most popular applications of genetic programming (GP) and an attractive alternative to the standard deterministic regression approaches due to its flexibility in generating free-form mathematical models from observed data without any domain knowledge. However, GP suffers from various issues hindering the applicability of the technique to real-life problems. In this paper, we show that a hybrid deterministic regression (DR)/genetic programming based symbolic regression (GP-SR) algorithm outperforms GP-SR alone on a brain imaging dataset.
Abstract: Given n variables to model, symbolic regression (SR) returns a flat list of n equations. As the number of state variables to be modeled scales, it becomes increasingly difficult to interpret such a list. Here we present a symbolic regression method that detects and captures hidden hierarchy in a given system. The method returns the equations in a hierarchical dependency graph, which increases the interpretability of the results. We demonstrate two variations of this hierarchical modeling approach, and show that both consistently outperform non-hierarchical symbolic regression on a number of synthetic data sets.
Abstract: Symbolic regression (SR) is a well studied method in genetic programming (GP) for discovering free-form mathematical models from observed data. However, it has not been widely accepted as a standard data science tool. The reluctance is in part due to the hard to analyze random nature of GP and scalability issues. On the other hand, most popular deterministic regression algorithms were designed to generate linear models and therefore lack the flexibility of GP based SR (GP-SR). Our hypothesis is that hybridizing these two techniques will create a synergy between the GP-SR and deterministic approaches to machine learning, which might help bring the GP based techniques closer to the realm of big learning. In this paper, we show that a hybrid deterministic/GP-SR algorithm outperforms GP-SR alone and the state-of-the-art deterministic regression technique alone on a set of multivariate polynomial symbolic regression tasks as the system to be modeled becomes more multivariate.
Abstract: Symbolic Regression is an attractive modeling approach because it can capture and present, mathematically, relationships between variables of interest. However, given n variables to model, symbolic regression returns a flat list of n equations. As the number of state variables to be modeled scales, interpretation of such a list becomes difficult. Here we present a symbolic regression method that detects and captures hidden hierarchy in a given system. The method returns the equations in a hierarchical dependency graph, which increases the interpretability of the results. We demonstrate that two variations of this hierarchical modeling approach outperform non-hierarchical symbolic regression on a synthetic data suite.