Sam Yang

Peter Washington, Aayush Nandkeolyar, Sam Yang

The task of developing a machine learning (ML) model for a particular problem is inherently open-ended, and there is an unbounded set of possible solutions. Steps of the ML development pipeline, such as feature engineering, loss function specification, data imputation, and dimensionality reduction, require the engineer to consider an extensive and often infinite array of possibilities. Successfully identifying high-performing solutions for an unfamiliar dataset or problem requires a mix of mathematical prowess and creativity applied towards inventing and repurposing novel ML methods. Here, we explore the feasibility of hosting crowdsourced ML challenges to facilitate a breadth-first exploration of open-ended research problems, thereby expanding the search space of problem solutions beyond what a typical ML team could viably investigate. We develop MLPro, a system which combines the notion of open-ended ML coding problems with the concept of an automatic online code judging platform. To conduct a pilot evaluation of this paradigm, we crowdsource several open-ended ML challenges to ML and data science practitioners. We describe results from two separate challenges. We find that for sufficiently unconstrained and complex problems, many experts submit similar solutions, but some experts provide unique solutions which outperform the "typical" solution class. We suggest that automated expert crowdsourcing systems such as MLPro have the potential to accelerate ML engineering creativity.

Sam Yang, Bjorn Vaagensmith, Deepika Patra et al.

We propose a multi-fidelity neural network (MFNN) tailored for rapid high-dimensional grid power flow simulations and contingency analysis with scarce high-fidelity contingency data. The proposed model comprises two networks -- the first one trained on DC approximation as low-fidelity data and coupled to a high-fidelity neural net trained on both low- and high-fidelity power flow data. Each network features a latent module which parametrizes the model by a discrete grid topology vector for generalization (e.g., $n$ power lines with $k$ disconnections or contingencies, if any), and the targeted high-fidelity output is a weighted sum of linear and nonlinear functions. We tested the model on 14- and 118-bus test cases and evaluated its performance based on the $n-k$ power flow prediction accuracy with respect to imbalanced contingency data and high-to-low-fidelity sample ratio. The results presented herein demonstrate MFNN's potential and its limits with up to two orders of magnitude faster and more accurate power flow solutions than DC approximation.