Emerging domains such as smart electric grids require decisions to be made autonomously, based on the observed behaviors of large numbers of connected consumers. Existing approaches either lack the flexibility to capture nuanced, individualized preference profiles, or scale poorly with the size of the dataset. We propose a preference model that combines flexible Bayesian nonparametric priors—providing state-of-the-art predictive power—with well-justified structural assumptions that allow a scalable implementation. The Gaussian process scalable preference model via Kronecker factorization ( GaSPK ) model provides accurate choice predictions and principled uncertainty estimates as input to decision-making tasks. In consumer choice settings where alternatives are described by few key attributes, inference in our model is highly efficient and scalable to tens of thousands of choices.

Sustainable energy systems of the future will need more than efficient, clean, and low-cost energy sources. They will also need efficient price signals that motivate sustainable energy consumption behaviors and a tight real-time alignment of energy demand with supply from renewable and traditional sources. The Power Trading Agent Competition (Power TAC) is a rich, competitive, open-source simulation platform for future retail power markets built on real-world data and state-of-the-art customer models. Its purpose is to help researchers understand the dynamics of customer and retailer decision-making as well as the robustness of proposed market designs. Power TAC invites researchers to develop autonomous electricity broker agents and to pit them against best-in-class strategies in global competitions, the first of which will be held at AAAI 2013. Power TAC competitions provide compelling, actionable information for policy makers and industry leaders. We describe the competition scenario, demonstrate the realism of the Power TAC platform, and analyze key characteristics of successful brokers in one of our 2012 pilot competitions between seven research groups from five different countries.

We present a novel algorithm called CLICKS, that finds clusters in categorical datasets based on a search for k-partite maximal cliques. Unlike previous methods, CLICKS mines subspace clusters. It uses a selective vertical method to guarantee complete search. CLICKS outperforms previous approaches by over an order of magnitude and scales better than any of the existing method for high-dimensional datasets. We demonstrate this improvement in an excerpt from our comprehensive performance studies.

We present a novel algorithm called CLICKS, that finds clusters in categorical datasets based on a search for k-partite maximal cliques. Unlike previous methods, CLICKS mines subspace clusters. It uses a selective vertical method to guarantee complete search. CLICKS outperforms previous approaches by over an order of magnitude and scales better than any of the existing method for high-dimensional datasets. These results are demonstrated in a comprehensive performance study on real and synthetic datasets.