10 research outputs found
Spatially Aware Ensemble-Based Learning to Predict Weather-Related Outages in Transmission
This paper describes the implementation of prediction model for real-time assessment of weather related outages in the electric transmission system. The network data and historical outages are correlated with variety of weather sources in order to construct the knowledge extraction platform for accurate outage probability prediction. An extension of logistic regression prediction model that embeds the spatial configuration of the network was used for prediction. The results show that developed algorithm has very high accuracy and is able to differentiate the outage area from the rest of the network in 1 to 3 hours before the outage. The prediction algorithm is integrated inside weather testbed for real-time mapping of network outage probabilities using incoming weather forecast
Bid Shading in The Brave New World of First-Price Auctions
Online auctions play a central role in online advertising, and are one of the
main reasons for the industry's scalability and growth. With great changes in
how auctions are being organized, such as changing the second- to first-price
auction type, advertisers and demand platforms are compelled to adapt to a new
volatile environment. Bid shading is a known technique for preventing
overpaying in auction systems that can help maintain the strategy equilibrium
in first-price auctions, tackling one of its greatest drawbacks. In this study,
we propose a machine learning approach of modeling optimal bid shading for
non-censored online first-price ad auctions. We clearly motivate the approach
and extensively evaluate it in both offline and online settings on a major
demand side platform. The results demonstrate the superiority and robustness of
the new approach as compared to the existing approaches across a range of
performance metrics.Comment: In Proceedings of the 29th ACM International Conference on
Information and Knowledge Management (CIKM'20), October 19-23, 2020, Virtual
Event, Irelan
Uncertainty Propagation in Long-Term Structured Regression on Evolving Networks
In long-term forecasting it is important to estimate the confidence of predictions, as they are often affected by errors that are accumulated over the prediction horizon. To address this problem, an effective novel iterative method is developed for Gaussian structured learning models in this study for propagating uncertainty in temporal graphs by modeling noisy inputs. The proposed method is applied for three long-term (up to 8 years ahead) structured regression problems on real-world evolving networks from the health and climate domains. The obtained empirical results and use case analysis provide evidence that the new approach allows better uncertainty propagation as compared to published alternatives