Search and Score-Based Waterfall Auction Optimization

Online advertising is a major source of income for many online companies. One common approach is to sell online advertisements via waterfall auctions, where a publisher makes sequential price offers to ad networks. The publisher controls the order and prices of the waterfall and by that aims to maximize his revenue. In this work, we propose a methodology to learn a waterfall strategy from historical data by wisely searching in the space of possible waterfalls and selecting the one leading to the highest revenue. The contribution of this work is twofold; First, we propose a novel method to estimate the valuation distribution of each user with respect to each ad network. Second, we utilize the valuation matrix to score our candidate waterfalls as part of a procedure that iteratively searches in local neighborhoods. Our framework guarantees that the waterfall revenue improves between iterations until converging to a local optimum. Real-world demonstrations are provided to show that the proposed method improves the total revenue of real-world waterfalls compared to manual expert optimization. Finally, the code and the data are available here.

Transfer Learning of Photometric Phenotypes in Agriculture Using Metadata

Estimation of photometric plant phenotypes (e.g., hue, shine, chroma) in field conditions is important for decisions on the expected yield quality, fruit ripeness, and need for further breeding. Estimating these from images is difficult due to large variances in lighting conditions, shadows, and sensor properties. We combine the image and metadata regarding capturing conditions embedded into a network, enabling more accurate estimation and transfer between different conditions. Compared to a state-of-the-art deep CNN and a human expert, metadata embedding improves the estimation of the tomato's hue and chroma.