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2 papers

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IJCAI Conference 2016 Conference Paper

Deep Neural Decision Forests

Peter Kontschieder
Madalina Fiterau
Antonio Criminisi
Samuel Rota Bul
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We present a novel approach to enrich classification trees with the representation learning ability of deep (neural) networks within an end-to-end trainable architecture. We combine these two worlds via a stochastic and differentiable decision tree model, which steers the formation of latent representations within the hidden layers of a deep network. The proposed model differs from conventional deep networks in that a decision forest provides the final predictions and it differs from conventional decision forests by introducing a principled, joint and global optimization of split and leaf node parameters. Our approach compares favourably to other state-of-the-art deep models on a large-scale image classification task like ImageNet.

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AAMAS Conference 2012 Conference Paper

A Truthful Learning Mechanism for Multi-Slot Sponsored Search Auctions with Externalities

Nicola Gatti
Alessandro Lazaric
Francesco Trov
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In recent years, effective sponsored search auctions (SSAs) have been designed to incentivize advertisers (advs) to bid their truthful valuations and, at the same time, to assure both the advs and the auctioneer a non–negative utility. Nonetheless, when the click–through–rates (CTRs) of the advs are unknown to the auction, these mechanisms must be paired with a learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a learning mechanism able to estimate the CTRs as the same time as implementing a truthful mechanism with a revenue loss as small as possible. In this paper, we extend previous results [2, 3] to the general case of multi–slot auctions with position– and ad–dependent externalities with particular attention on the dependency of the regret on the number of slots K and the number of advertisements n.

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