Valuable information in early sales proxies: The use of Google search ranks in portfolio optimization

Kupfer, Alexander; Zorn, Josef

doi:10.1002/for.2547

Cited by 6 publications

(1 citation statement)

References 41 publications

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“…Therefore, one disambiguation strategy is building a certain parametric model and regarding ground-truth label as a latent variable. The model is iteratively refined by optimizing certain objectives, such as the maximum likelihood criterion (Kupfer and Zorn 2019;Liu and Dietterich 2014), or the maximum margin criterion (Yu and Zhang 2016). Another strategy assumes equal importance for all kinds of candidate labels and predicts label scores by averaging their modeling outputs (Cour, Sapp, and Taskar 2011;Tang and Zhang 2017;Wu and Zhang 2018;Wang, Li, and Zhang 2019;Xu, Lv, and Geng 2019).…”

Section: Related Workmentioning

confidence: 99%

General Partial Label Learning via Dual Bipartite Graph Autoencoder

Chen

Zareian

et al. 2020

AAAI

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We formulate a practical yet challenging problem: General Partial Label Learning (GPLL). Compared to the traditional Partial Label Learning (PLL) problem, GPLL relaxes the supervision assumption from instance-level — a label set partially labels an instance — to group-level: 1) a label set partially labels a group of instances, where the within-group instance-label link annotations are missing, and 2) cross-group links are allowed — instances in a group may be partially linked to the label set from another group. Such ambiguous group-level supervision is more practical in real-world scenarios as additional annotation on the instance-level is no longer required, e.g., face-naming in videos where the group consists of faces in a frame, labeled by a name set in the corresponding caption. In this paper, we propose a novel graph convolutional network (GCN) called Dual Bipartite Graph Autoencoder (DB-GAE) to tackle the label ambiguity challenge of GPLL. First, we exploit the cross-group correlations to represent the instance groups as dual bipartite graphs: within-group and cross-group, which reciprocally complements each other to resolve the linking ambiguities. Second, we design a GCN autoencoder to encode and decode them, where the decodings are considered as the refined results. It is worth noting that DB-GAE is self-supervised and transductive, as it only uses the group-level supervision without a separate offline training stage. Extensive experiments on two real-world datasets demonstrate that DB-GAE significantly outperforms the best baseline over absolute 0.159 F1-score and 24.8% accuracy. We further offer analysis on various levels of label ambiguities.

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