552 citations · 1.1k across the 22 of their papers we have counts for
5 papers · 1 filter
Learning from positive and unlabeled data: a survey
Jessa Bekker, Jesse Davis
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabele…
Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data
Jessa Bekker, Pieter Robberechts, Jesse Davis
Most positive and unlabeled data is subject to selection biases. The labeled examples can, for example, be selected from the positive set because they are easier to obtain or more…
Learning from Positive and Unlabeled Data under the Selected At Random Assumption
Jessa Bekker, Jesse Davis
For many interesting tasks, such as medical diagnosis and web page classification, a learner only has access to some positively labeled examples and many unlabeled examples. Learni…
PAC-Reasoning in Relational Domains
Ondrej Kuzelka, Yuyi Wang, Jesse Davis +1
We consider the problem of predicting plausible missing facts in relational data, given a set of imperfect logical rules. In particular, our aim is to provide bounds on the (expect…
Actions Speak Louder Than Goals: Valuing Player Actions in Soccer
Tom Decroos, Lotte Bransen, Jan Van Haaren +1
Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metri…