activity
20122021
most citedLearning from positive and unlabeled data: a survey

552 citations · 1.1k across the 22 of their papers we have counts for

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9 papers · 1 filter

cs.AI2021★ 10 cited

Leaving Goals on the Pitch: Evaluating Decision Making in Soccer

Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang +2

Analysis of the popular expected goals (xG) metric in soccer has determined that a (slightly) smaller number of high-quality attempts will likely yield more goals than a slew of lo…

cs.AI2018★ 2 cited

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…

cs.AI2017★ 8 cited

Relational Marginal Problems: Theory and Estimation

Ondrej Kuzelka, Yuyi Wang, Jesse Davis +1

In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals. We study this problem in a relational setting and make…

cs.AI2017★ 1 cited

Induction of Interpretable Possibilistic Logic Theories from Relational Data

Ondrej Kuzelka, Jesse Davis, Steven Schockaert

The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kin…

cs.AI2016

Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)

Ondrej Kuzelka, Jesse Davis, Steven Schockaert

In this paper, we advocate the use of stratified logical theories for representing probabilistic models. We argue that such encodings can be more interpretable than those obtained…

cs.AI2016★ 2 cited

Learning Possibilistic Logic Theories from Default Rules

Ondrej Kuzelka, Jesse Davis, Steven Schockaert

We introduce a setting for learning possibilistic logic theories from defaults of the form "if alpha then typically beta". We first analyse this problem from the point of view of m…