Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Schulte, Oliver"'
Autor:
Schulte, Oliver, Poupart, Pascal
Reinforcement learning (RL) and causal modelling naturally complement each other. The goal of causal modelling is to predict the effects of interventions in an environment, while the goal of reinforcement learning is to select interventions that maxi
Externí odkaz:
http://arxiv.org/abs/2403.04221
Autor:
Schulte, Oliver
Estimating the expected value of a graph statistic is an important inference task for using and learning graph models. This note presents a scalable estimation procedure for expected motif counts, a widely used type of graph statistic. The procedure
Externí odkaz:
http://arxiv.org/abs/2305.01089
Autor:
Mar, Richard, Schulte, Oliver
Statistical-Relational Model Discovery aims to find statistically relevant patterns in relational data. For example, a relational dependency pattern may stipulate that a user's gender is associated with the gender of their friends. As with propositio
Externí odkaz:
http://arxiv.org/abs/2110.09767
We describe NTS-NOTEARS, a score-based structure learning method for time-series data to learn dynamic Bayesian networks (DBNs) that captures nonlinear, lagged (inter-slice) and instantaneous (intra-slice) relations among variables. NTS-NOTEARS utili
Externí odkaz:
http://arxiv.org/abs/2109.04286
Autor:
Jaeger, Manfred, Schulte, Oliver
A generative probabilistic model for relational data consists of a family of probability distributions for relational structures over domains of different sizes. In most existing statistical relational learning (SRL) frameworks, these models are not
Externí odkaz:
http://arxiv.org/abs/2004.10984
Autor:
Jaeger, Manfred, Schulte, Oliver
A subtle difference between propositional and relational data is that in many relational models, marginal probabilities depend on the population or domain size. This paper connects the dependence on population size to the classic notion of projectivi
Externí odkaz:
http://arxiv.org/abs/1807.00564
Autor:
Riahi, Fatemeh, Schulte, Oliver
This paper is based on a previous publication [29]. Our work extends exception mining and outlier detection to the case of object-relational data. Object-relational data represent a complex heterogeneous network [12], which comprises objects of diffe
Externí odkaz:
http://arxiv.org/abs/1807.00381
Autor:
Liu, Guiliang, Schulte, Oliver
A variety of machine learning models have been proposed to assess the performance of players in professional sports. However, they have only a limited ability to model how player performance depends on the game context. This paper proposes a new appr
Externí odkaz:
http://arxiv.org/abs/1805.11088
Many databases store data in relational format, with different types of entities and information about links between the entities. The field of statistical-relational learning (SRL) has developed a number of new statistical models for such data. In t
Externí odkaz:
http://arxiv.org/abs/0811.4458