Gradient-based boosting for statistical relational learning: The relational dependency network case

By Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann, Jude W. Shavlik. 2011

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In Artificial Intelligence

Citations in the corpus

Fitted Q-Learning for Relational Domains (2020, methodology): “To represent these lifted representations, as mentioned earlier, we use RFGB (Natarajan et al. 2014; Natarajan et al. 2012; Natarajan et al. 2011) and a single tree, TILDE (Blockeel and De Raedt 1998), for approximating the Q-value functions.”
Structure learning for relational logistic regression: an ensemble approach (2018, methodology): “The overall procedure is similar to RDNs and MLNs with two significant differences - the need for multiple weights in the clauses and correspondingly the different optimization function inside the inner loop of the algorithm.”
Learning relational dependency networks in hybrid domains (2015, methodology): “In parallel with the claims of Neville and Jensen (2007), there is a direct correspondence between consistent HRDNs and hybrid Markov logic networks (HMLN) in that the set of distributions that can be encoded by a consistent HRDN is equal to the set of positive distributions that can be encoded with”
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (2016, methodology): “Statistical relational learning has also been employed for learning to predict heart attacks [Weiss et al., 2012], onset of Alzheimer’s [Natarajan et al., 2012a], for analyzing pathways and omics experiments [De Maeyer et al., 2013], and extracting adverse drug events from text [Odom et al., 2015].”
Human-Guided Learning for Probabilistic Logic Models (2018, methodology): “Specifically, we consider a recently successful PLM learning algorithm called Relational Functional Gradient Boosting (RFGB) (Natarajan et al., 2012) as the underlying base learner and provide a framework1 for incorporating human advice.”
Learning Markov Logic Networks via Functional Gradient Boosting (2011, background): “The functional gradient approach has produced state of the art results in building relational dependency networks over many domains [14] and has been employed in other relational learning problems such as relational CRFs [15], relational policies [16], relational sequences [17], etc.”
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation (2012, methodology): “The existence of the minimum AUCPR and minimum AP can affect the qualitative interpretation of a model’s performance.”
RelNN: A Deep Neural Model for Relational Learning (2017, methodology): “The RDN-Boost (Natarajan et al. 2012) model can be viewed as an extension of random forests with ways to aggregate over multiple observations, thus enabling random forests to be applicable to relational data.”
KnowRob: A knowledge processing infrastructure for cognition-enabled robots (2013, background): “, 2003) is a very active field of research, and examples of recent accomplishments are methods for structure learning in Markov networks (Davis and Domingos, 2010), learning Markov logic networks (Kiddon and Domingos, 2011), and learning relational dependency networks (Natarajan et al., 2012).”