Probabilistic Inductive Logic Programming

By Luc De Raedt, Kristian Kersting. 2004

441 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Anchors: High-Precision Model-Agnostic Explanations (2018, background): “While the search for anchors is similar in spirit to Probabilistic Inductive Logic Programming (ILP) (De Raedt and Kersting 2008) and other rule-finding methods, one crucial difference is that we do not assume a dataset apriori - instead we have perturbation distributions and a black box model,…”
Learning Explanatory Rules from Noisy Data (2017, background): “[De Raedt and Kersting, 2008] discuss this issue in depth, stressing the importance of building systems capable of applying relational learning to uncertain data.”
Venture: a higher-order probabilistic programming platform with programmable inference (2014, background): “Additionally, a number of probabilistic extensions of classical logic programming languages have also been developed (Poole, 1997; Sato and Kameya, 1997; De Raedt and Kersting, 2008), motivated by problems in statistical relational learning.”
Tunneling Neural Perception and Logic Reasoning through Abductive Learning (2018, background): “the great inventions in the fruitful symbolic AI research. A natural approach to x these problems is to combine symbolic and sub-symbolic AI systems. Fuzzy logic [48], probabilistic logic programming [7] and statistical relational learning [11] have been proposed to empower traditional logic-based m”
Counting Belief Propagation (2009, background): “Both approaches yield the same lower bound of 5.8×1028, which is in the same range as Kroc et al. report.”
TensorLog: Deep Learning Meets Probabilistic DBs (2017, background): “…2016) is one well-known logic programming language which adopts this semantics, and there is a large literature (for surveys, see (Suciu et al., 2011; De Raedt & Kersting, 2008)) on approaches to more tractibly estimating Eq 5, which naively requires marginalizing over all 2 |DB| interpretations.”
Fast relational learning using bottom clause propositionalization with artificial neural networks (2013, unknown): “Finally, in the third category, we place the body of work on statistical relational learning (Getoor and Taskar, 2007; De Raedt et al., 2008), that albeit relevant for comparison, is less directly related to this work, e.g. Markov Logic Networks (MLN) (Richardson and Domingos, 2006) and other…”
nFOIL: Integrating Naïve Bayes and FOIL (2005, methodology): “A probabilistic covers function In the setting of learning from probabilistic entailment (De Raedt & Kersting 2004), the notion of coverage is replaced by a probability.”
Towards Neural Theorem Proving at Scale (2018, background): “However, these systems are unable to handle ambiguous and noisy high-dimensional data such as sensory inputs (Raedt and Kersting, 2008).”
Transfer Learning via Relational Type Matching (2015, methodology): “…1 LTL: Language-bias Transfer Learning function PERFORMTRANSFER(Pt,M2Tsource,QS ,QT ) rules = ∅ for path ∈M2Tsource do matchesT = GENMATCHES(path,Pt,QS ,QT ) rules = rules ⋃ matchesT end for rules = REFINE(rules) return rules end function function GENMATCHES(path,Pt,QS ,QT ) matches = ∅ for all…”
Parameter Learning in Probabilistic Databases: A Least Squares Approach (2008, background): “The situation fits the general learning setting stated in [21]:”
A survey on question answering technology from an information retrieval perspective (2011, background): “Many existing logical formalisms integrate probabilistic statements, and probabilistic relational learners for recognising the logical forms become popular [117].”
Measure Transformer Semantics for Bayesian Machine Learning (2011, background): “witness the following list of probabilistic programming la nguages: AutoBayes [ 50], Alchemy [11], Blaise [7], BLOG [36], Church [15], Csoft [52], FACTORIE [32], Figaro [44], HANSEI [24], HBC [10], IBAL [ 42], λ◦ [41], Probabilistic cc [ 18], PFP [12], and Probabilistic Scheme [ 45].”