David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Causal structure learning algorithms have focused on learning in ”batch-mode”: i.e., when a full dataset is presented. In many domains, however, it is important to learn in an online fashion from sequential or ordered data, whether because of memory storage constraints or because of potential changes in the underlying causal structure over the course of learning. In this paper, we present TDSL, a novel causal structure learning algorithm that processes data sequentially. This algorithm can track changes in the generating causal structure or parameters, and requires signiﬁcantly less memory in realistic settings. We show by simulation that the algorithm performs comparably to batch-mode learning when the causal structure is stationary, and signiﬁcantly better in non-stationary environments.
|Keywords||No keywords specified (fix it)|
No categories specified
(categorize this paper)
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Alison Gopnik & Laura Schulz (eds.) (2007). Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2010). Learning to Learn Causal Models. Cognitive Science 34 (7):1185-1243.
Richard Scheines, Matt Easterday & David Danks (2007). Teaching the Normative Theory of Causal Reasoning. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. 119--38.
Caren A. Frosch, Teresa McCormack, David A. Lagnado & Patrick Burns (2012). Are Causal Structure and Intervention Judgments Inextricably Linked? A Developmental Study. Cognitive Science 36 (2):261-285.
Alison Gopnik, Clark Glymour, David M. Sobel, Laura Schulz, Tamar Kushnir & David Danks, A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
Alison Gopnik, Clark Glymour, David M. Sobel & Laura E. Schultz, Causal Learning in Children: Causal Maps and Bayes Nets.
Kuo-Chin Chang, Tzung-Pei Hong & Shian-Shyong Tseng (1996). Machine Learning by Imitating Human Learning. Minds and Machines 6 (2):203-228.
York Hagmayer, Björn Meder, Momme von Sydow & Michael R. Waldmann (2011). Category Transfer in Sequential Causal Learning: The Unbroken Mechanism Hypothesis. Cognitive Science 35 (5):842-873.
Robert Gerlai (1997). A Causal Relationship Between LTP and Learning? Has the Question Been Answered by Genetic Approaches? Behavioral and Brain Sciences 20 (4):617-618.
Toby Handfield (2010). Dispositions, Manifestations, and Causal Structure. In Anna Marmodoro (ed.), The Metaphysics of Powers: Their Grounding and Their Manifestations. Routledge.
Added to index2010-12-22
Total downloads11 ( #143,899 of 1,101,833 )
Recent downloads (6 months)1 ( #306,516 of 1,101,833 )
How can I increase my downloads?