Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 15 of 15 for “"Causal learning"”.
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Three algorithms for causal learning
The field of causal learning has grown in the past decade, establishing itself as a major focus in artificial intelligence research. Traditionally, approaches to causal learning are split into two areas. One area involves the learning of structures from observational data alone and the second, …
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Age and Context Dependency in Causal Learning
<p>The ability to make associations between causal cues and outcomes is an important adaptive trait that allows us to properly prepare for an upcoming event. Encoding context is a type of associative processing; thus, context is also an important aspect of acquiring causal relationships. Context …
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Aging and Selective Attention in Causal Learning
… age differences in generalization of causal value employing similarity as a cue to causality. Exemplars from six food categories (A+, B-, C+, D-. E+, F-) were presented to both young and older adults in two contiguous training phases. Training Phase 1 included exemplars from categories …
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The Redundancy Effect in Human Causal Learning: Attention, Uncertainty, And Inhibition
… for those theories of conditioning that compute learning through a global error-term” (p. 119). One such theory is the Rescorla-Wagner (1972) model, which predicts the opposite result, that Y will have a stronger association with the outcome than X. This thesis explored the basis of the …
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The role of temporal factors and prior knowledge in causal learning and judgment
Causal relationships are all around us: wine causes stains; matches cause flames; foods cause allergic reactions. Next to language, it is hard to imagine a cognitive process more indicative of human intelligence than causal reasoning. To understand how people accomplish these feats, two major …
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The Embodied Causal Learner
… 2008). For this specific study, the area of causal learning was examined.</p> <p>The primary goal of this specific study was to investigate whether elements of embodiment, and any mechanisms therein, would be found in the area of causal learning. That is, would motor actions irrelevant to …
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Judicious imitation : children differentially imitate deterministically and probabilistically effective actions
Three studies look at whether the assumption of causal determinism (the assumption that all else being equal, causes generate effects deterministically) affects children's imitation of modeled actions. We show that, even when the frequency of an effect is matched, both preschoolers and toddlers …
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Learning from Commerce Data: from Theory to Practice
… by these challenges, we address the problem of causal learning in panels with general intervention patterns that may depend on historical data. In this thesis, we present a novel and nearly complete solution to this problem that allows for the rate-optimal recovery of treatment effects. Our work …
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Collective Motion in Spatially Heterogeneous Environments
… 2D camera and pixel datasets to examine whether causal information can be retained in low-dimensional and noisy representations. Using midge swarming datasets, the analysis showed that EUGENE, the classification tool employed, performs better in constrained conditions such as cross-wind motion. …
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Causal structure of networks of stochastic processes
We propose different approaches to infer causal influences between agents in a network using only observed time series. This includes graphical models to depict causal relationships in the network, algorithms to identify the graphs in different scenarios and when only a subset of agents are …
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Exploring Complex Problems in Fluid Dynamics: from CFD to Experiments Leveraging ML
… but can provide accurate predictions as well. 3. Causal Learning of Large Amplitude Ship Motions with Emphasis on Parametric Rolling: Predicting ship motions in severe sea states is complex due to the nonlinear wave-body interactions involved. This section introduces a simulation approach …
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Software defect localization using explainable deep learning
… in this regard is the field of machine learning-based software vulnerability detection, where models are trained to classify code as either vulnerable or clean. These models offer advantages over traditional static application testing tools, including adaptability to project-specific …
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Learning and restructuring causal concepts
Typical studies of concept learning in adults address the learning of novel concepts, but much of learning involves the updating and restructuring of familiar concepts. Research on conceptual change explores this issue directly but differs greatly from the formal approach of the adult learning …
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Seeing versus Doing: Causal Bayes Nets as Psychological Models of Causal Reasoning
Diese Dissertation geht der Frage nach, wie Menschen Vorhersagen über die Folgen von aktiven Interventionen in kausalen Systemen zu treffen, wenn sie diese Systeme zuvor nur passiv beobachtet haben. Die Theorie der kausalen Bayes-Netze (Spirtes, Glymour & Scheines, 1993; Pearl, 2000) stellt einen …
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Learning Transferable Representations
A first contribution of this thesis is to propose causality as a language for problems of distribution shift. First, we consider domain generalisation, where no data from the test distribution are observed during training. What assumptions can be made regarding the relation between train and test …