Institutsseminar/2023-04-14

Termin (Alle Termine)

	Datum	Freitag, 14. April 2023
	Uhrzeit	11:30 – 12:15 Uhr (Dauer: 45 min)
	Ort	Raum 348 (Gebäude 50.34)
	Webkonferenz
	Vorheriger Termin	Fr 31. März 2023
	Nächster Termin	Fr 28. April 2023

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Vorträge

Vortragende(r)	Paul Giza
Titel	CGFLEX: A Flexible Framework for Causal Graph-based Data Synthesis
Vortragstyp	Masterarbeit
Betreuer(in)	Bela Böhnke
Vortragssprache
Vortragsmodus	in Präsenz
Kurzfassung	Algorithms that extract dependencies from data and represent them as causal graphs must also be tested. For such tests, data with a known ground truth is required, but this is rarely available. Generating data under controlled conditions through simulations is expensive and time-consuming. A solution to this problem is to create synthetic datasets, where dependencies are predefined, to evaluate the results of these algorithms. This work focuses on building a framework for the synthesis of data. In the framework, the synthesis process begins with generating a random dependency graph, specifically a directed acyclic graph. Each node in the graph, except the source nodes, has parent nodes and represents a variable. In the next step, each node is populated with predefined random dependencies. A dependency is a model that determines the value of a variable based on its parent variables. From this structure, datasets can be sampled. Users can control the properties of the causal graph through various parameters and choose from multiple types of dependencies, representing different complexity levels. Additionally, the sampling process allows for interactivity by enabling the exchange of dependencies during the sampling process. Dependencies can be exchanged with fixed values, probability distributions, or time series functions. This flexibility provides a robust tool for improving and comparing the mentioned algorithms under various conditions.

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