6 papers
Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy
Vanessa Toborek, Florian Seiffarth, Sebastian Müller +3
Despite more than a decade of curriculum learning (CL) research in NLP, the field lacks a principled account of which difficulty function or scheduler to use for a given problem. T…
Scientific Theory of a Black-Box: A Life Cycle-Scale XAI Framework Based on Constructive Empiricism
Sebastian Müller, Vanessa Toborek, Eike Stadtländer +3
Explainable AI (XAI) offers a growing number of algorithms that aim to answer specific questions about black-box models. What is missing is a principled way to consolidate explanat…
Improving Compactness and Reducing Ambiguity of CFIRE Rule-Based Explanations
Sebastian Müller, Tobias Schneider, Ruben Kemna +1
Models trained on tabular data are widely used in sensitive domains, increasing the demand for explanation methods to meet transparency needs. CFIRE is a recent algorithm in this d…
Four Quadrants of Difficulty: A Simple Categorisation and its Limits
Vanessa Toborek, Sebastian Müller, Christian Bauckhage
Curriculum Learning (CL) aims to improve the outcome of model training by estimating the difficulty of samples and scheduling them accordingly. In NLP, difficulty is commonly appro…
Beyond Shallow Heuristics: Leveraging Human Intuition for Curriculum Learning
Vanessa Toborek, Sebastian Müller, Tim Selbach +2
Curriculum learning (CL) aims to improve training by presenting data from "easy" to "hard", yet defining and measuring linguistic difficulty remains an open challenge. We investiga…
CFIRE: A General Method for Combining Local Explanations
Sebastian Müller, Vanessa Toborek, Tamás Horváth +1
We propose a novel eXplainable AI algorithm to compute faithful, easy-to-understand, and complete global decision rules from local explanations for tabular data by combining XAI me…