Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Tschiatschek, Sebastian"'
Autor:
Tschiatschek, Sebastian, Stamboliev, Eugenia, Schmude, Timothée, Coeckelbergh, Mark, Koesten, Laura
We discuss the role of humans in algorithmic decision-making (ADM) for socially relevant problems from a technical and philosophical perspective. In particular, we illustrate tensions arising from diverse expectations, values, and constraints by and
Externí odkaz:
http://arxiv.org/abs/2405.10706
Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novice
Externí odkaz:
http://arxiv.org/abs/2401.13324
We consider the problem of third-person imitation learning with the additional challenge that the learner must select the perspective from which they observe the expert. In our setting, each perspective provides only limited information about the exp
Externí odkaz:
http://arxiv.org/abs/2312.16365
Autor:
Sudak, Timur, Tschiatschek, Sebastian
We consider the problem of learning Variational Autoencoders (VAEs), i.e., a type of deep generative model, from data with missing values. Such data is omnipresent in real-world applications of machine learning because complete data is often impossib
Externí odkaz:
http://arxiv.org/abs/2310.16648
Student modeling is central to many educational technologies as it enables predicting future learning outcomes and designing targeted instructional strategies. However, open-ended learning domains pose challenges for accurately modeling students due
Externí odkaz:
http://arxiv.org/abs/2310.10690
We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences. We shortly introduce the importance of explaining ADM systems, give a brief overview of approa
Externí odkaz:
http://arxiv.org/abs/2305.16700
We propose Convex Constraint Learning for Reinforcement Learning (CoCoRL), a novel approach for inferring shared constraints in a Constrained Markov Decision Process (CMDP) from a set of safe demonstrations with possibly different reward functions. W
Externí odkaz:
http://arxiv.org/abs/2305.16147
Block-based programming environments are increasingly used to introduce computing concepts to beginners. However, novice students often struggle in these environments, given the conceptual and open-ended nature of programming tasks. To effectively su
Externí odkaz:
http://arxiv.org/abs/2303.16359
Ethical principles for algorithms are gaining importance as more and more stakeholders are affected by "high-risk" algorithmic decision-making (ADM) systems. Understanding how these systems work enables stakeholders to make informed decisions and to
Externí odkaz:
http://arxiv.org/abs/2302.08264
We study sequential decision-making with known rewards and unknown constraints, motivated by situations where the constraints represent expensive-to-evaluate human preferences, such as safe and comfortable driving behavior. We formalize the challenge
Externí odkaz:
http://arxiv.org/abs/2206.05255