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pro vyhledávání: '"Glauner, A"'
Autor:
Glauner, Patrick
Publikováno v:
Proceedings of the 2nd Teaching in Machine Learning Workshop, PMLR, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021
As a result of the rapidly advancing digital transformation of teaching, universities have started to face major competition from Massive Open Online Courses (MOOCs). Universities thus have to set themselves apart from MOOCs in order to justify the a
Externí odkaz:
http://arxiv.org/abs/2107.04024
Autor:
Glauner, Patrick
In April 2021, the European Commission published a proposed regulation on AI. It intends to create a uniform legal framework for AI within the European Union (EU). In this chapter, we analyze and assess the proposal. We show that the proposed regulat
Externí odkaz:
http://arxiv.org/abs/2105.15133
Autor:
Glauner, Alexander
In the classical static optimal reinsurance problem, the cost of capital for the insurer's risk exposure determined by a monetary risk measure is minimized over the class of reinsurance treaties represented by increasing Lipschitz retained loss funct
Externí odkaz:
http://arxiv.org/abs/2012.09648
Autor:
Bäuerle, Nicole, Glauner, Alexander
Publikováno v:
Mathenatical Methods of Operations Research 94, 35-69, (2021)
We study the minimization of a spectral risk measure of the total discounted cost generated by a Markov Decision Process (MDP) over a finite or infinite planning horizon. The MDP is assumed to have Borel state and action spaces and the cost function
Externí odkaz:
http://arxiv.org/abs/2012.04521
Autor:
Bäuerle, Nicole, Glauner, Alexander
Publikováno v:
European Journal of Operational Research 2021
In this paper, we consider risk-sensitive Markov Decision Processes (MDPs) with Borel state and action spaces and unbounded cost under both finite and infinite planning horizons. Our optimality criterion is based on the recursive application of stati
Externí odkaz:
http://arxiv.org/abs/2010.07220
Autor:
Bäuerle, Nicole, Glauner, Alexander
Publikováno v:
Mathematics of Operations Research (47), 1757-1780, 2021
We consider robust Markov Decision Processes with Borel state and action spaces, unbounded cost and finite time horizon. Our formulation leads to a Stackelberg game against nature. Under integrability, continuity and compactness assumptions we derive
Externí odkaz:
http://arxiv.org/abs/2007.13103
Publikováno v:
Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936,
Externí odkaz:
http://arxiv.org/abs/1803.00897
Publikováno v:
Proceedings of the 13th International FLINS Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018)
In machine learning, a bias occurs whenever training sets are not representative for the test data, which results in unreliable models. The most common biases in data are arguably class imbalance and covariate shift. In this work, we aim to shed ligh
Externí odkaz:
http://arxiv.org/abs/1801.05627
Autor:
Bäuerle, Nicole, Glauner, Alexander
Publikováno v:
Insurance: Mathematics and Economics 82, 37-47, 2018
In this paper we consider reinsurance or risk sharing from a macroeconomic point of view. Our aim is to find socially optimal reinsurance treaties. In our setting we assume that there are $n$ insurance companies each bearing a certain risk and one re
Externí odkaz:
http://arxiv.org/abs/1711.10210
Autor:
Glauner, Patrick, Dahringer, Niklas, Puhachov, Oleksandr, Meira, Jorge Augusto, Valtchev, Petko, State, Radu, Duarte, Diogo
Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging countries. Industrial NTL detection systems are s
Externí odkaz:
http://arxiv.org/abs/1709.03008