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Stefan Feuerriegel
Researcher at ETH Zurich
Publications - 226
Citations - 3640
Stefan Feuerriegel is an academic researcher from ETH Zurich. The author has contributed to research in topics: Computer science & Sentiment analysis. The author has an hindex of 25, co-authored 175 publications receiving 2192 citations. Previous affiliations of Stefan Feuerriegel include University of New South Wales & National Institute of Informatics.
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Proceedings ArticleDOI
Learning from On-Line User Feedback in Neural Question Answering on the Web
TL;DR: This work develops a simple feedback mechanism that allows users to express whether a question was answered satisfactorily or whether a different answer is needed, and introduces a question-answering system for (web-based) content repositories with an on-line mechanism for user feedback that achieves state-of-the-art results over several benchmarking datasets.
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Parallel sensitivity analysis for efficient large-scale dynamic optimization
TL;DR: An efficient parallel algorithm for the computation of parametric sensitivities for differential-algebraic equations (DAEs) with a focus on dynamic optimization problems is presented and can almost be reduced to the computational effort of the pure state integration.
Proceedings ArticleDOI
Early Detection of User Exits from Clickstream Data: A Markov Modulated Marked Point Process Model
Tobias Hatt,Stefan Feuerriegel +1 more
TL;DR: In this paper, a Markov modulated marked point process (M3PP) model was proposed to detect users at risk of exiting with no purchase from clickstream data, which accommodates click stream data in a holistic manner.
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Short-term dynamics of day-ahead and intraday electricity prices
Georg Wolff,Stefan Feuerriegel +1 more
TL;DR: In this article, an autoregressive model with exogenous variables (ARX) was proposed to study the short-term drivers of electricity prices in the combined German and Austrian electricity market.
Proceedings ArticleDOI
Causal Transformer for Estimating Counterfactual Outcomes
TL;DR: A novel Causal Transformer for estimating counterfactual outcomes over time that aims to learn adversarial balanced representations, so that they are predictive of the next outcome but non-predictive of the current treatment assignment.