Zobrazeno 1 - 8 of 8 pro vyhledávání: '"Reisach, Alexander"'
1
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A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
Autor: Reisach, Alexander G., Tami, Myriam, Seiler, Christof, Chambaz, Antoine, Weichwald, Sebastian
Additive Noise Models (ANMs) are a common model class for causal discovery from observational data and are often used to generate synthetic data for causal discovery benchmarking. Specifying an ANM requires choosing all parameters, including those no
Externí odkaz: http://arxiv.org/abs/2303.18211
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2
Report
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game
Autor: Reisach, Alexander G., Seiler, Christof, Weichwald, Sebastian
Simulated DAG models may exhibit properties that, perhaps inadvertently, render their structure identifiable and unexpectedly affect structure learning algorithms. Here, we show that marginal variance tends to increase along the causal order for gene
Externí odkaz: http://arxiv.org/abs/2102.13647
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3
Akademický článek
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4
Simple Sorting Criteria Help Find the Causal Order in Additive Noise Models
Autor: Reisach, Alexander G., Tami, Myriam, Seiler, Christof, Chambaz, Antoine, Weichwald, Sebastian
Additive Noise Models (ANM) encode a popular functional assumption that enables learning causal structure from observational data. Due to a lack of real-world data meeting the assumptions, synthetic ANM data are often used to evaluate causal discover
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb6f5091893d9b207070176d3e600255
http://arxiv.org/abs/2303.18211
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7
spillR: Causal Modelling of Spillover in Mass Cytometry
Autor: Guazzini, Marco, Reisach, Alexander, Weichwald, Sebastian, Seiler, Christof
Publikováno v: EuroBioC 2022
In mass cytometry marker interference called spillover, can cause markers to have higher abundances than their true abundances. Chevrier and Crowell et al. 2018 introduced an experimental and computational procedure to estimate spillover and compensa
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::8f39cc41d0bfd6baf7eadd0c9c6142ef
https://cris.maastrichtuniversity.nl/en/publications/7a9caef8-2dfe-4976-9313-14c9085cabee
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8
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game
Autor: Reisach, Alexander, Seiler, Christof, Weichwald, Sebastian, Ranzato, M., Beygelzimer, A., Liang, P.S., Vaughan, J.W., Dauphin, Y.
Publikováno v: Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021)
Simulated DAG models may exhibit properties that, perhaps inadvertently, render their structure identifiable and unexpectedly affect structure learning algorithms. Here, we show that marginal variance tends to increase along the causal order for gene
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::f98140c7446d62e066d3e7baef5a4076
https://cris.maastrichtuniversity.nl/en/publications/e880018b-05a8-4ecb-a89f-2493e36e1ee8
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