Výsledky vyhledávání - "Papakostas, Demetrios"

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Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation

Autor: Papakostas, Demetrios, Herren, Andrew, Hahn, P. Richard, Castillo, Francisco

Causal inference has gained much popularity in recent years, with interests ranging from academic, to industrial, to educational, and all in between. Concurrently, the study and usage of neural networks has also grown profoundly (albeit at a far fast

Externí odkaz: http://arxiv.org/abs/2405.03130

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Do forecasts of bankruptcy cause bankruptcy? A machine learning sensitivity analysis

Autor: Papakostas, Demetrios, Hahn, P. Richard, Murray, Jared, Zhou, Frank, Gerakos, Joseph

It is widely speculated that auditors' public forecasts of bankruptcy are, at least in part, self-fulfilling prophecies in the sense that they might actually cause bankruptcies that would not have otherwise occurred. This conjecture is hard to prove,

Externí odkaz: http://arxiv.org/abs/2106.04503

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Low-Temperature and Dynamic Magnetism of Highly Frustrated 5d2 Li4MgOsO6 Polymorphs in Comparison with 5d3 Li3Mg2OsO6

Autor: Tran, Gia T., Nguyen, Phuong-Hieu T., Bloed, Charles J., Evans, Michael E., Anczarski, Jamie A., Martin, William P., Toro, Jefferson, Papakostas, Demetrios V., Beare, James, Wilson, Murray N., Greedan, John E., Luke, Graeme M., Gredig, Thomas, Carlo, Jeremy P., Derakhshan, Shahab

Publikováno v: Phys. Rev. B 98, 184401 (2018)

Geometric magnetic frustration (GMF) has attracted substantial interest due to the exotic physics and rich phase diagrams revealed by the cancellation of normally-dominant magnetic interactions, giving impetus for the search for novel frustrated syst

Externí odkaz: http://arxiv.org/abs/1809.04687

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