Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approxima
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(2005). Algorithmic Learning in a Random World. New York: Springer.V. Vovk, A. Gammerman, and G. Shafer. Algorithmic learning in a random world. Springer, Berlin, 2005.V. Vovk, A. Gammerman and G. Shafer "Algorithmic learning in a random world" Springer, New York, (2005)....
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The speed prior: a new simplicity measure yielding, near-optimal computable predictions. In Proc. 15th annual conference on Computational Learning Theory (COLT 2002) (eds Kivinen, J. & Sloan, R. H.) 216–228 (Springer, Sydney, 2002). Daley, R. P. Minimal-program complexity of pseudo-...
While this restriction may limit the external validity of the quantitative results, which we inspect in robustness checks, our setting provides a unique lens to measure complementarities between physician and prediction-based decisions. We first apply a machine learning algorithm, XGBoost, to high-...
(EBSCO) and Wall Street Journal, was conducted using the following search strings: “artificial intelligence”, “bias” and “marketing”, “artificial intelligence in marketing”, “algorithmic bias in marketing”, “bias in artificial intelligence”, “machine learning in marketing”, “deep ...
in a software environment, platform companies can collect data about practically any aspect of user interactions on a granular level (e.g. with event logging) and deploy machine learning techniques to identify suspicious patterns of behavior automatically (Curchod et al.,2020). More subtle forms ...
Hildebrandt proposes to practice what she calls agonistic machine learning (2019, 86), which is based on plurality of perspectives on modelling reality through algorithms and not sticking to the one elaborated by government or private companies, thus ensuring “the foundational incomputability of human...