283 citations
- University College LondonGB28 papers
- Systemic Risk CentreGB17 papers
- University of OxfordGB10 papers
- London Mathematical LaboratoryGB9 papers
- University of WarwickGB9 papers
- King's College LondonGB8 papers
- University of CambridgeGB7 papers
- Carnegie Mellon UniversityUS5 papers
- City, University of LondonGB5 papers
- North Carolina State UniversityUS5 papers
- University of Illinois Urbana-ChampaignUS5 papers
- University of ViennaAT5 papers
241 papers
Large-Scale Qualitative Research with AI: Infrastructure, Management and Operation of the Socioscope Data Pipeline
Saadi Lahlou, Juan Pablo Caicedo, Shriya Sekhsaria +3
The Socioscope project is a pioneering effort in Large-Scale Qualitative Research (LSQR) collecting comparable, open-ended, multimedia field data on hundreds of cases and using AI…
GLP: A Grassroots, Multiagent, Concurrent, Logic Programming Language for AI
Ehud Shapiro
A grassroots platform is a multiagent distributed system in which multiple independent instances can form and operate independently of each other and of any global resource, yet ma…
Note on the Weak Convergence of Hyperplane -Quantile Functionals and Their Continuity in the Skorokhod J1 Topology
Pietro Maria Sparago
The -quantile of a stochastic process has been introduced in Miura (Hitotsubashi J Commerce Manag 27(1):15-28, 1992), and important distributional results have been de…
A tensor invariant approach to energy flux in magnetohydrodynamic turbulence
Conan M. Liptrott, Sandra C. Chapman, Bogdan Hnat +1
A scale-by-scale analysis of energy flux in the turbulent cascade can be performed using the spatially filtered magnetohydrodynamic (MHD) equations, while the gradient tensor invar…
Computational Hermeneutics: Evaluating generative AI as a cultural technology
Cody Kommers, Ruth Ahnert, Maria Antoniak +35
Generative AI systems are increasingly recognized as cultural technologies, yet current evaluation frameworks often treat culture as a variable to be measured rather than fundament…
Uncertainty-Aware Deep Hedging
Manan Poddar
Deep hedging trains neural networks to manage derivative risk under market frictions, but produces hedge ratios with no measure of model confidence -- a significant barrier to depl…