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Found 10 papers, 1 papers with code
Causally Abstracted Multi-armed Bandits
Multi-armed bandits (MAB) and causal MABs (CMAB) are established frameworks for decision-making problems.
Interventionally Consistent Surrogates for Agent-based Simulators
Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system's constituent agents.
Some challenges of calibrating differentiable agent-based models
Agent-based models (ABMs) are a promising approach to modelling and reasoning about complex systems, yet their application in practice is impeded by their complexity, discrete nature, and the difficulty of performing parameter inference and optimisation tasks.
Bayesian calibration of differentiable agent-based models
Agent-based modelling (ABMing) is a powerful and intuitive approach to modelling complex systems; however, the intractability of ABMs' likelihood functions and the non-differentiability of the mathematical operations comprising these models present a challenge to their use in the real world.
Calibrating Agent-based Models to Microdata with Graph Neural Networks
Calibrating agent-based models (ABMs) to data is among the most fundamental requirements to ensure the model fulfils its desired purpose.
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible.
Black-box Bayesian inference for economic agent-based models
We present benchmarking experiments in which we demonstrate that neural network based black-box methods provide state of the art parameter inference for economic simulation models, and crucially are compatible with generic multivariate time-series data.
Approximate Bayesian Computation with Path Signatures
Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable.
Deep Signature Statistics for Likelihood-free Time-series Models
Our approach leverages deep signature transforms, trained concurrently with a neural density estimator, to produce informative statistics for multivariate sequential data that encode important geometric properties of the underlying path.
Public risk perception and emotion on Twitter during the Covid-19 pandemic
Signatures of public emotion and attention are present in social media data, thus natural language analysis of this text enables near-to-real-time monitoring of indicators of public risk perception.
Social and Information Networks Computers and Society 62P15, 91D30 J.4
