no code implementations • 10 Apr 2024 • Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
Based on this, we design a two-stage causal generative model that first infers the causal order from observations in a zero-shot manner, thus by-passing the search, and then learns the generative fixed-point SCM on the ordered variables.
no code implementations • 6 Nov 2023 • Benjie Wang, Joel Jennings, Wenbo Gong
Unfortunately, most existing structure learning approaches assume that the underlying process evolves in discrete-time and/or observations occur at regular time intervals.
no code implementations • 1 Oct 2023 • JiaQi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma
Foundation models have brought changes to the landscape of machine learning, demonstrating sparks of human-level intelligence across a diverse array of tasks.
1 code implementation • NeurIPS 2023 • Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong
Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks.
no code implementations • 11 Apr 2023 • Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan
We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question.
1 code implementation • 22 Mar 2023 • Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
In this work, we further extend the existing body of work and develop a novel gradient-based approach to learning an ADMG with non-linear functional relations from observational data.
1 code implementation • 27 Feb 2023 • Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED -- a general, model-agnostic framework for designing contextual experiments using information-theoretic principles.
no code implementations • 26 Oct 2022 • Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski
Given the complexity of real-world relationships and the nature of observations in discrete time, causal discovery methods need to consider non-linear relations between variables, instantaneous effects and history-dependent noise (the change of noise distribution due to past actions).
no code implementations • 17 Aug 2022 • Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang
In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data.
no code implementations • 12 Jul 2022 • Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster
In this paper we introduce a model-agnostic framework for gathering data to evaluate and improve contextual decision making through Bayesian Experimental Design.
no code implementations • 27 Oct 2021 • David Mguni, Usman Islam, Yaqi Sun, Xiuling Zhang, Joel Jennings, Aivar Sootla, Changmin Yu, Ziyan Wang, Jun Wang, Yaodong Yang
In this paper, we introduce a new generation of RL solvers that learn to minimise safety violations while maximising the task reward to the extent that can be tolerated by the safe policy.