Search Results for author: Joel Jennings

Found 11 papers, 3 papers with code

FiP: a Fixed-Point Approach for Causal Generative Modeling

no code implementations10 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.

Neural Structure Learning with Stochastic Differential Equations

no code implementations6 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.

Variational Inference

Towards Causal Foundation Model: on Duality between Causal Inference and Attention

no code implementations1 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.

Causal Inference

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery

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.

Causal Discovery Variational Inference

Understanding Causality with Large Language Models: Feasibility and Opportunities

no code implementations11 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.

Decision Making

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning

1 code implementation22 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.

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design

1 code implementation27 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.

Experimental Design

Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise

no code implementations26 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).

Causal Discovery Time Series +2

NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education

no code implementations17 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.

Causal Discovery Selection bias +2

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation

no code implementations12 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.

Decision Making Experimental Design

DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention

no code implementations27 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.

OpenAI Gym reinforcement-learning +3

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