16 citations · 58 across the 14 of their papers we have counts for
16 papers
A Fixed-Point Approach for Causal Generative Modeling
Meyer Scetbon, Joel Jennings, Agrin Hilmkil +2
We propose a novel formalism for describing Structural Causal Models (SCMs) as fixed-point problems on causally ordered variables, eliminating the need for Directed Acyclic Graphs…
Neural Structure Learning with Stochastic Differential Equations
Benjie Wang, Joel Jennings, Wenbo Gong
Discovering the underlying relationships among variables from temporal observations has been a longstanding challenge in numerous scientific disciplines, including biology, finance…
Towards Causal Foundation Model: on Duality between Causal Inference and Attention
Jiaqi Zhang, Joel Jennings, Agrin Hilmkil +3
Foundation models have brought changes to the landscape of machine learning, demonstrating sparks of human-level intelligence across a diverse array of tasks. However, a gap persis…
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
Yashas Annadani, Nick Pawlowski, Joel Jennings +3
Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks. However…
Understanding Causality with Large Language Models: Feasibility and Opportunities
Cheng Zhang, Stefan Bauer, Paul Bennett +8
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. We believe that…
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
Matthew Ashman, Chao Ma, Agrin Hilmkil +2
Latent confounding has been a long-standing obstacle for causal reasoning from observational data. One popular approach is to model the data using acyclic directed mixed graphs (AD…
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
Desi R. Ivanova, Joel Jennings, Tom Rainforth +2
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 context…
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise
Wenbo Gong, Joel Jennings, Cheng Zhang +1
Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains such as climate science, finance, and healthc…
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education
Wenbo Gong, Digory Smith, Zichao Wang +5
In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data. The first is to identify th…
Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation
Desi R. Ivanova, Joel Jennings, Cheng Zhang +1
The real-world testing of decisions made using causal machine learning models is an essential prerequisite for their successful application. We focus on evaluating and improving co…
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster +9
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making. However, research on causal discovery…
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
David Mguni, Usman Islam, Yaqi Sun +7
Reinforcement learning (RL) involves performing exploratory actions in an unknown system. This can place a learning agent in dangerous and potentially catastrophic system states. C…
Learning in Nonzero-Sum Stochastic Games with Potentials
David Mguni, Yutong Wu, Yali Du +6
Multi-agent reinforcement learning (MARL) has become effective in tackling discrete cooperative game scenarios. However, MARL has yet to penetrate settings beyond those modelled by…
Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems
David Mguni, Joel Jennings, Sergio Valcarcel Macua +3
Many real-world systems such as taxi systems, traffic networks and smart grids involve self-interested actors that perform individual tasks in a shared environment. However, in suc…
Decentralised Learning in Systems with Many, Many Strategic Agents
David Mguni, Joel Jennings, Enrique Munoz de Cote
Although multi-agent reinforcement learning can tackle systems of strategically interacting entities, it currently fails in scalability and lacks rigorous convergence guarantees. C…
Cancer Metastasis: Collective Invasion in Heterogeneous Multicellular Systems
Adrien Hallou, Joel Jennings, Alexandre Kabla
Heterogeneity within tumour cell populations is associated with an increase in malignancy and appears to play an important role during cancer metastasis. Using in silico experiment…