no code implementations • 6 Feb 2024 • Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Marc Rigter, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, Danica Kragic, Bernhard Schölkopf, Cheng Zhang
The study of causality lends itself to the construction of veridical world models, which are crucial for accurately predicting the outcomes of possible interactions.
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.
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.
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.
1 code implementation • 4 Feb 2022 • Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.
1 code implementation • 15 Oct 2021 • Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve.
no code implementations • ICML Workshop INNF 2021 • Wenbo Gong, Yingzhen Li
Specifically, we theoretically prove that DSM (or DSD) is equivalent to the original score matching (or Stein discrepancy) evaluated in the transformed space defined by the normalizing flow, where the diffusion matrix is the inverse of the flow's Jacobian matrix.
1 code implementation • 5 Feb 2021 • Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato
First, we provide theoretical results stating that the requirement of using optimal slicing directions in the kernelized version of SSD can be relaxed, validating the resulting discrepancy with finite random slicing directions.
1 code implementation • ICLR 2021 • Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
Kernelized Stein discrepancy (KSD), though being extensively used in goodness-of-fit tests and model learning, suffers from the curse-of-dimensionality.
1 code implementation • NeurIPS 2019 • Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
In this paper, we address the ice-start problem, i. e., the challenge of deploying machine learning models when only a little or no training data is initially available, and acquiring each feature element of data is associated with costs.
1 code implementation • 13 Aug 2019 • Wenbo Gong, Sebastian Tschiatschek, Richard Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang
In this paper we introduce the ice-start problem, i. e., the challenge of deploying machine learning models when only little or no training data is initially available, and acquiring each feature element of data is associated with costs.
1 code implementation • ICLR 2019 • Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
Stochastic gradient Markov chain Monte Carlo (SG-MCMC) has become increasingly popular for simulating posterior samples in large-scale Bayesian modeling.