17 citations · 90 across the 11 of their papers we have counts for
11 papers
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…
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…
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…
Simultaneous Missing Value Imputation and Structure Learning with Groups
Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb +5
Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve. One typical scenario is discovering th…
Interpreting diffusion score matching using normalizing flow
Wenbo Gong, Yingzhen Li
Scoring matching (SM), and its related counterpart, Stein discrepancy (SD) have achieved great success in model training and evaluations. However, recent research shows their limit…
Active Slices for Sliced Stein Discrepancy
Wenbo Gong, Kaibo Zhang, Yingzhen Li +1
Sliced Stein discrepancy (SSD) and its kernelized variants have demonstrated promising successes in goodness-of-fit tests and model learning in high dimensions. Despite their theor…
Sliced Kernelized Stein Discrepancy
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. We address this issue by pr…
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
Wenbo Gong, Sebastian Tschiatschek, Richard Turner +3
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 acquirin…
Meta-Learning for Stochastic Gradient MCMC
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. However, existing SG-MCMC…