4 citations · 6 across the 2 of their papers we have counts for
6 papers
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…
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…