Wenbo Gong

According to our database, Wenbo Gong authored at least 17 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

Deep End-to-end Causal Inference.

Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Agrin Hilmkil, Joel Jennings, Meyer Scetbon, Miltiadis Allamanis, Cheng Zhang

Trans. Mach. Learn. Res., 2024

The Essential Role of Causality in Foundation World Models for Embodied AI.

Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, Danica Kragic, Bernhard Schölkopf, Cheng Zhang

CoRR, 2024

Neural structure learning with stochastic differential equations.

Benjie Wang, Joel Jennings, Wenbo Gong

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery.

Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

CoRR, 2023

Understanding Causality with Large Language Models: Feasibility and Opportunities.

Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan

CoRR, 2023

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery.

Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise.

Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Instructions and Guide: Causal Insights for Learning Paths in Education.

Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang

CoRR, 2022

Deep End-to-end Causal Inference.

Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

CoRR, 2022

Simultaneous Missing Value Imputation and Structure Learning with Groups.

Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Advances in approximate inference: combining VI and MCMC and improving on Stein discrepancy.

Wenbo Gong

PhD thesis, 2021

Interpreting diffusion score matching using normalizing flow.

Wenbo Gong, Yingzhen Li

CoRR, 2021

Active Slices for Sliced Stein Discrepancy.

Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato

Proceedings of the 38th International Conference on Machine Learning, 2021

Sliced Kernelized Stein Discrepancy.

Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato

Proceedings of the 9th International Conference on Learning Representations, 2021

2019

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model.

Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang

CoRR, 2019

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model.

Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Meta-Learning For Stochastic Gradient MCMC.

Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato

Proceedings of the 7th International Conference on Learning Representations, 2019