Meta-Learning For Stochastic Gradient MCMC. - Wenbo Gong 001; Yingzhen Li; José Miguel Hernández-Lobato (ICLR (Poster), 2019)

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. - Wenbo Gong 001; Sebastian Tschiatschek; Sebastian Nowozin; Richard E. Turner; José Miguel Hernández-Lobato; Cheng Zhang 005 (NeurIPS, 2019)

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. - Wenbo Gong 001; Sebastian Tschiatschek; Richard E. Turner; Sebastian Nowozin; José Miguel Hernández-Lobato; Cheng Zhang 005 (CoRR, 2019)

Sliced Kernelized Stein Discrepancy. - Wenbo Gong 001; Yingzhen Li; José Miguel Hernández-Lobato (ICLR, 2021)

Active Slices for Sliced Stein Discrepancy. - Wenbo Gong 001; Kaibo Zhang; Yingzhen Li; José Miguel Hernández-Lobato (ICML, 2021)

Interpreting diffusion score matching using normalizing flow. - Wenbo Gong 001; Yingzhen Li (CoRR, 2021)

Advances in approximate inference: combining VI and MCMC and improving on Stein discrepancy. - Wenbo Gong 001 (2021)

Simultaneous Missing Value Imputation and Structure Learning with Groups. - Pablo Morales-Alvarez; Wenbo Gong 001; Angus Lamb; Simon Woodhead 002; Simon Peyton Jones; Nick Pawlowski; Miltiadis Allamanis; Cheng Zhang 005 (NeurIPS, 2022)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Miltiadis Allamanis; Cheng Zhang 005 (CoRR, 2022)

Instructions and Guide: Causal Insights for Learning Paths in Education. - Wenbo Gong 001; Digory Smith; Zichao Wang 001; Craig Barton; Simon Woodhead 002; Nick Pawlowski; Joel Jennings; Cheng Zhang 005 (CoRR, 2022)

Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. - Wenbo Gong 001; Joel Jennings; Cheng Zhang 005; Nick Pawlowski (ICLR, 2023)

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery. - Yashas Annadani; Nick Pawlowski; Joel Jennings; Stefan Bauer; Cheng Zhang 005; Wenbo Gong 001 (NeurIPS, 2023)

Understanding Causality with Large Language Models: Feasibility and Opportunities. - Cheng Zhang 005; Stefan Bauer; Paul Bennett; Jiangfeng Gao; Wenbo Gong 001; Agrin Hilmkil; Joel Jennings; Chao Ma 019; Tom Minka; Nick Pawlowski; James Vaughan (CoRR, 2023)

BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery. - Yashas Annadani; Nick Pawlowski; Joel Jennings; Stefan Bauer; Cheng Zhang 005; Wenbo Gong 001 (CoRR, 2023)

Neural structure learning with stochastic differential equations. - Benjie Wang 001; Joel Jennings; Wenbo Gong 001 (ICLR, 2024)

The Essential Role of Causality in Foundation World Models for Embodied AI. - Tarun Gupta 002; Wenbo Gong 001; Chao Ma 019; Nick Pawlowski; Agrin Hilmkil; Meyer Scetbon; Ade Famoti; Ashley Juan Llorens; Jianfeng Gao 001; Stefan Bauer; Danica Kragic; Bernhard Schölkopf; et al. (CoRR, 2024)

SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training. - Chao Ma 019; Wenbo Gong 001; Meyer Scetbon; Edward Meeds (CoRR, 2024)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Agrin Hilmkil; Joel Jennings; et al. (Trans. Mach. Learn. Res., 2024)

Gradient Multi-Normalization for Stateless and Scalable LLM Training. - Meyer Scetbon; Chao Ma 019; Wenbo Gong 001; Edward Meeds (CoRR, 2025)

Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension. - Wenbo Gong 001; Meyer Scetbon; Chao Ma 019; Edward Meeds (CoRR, 2025)