Wenbo Gong - RankMe

27 papers A* 8Journal 17Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	ARO: A New Lens On Matrix Optimization For Large Models. CoRR Wenbo Gong, Javier Zazo, Qijun Luo, Puqian Wang, James Hensman, Chao Ma
2025	J	`jnl`	Gradient Multi-Normalization for Stateless and Scalable LLM Training. CoRR Meyer Scetbon, Chao Ma, Wenbo Gong, Edward Meeds
2025	A*	`conf`	SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training. ICML Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds
2025	J	`jnl`	Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension. CoRR Wenbo Gong, Meyer Scetbon, Chao Ma, Edward Meeds
2024	J	`jnl`	Deep End-to-end Causal Inference. Trans. Mach. Learn. Res. 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
2024	A*	`conf`	Neural structure learning with stochastic differential equations. ICLR Benjie Wang, Joel Jennings, Wenbo Gong
2024	J	`jnl`	SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training. CoRR Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds
2024	J	`jnl`	The Essential Role of Causality in Foundation World Models for Embodied AI. CoRR 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
2023	A*	`conf`	BayesDAG: Gradient-Based Posterior Inference for Causal Discovery. NeurIPS Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong
2023	J	`jnl`	BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery. CoRR Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong
2023	J	`jnl`	Neural Structure Learning with Stochastic Differential Equations. CoRR Benjie Wang, Joel Jennings, Wenbo Gong
2023	A*	`conf`	Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. ICLR Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski
2023	J	`jnl`	Understanding Causality with Large Language Models: Feasibility and Opportunities. CoRR Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan
2022	J	`jnl`	Deep End-to-end Causal Inference. CoRR Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
2022	J	`jnl`	Instructions and Guide: Causal Insights for Learning Paths in Education. CoRR Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang
2022	J	`jnl`	Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise. CoRR Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski
2022	A*	`conf`	Simultaneous Missing Value Imputation and Structure Learning with Groups. NeurIPS Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
2021	A*	`conf`	Active Slices for Sliced Stein Discrepancy. ICML Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato
2021	J	`jnl`	Active Slices for Sliced Stein Discrepancy. CoRR Wenbo Gong, Kaibo Zhang, Yingzhen Li, José Miguel Hernández-Lobato
2021	—	`—`	Advances in approximate inference: combining VI and MCMC and improving on Stein discrepancy. Wenbo Gong
2021	J	`jnl`	Interpreting diffusion score matching using normalizing flow. CoRR Wenbo Gong, Yingzhen Li
2021	A*	`conf`	Sliced Kernelized Stein Discrepancy. ICLR Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2020	J	`jnl`	Sliced Kernelized Stein Discrepancy. CoRR Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2019	J	`jnl`	Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model. CoRR Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang
2019	A*	`conf`	Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. NeurIPS Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
2019	—	`conf`	Meta-Learning For Stochastic Gradient MCMC. ICLR (Poster) Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018	J	`jnl`	Meta-Learning for Stochastic Gradient MCMC. CoRR Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato