Wenbo Gong

Senior Researcher

Publications

Afficher par:

- SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training
  
  Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds
  
  ICML 2025 | February 2025
  
  DOI
- Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension
  
  Wenbo Gong, Meyer Scetbon, Chao Ma, Edward Meeds
  
  ICLR 2026 | February 2025
  
  Publication
- Gradient Multi-Normalization for Stateless and Scalable LLM Training
  
  Meyer Scetbon, Chao Ma, Wenbo Gong, Edward Meeds
  
  NeurIPS 2025 | February 2025
  
  DOI
- Neural Structure Learning with Stochastic Differential Equations
  
  Benjie Wang, Joel Jennings, Wenbo Gong
  
  ICLR 2024 | November 2023
  
  DOI Publication
- Deep End-to-end Causal Inference
  
  Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
  
  NeurIPS 2022 Workshop on Causal Machine Learning for Real-World Impact (CML4Impact 2022) | November 2022
  
  Publication Projet Projet
- Rhino: Deep Causal Temporal Relationship Learning with history-dependent noise
  
  Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski
  
  NeurIPS 2022 Workshop on Causal Machine Learning for Real-World Impact (CML4Impact 2022) | November 2022
  
  Publication
- Simultaneous Missing Value Imputation and Structure Learning with Groups
  
  Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
  
  2022 Neural Information Processing Systems | February 2022
  
  Publication Publication Projet
- Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
  
  Wenbo Gong, Sebastian Tschiatschek, Richard Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang
  
  Thirty-third Conference on Neural Information Processing Systems (NeurIPS) | August 2019
  
  Publication Github Projet

- SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training
  
  Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds
  
  ICML 2025 | February 2025
  
  DOI
- Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension
  
  Wenbo Gong, Meyer Scetbon, Chao Ma, Edward Meeds
  
  ICLR 2026 | February 2025
  
  Publication
- Gradient Multi-Normalization for Stateless and Scalable LLM Training
  
  Meyer Scetbon, Chao Ma, Wenbo Gong, Edward Meeds
  
  NeurIPS 2025 | February 2025
  
  DOI
- Neural Structure Learning with Stochastic Differential Equations
  
  Benjie Wang, Joel Jennings, Wenbo Gong
  
  ICLR 2024 | November 2023
  
  DOI Publication
- Deep End-to-end Causal Inference
  
  Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
  
  NeurIPS 2022 Workshop on Causal Machine Learning for Real-World Impact (CML4Impact 2022) | November 2022
  
  Publication Projet Projet
- Rhino: Deep Causal Temporal Relationship Learning with history-dependent noise
  
  Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski
  
  NeurIPS 2022 Workshop on Causal Machine Learning for Real-World Impact (CML4Impact 2022) | November 2022
  
  Publication
- Simultaneous Missing Value Imputation and Structure Learning with Groups
  
  Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
  
  2022 Neural Information Processing Systems | February 2022
  
  Publication Publication Projet
- Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
  
  Wenbo Gong, Sebastian Tschiatschek, Richard Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang
  
  Thirty-third Conference on Neural Information Processing Systems (NeurIPS) | August 2019
  
  Publication Github Projet

- SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training
  
  Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds
  
  ICML 2025 | February 2025
  
  DOI
- Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension
  
  Wenbo Gong, Meyer Scetbon, Chao Ma, Edward Meeds
  
  ICLR 2026 | February 2025
  
  Publication
- Gradient Multi-Normalization for Stateless and Scalable LLM Training
  
  Meyer Scetbon, Chao Ma, Wenbo Gong, Edward Meeds
  
  NeurIPS 2025 | February 2025
  
  DOI
- Neural Structure Learning with Stochastic Differential Equations
  
  Benjie Wang, Joel Jennings, Wenbo Gong
  
  ICLR 2024 | November 2023
  
  DOI Publication
- Deep End-to-end Causal Inference
  
  Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
  
  NeurIPS 2022 Workshop on Causal Machine Learning for Real-World Impact (CML4Impact 2022) | November 2022
  
  Publication Projet Projet
- Rhino: Deep Causal Temporal Relationship Learning with history-dependent noise
  
  Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski
  
  NeurIPS 2022 Workshop on Causal Machine Learning for Real-World Impact (CML4Impact 2022) | November 2022
  
  Publication
- Simultaneous Missing Value Imputation and Structure Learning with Groups
  
  Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
  
  2022 Neural Information Processing Systems | February 2022
  
  Publication Publication Projet
- Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
  
  Wenbo Gong, Sebastian Tschiatschek, Richard Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang
  
  Thirty-third Conference on Neural Information Processing Systems (NeurIPS) | August 2019
  
  Publication Github Projet

Wenbo Gong

Publications

SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training

Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension

Gradient Multi-Normalization for Stateless and Scalable LLM Training

Neural Structure Learning with Stochastic Differential Equations

Deep End-to-end Causal Inference

Rhino: Deep Causal Temporal Relationship Learning with history-dependent noise

Simultaneous Missing Value Imputation and Structure Learning with Groups

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

SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training

Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension

Gradient Multi-Normalization for Stateless and Scalable LLM Training

Neural Structure Learning with Stochastic Differential Equations

Deep End-to-end Causal Inference

Rhino: Deep Causal Temporal Relationship Learning with history-dependent noise

Simultaneous Missing Value Imputation and Structure Learning with Groups

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

SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training

Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension

Gradient Multi-Normalization for Stateless and Scalable LLM Training

Neural Structure Learning with Stochastic Differential Equations

Deep End-to-end Causal Inference

Rhino: Deep Causal Temporal Relationship Learning with history-dependent noise

Simultaneous Missing Value Imputation and Structure Learning with Groups

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

Contact Wenbo Gong

Microsoft Research Lab – Cambridge