Chao Ma

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
  
  ArXiv | February 2025
  
  DOI
- Gradient Multi-Normalization for Stateless and Scalable LLM Training
  
  Meyer Scetbon, Chao Ma, Wenbo Gong, Edward Meeds
  
  NeurIPS 2025 | February 2025
  
  DOI
- A Fixed-Point Approach for Causal Generative Modeling
  
  Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
  
  ICML 2024 | May 2024
  
  DOI Publication Publication
- Towards Causal Foundation Model: on Duality between Causal Inference and Attention
  
  Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma
  
  ICML 2024 | September 2023
  
  DOI Publication Publication Publication
- Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
  
  Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
  
  ICLR 2023 | May 2023
  
  Publication Projet
- A Causal AI Suite for Decision-Making
  
  Emre Kiciman, Eleanor Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Osazuwa Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang
  
  NeurIPS 2022 Workshop on Causality for Real-world Impact | December 2022
  
  Publication Projet Projet Projet
- 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
- Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
  
  Ignacio Peis, Chao Ma, Jose Miguel Hernandez Lobato
  
  2022 Neural Information Processing Systems | September 2022
  
  Publication
- Deep End-to-end Causal Inference
  
  Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman (emrek), Amit Sharma, Martin Kukla, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
  
  MSR-TR-2022-1 | February 2022
  
  Publié par Microsoft
  
  Publication Projet Projet
- Identifiable Generative Models for Missing Not at Random Data Imputation
  
  Chao Ma, Cheng Zhang
  
  NeurIPS | December 2021
  
  Publication Projet
- VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
  
  Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
  
  2020 Neural Information Processing Systems | December 2020
  
  PDF PDF Publication Publication Publication Publication Projet
- EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
  
  Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez Lobato, Sebastian Nowozin, Cheng Zhang
  
  International Conference on Machine Learning | June 2019
  
  Publication 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
  
  ArXiv | February 2025
  
  DOI
- Gradient Multi-Normalization for Stateless and Scalable LLM Training
  
  Meyer Scetbon, Chao Ma, Wenbo Gong, Edward Meeds
  
  NeurIPS 2025 | February 2025
  
  DOI
- A Fixed-Point Approach for Causal Generative Modeling
  
  Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
  
  ICML 2024 | May 2024
  
  DOI Publication Publication
- Towards Causal Foundation Model: on Duality between Causal Inference and Attention
  
  Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma
  
  ICML 2024 | September 2023
  
  DOI Publication Publication Publication
- Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
  
  Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
  
  ICLR 2023 | May 2023
  
  Publication Projet
- A Causal AI Suite for Decision-Making
  
  Emre Kiciman, Eleanor Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Osazuwa Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang
  
  NeurIPS 2022 Workshop on Causality for Real-world Impact | December 2022
  
  Publication Projet Projet Projet
- 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
- Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
  
  Ignacio Peis, Chao Ma, Jose Miguel Hernandez Lobato
  
  2022 Neural Information Processing Systems | September 2022
  
  Publication
- Deep End-to-end Causal Inference
  
  Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman (emrek), Amit Sharma, Martin Kukla, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
  
  MSR-TR-2022-1 | February 2022
  
  Publié par Microsoft
  
  Publication Projet Projet
- Identifiable Generative Models for Missing Not at Random Data Imputation
  
  Chao Ma, Cheng Zhang
  
  NeurIPS | December 2021
  
  Publication Projet
- VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
  
  Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
  
  2020 Neural Information Processing Systems | December 2020
  
  PDF PDF Publication Publication Publication Publication Projet
- EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
  
  Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez Lobato, Sebastian Nowozin, Cheng Zhang
  
  International Conference on Machine Learning | June 2019
  
  Publication Projet

- SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training
  
  Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds
  
  ICML 2025 | February 2025
  
  DOI
- Gradient Multi-Normalization for Stateless and Scalable LLM Training
  
  Meyer Scetbon, Chao Ma, Wenbo Gong, Edward Meeds
  
  NeurIPS 2025 | February 2025
  
  DOI
- A Fixed-Point Approach for Causal Generative Modeling
  
  Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
  
  ICML 2024 | May 2024
  
  DOI Publication Publication
- Towards Causal Foundation Model: on Duality between Causal Inference and Attention
  
  Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma
  
  ICML 2024 | September 2023
  
  DOI Publication Publication Publication
- Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
  
  Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
  
  ICLR 2023 | May 2023
  
  Publication Projet
- A Causal AI Suite for Decision-Making
  
  Emre Kiciman, Eleanor Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Osazuwa Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang
  
  NeurIPS 2022 Workshop on Causality for Real-world Impact | December 2022
  
  Publication Projet Projet Projet
- 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
- Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
  
  Ignacio Peis, Chao Ma, Jose Miguel Hernandez Lobato
  
  2022 Neural Information Processing Systems | September 2022
  
  Publication
- Identifiable Generative Models for Missing Not at Random Data Imputation
  
  Chao Ma, Cheng Zhang
  
  NeurIPS | December 2021
  
  Publication Projet
- VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
  
  Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
  
  2020 Neural Information Processing Systems | December 2020
  
  PDF PDF Publication Publication Publication Publication Projet
- EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
  
  Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez Lobato, Sebastian Nowozin, Cheng Zhang
  
  International Conference on Machine Learning | June 2019
  
  Publication Projet
- Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension
  
  Wenbo Gong, Meyer Scetbon, Chao Ma, Edward Meeds
  
  ArXiv | February 2025
  
  DOI
- Deep End-to-end Causal Inference
  
  Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman (emrek), Amit Sharma, Martin Kukla, Nick Pawlowski, Miltos Allamanis, Cheng Zhang
  
  MSR-TR-2022-1 | February 2022
  
  Publié par Microsoft
  
  Publication Projet Projet

Chao Ma

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

A Fixed-Point Approach for Causal Generative Modeling

Towards Causal Foundation Model: on Duality between Causal Inference and Attention

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning

A Causal AI Suite for Decision-Making

Deep End-to-end Causal Inference

Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo

Deep End-to-end Causal Inference

Identifiable Generative Models for Missing Not at Random Data Imputation

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

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

A Fixed-Point Approach for Causal Generative Modeling

Towards Causal Foundation Model: on Duality between Causal Inference and Attention

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning

A Causal AI Suite for Decision-Making

Deep End-to-end Causal Inference

Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo

Deep End-to-end Causal Inference

Identifiable Generative Models for Missing Not at Random Data Imputation

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training

Gradient Multi-Normalization for Stateless and Scalable LLM Training

A Fixed-Point Approach for Causal Generative Modeling

Towards Causal Foundation Model: on Duality between Causal Inference and Attention

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning

A Causal AI Suite for Decision-Making

Deep End-to-end Causal Inference

Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo

Identifiable Generative Models for Missing Not at Random Data Imputation

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

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

Deep End-to-end Causal Inference