SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training
Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds
ICML 2025 | February 2025
Chao Ma
Papiers de Collaboration
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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
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Gradient Multi-Normalization for Stateless and Scalable LLM Training
Meyer Scetbon, Chao Ma, Wenbo Gong, Edward Meeds
ArXiv | February 2025
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A Fixed-Point Approach for Causal Generative Modeling
Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
ICML 2024 | May 2024
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Towards Causal Foundation Model: on Duality between Causal Inference and Attention
Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma
ICML 2024 | September 2023
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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
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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
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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
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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
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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
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Identifiable Generative Models for Missing Not at Random Data Imputation
Chao Ma, Cheng Zhang
NeurIPS | December 2021
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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
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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
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SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training
Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds
ICML 2025 | February 2025
-
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
-
Gradient Multi-Normalization for Stateless and Scalable LLM Training
Meyer Scetbon, Chao Ma, Wenbo Gong, Edward Meeds
ArXiv | February 2025
-
A Fixed-Point Approach for Causal Generative Modeling
Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
ICML 2024 | May 2024
-
Towards Causal Foundation Model: on Duality between Causal Inference and Attention
Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma
ICML 2024 | September 2023
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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
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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
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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
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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
-
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
-
Identifiable Generative Models for Missing Not at Random Data Imputation
Chao Ma, Cheng Zhang
NeurIPS | December 2021
-
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
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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
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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
-
Gradient Multi-Normalization for Stateless and Scalable LLM Training
Meyer Scetbon, Chao Ma, Wenbo Gong, Edward Meeds
ArXiv | February 2025
-
-
-
SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training
Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds
ICML 2025 | February 2025
-
A Fixed-Point Approach for Causal Generative Modeling
Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
ICML 2024 | May 2024
-
Towards Causal Foundation Model: on Duality between Causal Inference and Attention
Jiaqi Zhang, Joel Jennings, Cheng Zhang, Chao Ma
ICML 2024 | September 2023
-
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
-
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
-
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
-
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
-
Identifiable Generative Models for Missing Not at Random Data Imputation
Chao Ma, Cheng Zhang
NeurIPS | December 2021
-
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
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-
-
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
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