Urszula Chajewska

Principal Applied Scientist

论文与出版物

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- Methods for recovering conditional independence graphs (Abstract Reprint)
  
  Harsh Shrivastava, Urszula Chajewska
  
  IJCAI 2024 | July 2024
  
  DOI
- Federated Learning with Neural Graphical Models
  
  Urszula Chajewska, Harsh Shrivastava
  
  Workshop on Advancements in Federated Learning (ECML-PKDD 2023) | September 2023
  
  Publication DOI
- Neural Graphical Models
  
  Harsh Shrivastava, Urszula Chajewska
  
  European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023) | September 2023
  
  Publication DOI 项目
- Methods for Recovering Conditional Independence Graphs: A Survey
  
  Harsh Shrivastava, Urszula Chajewska
  
  The Journal of Artificial Intelligence Research (JAIR) | September 2023
  
  Publication DOI 项目
- Knowledge Propagation over Conditional Independence Graphs
  
  Urszula Chajewska, Harsh Shrivastava
  
  August 2023
  
  Arxiv preprint | August 2023
  
  Publication DOI 项目
- Neural Graph Revealers
  
  Harsh Shrivastava, Urszula Chajewska
  
  Workshop Machine Learning for Multimodal Healthcare Data (ICML 2023) | July 2023
  
  Publication DOI 项目
- Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
  
  Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin , Rich Caruana
  
  Conference on Health, Inference, and Learning (CHIL) 2023 | June 2023
  
  Publication
- uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs
  
  Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen
  
  Workshop on New Frontiers in Graph Learning (NeurIPS 2022) | December 2022
  
  PDF 项目
- Discovering Distribution Shifts using Latent Space Representations
  
  Leo Betthauser, Urszula Chajewska, Maurice Diesendruck, Rohith Pesala
  
  February 2022
  
  arXiv | February 2022
  
  Publication 项目
- Axiomatic Interpretability for Multiclass Additive Models
  
  Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
  
  The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19) | August 2019
  
  Publication

- Methods for recovering conditional independence graphs (Abstract Reprint)
  
  Harsh Shrivastava, Urszula Chajewska
  
  IJCAI 2024 | July 2024
  
  DOI
- Federated Learning with Neural Graphical Models
  
  Urszula Chajewska, Harsh Shrivastava
  
  Workshop on Advancements in Federated Learning (ECML-PKDD 2023) | September 2023
  
  Publication DOI
- Neural Graphical Models
  
  Harsh Shrivastava, Urszula Chajewska
  
  European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023) | September 2023
  
  Publication DOI 项目
- Methods for Recovering Conditional Independence Graphs: A Survey
  
  Harsh Shrivastava, Urszula Chajewska
  
  The Journal of Artificial Intelligence Research (JAIR) | September 2023
  
  Publication DOI 项目
- Knowledge Propagation over Conditional Independence Graphs
  
  Urszula Chajewska, Harsh Shrivastava
  
  August 2023
  
  Arxiv preprint | August 2023
  
  Publication DOI 项目
- Neural Graph Revealers
  
  Harsh Shrivastava, Urszula Chajewska
  
  Workshop Machine Learning for Multimodal Healthcare Data (ICML 2023) | July 2023
  
  Publication DOI 项目
- Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
  
  Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin , Rich Caruana
  
  Conference on Health, Inference, and Learning (CHIL) 2023 | June 2023
  
  Publication
- uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs
  
  Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen
  
  Workshop on New Frontiers in Graph Learning (NeurIPS 2022) | December 2022
  
  PDF 项目
- Discovering Distribution Shifts using Latent Space Representations
  
  Leo Betthauser, Urszula Chajewska, Maurice Diesendruck, Rohith Pesala
  
  February 2022
  
  arXiv | February 2022
  
  Publication 项目
- Axiomatic Interpretability for Multiclass Additive Models
  
  Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
  
  The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19) | August 2019
  
  Publication
- Federated Learning with Neural Graphical Models
  
  Urszula Chajewska, Harsh Shrivastava
  
  Workshop on Advancements in Federated Learning (ECML-PKDD 2023) | September 2023
  
  Publication DOI
- Knowledge Propagation over Conditional Independence Graphs
  
  Urszula Chajewska, Harsh Shrivastava
  
  August 2023
  
  Arxiv preprint | August 2023
  
  Publication DOI 项目
- uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs
  
  Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen
  
  Workshop on New Frontiers in Graph Learning (NeurIPS 2022) | December 2022
  
  PDF 项目
- Federated Learning with Neural Graphical Models
  
  Urszula Chajewska, Harsh Shrivastava
  
  Workshop on Advancements in Federated Learning (ECML-PKDD 2023) | September 2023
  
  Publication DOI
- Neural Graphical Models
  
  Harsh Shrivastava, Urszula Chajewska
  
  European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023) | September 2023
  
  Publication DOI 项目
- Knowledge Propagation over Conditional Independence Graphs
  
  Urszula Chajewska, Harsh Shrivastava
  
  August 2023
  
  Arxiv preprint | August 2023
  
  Publication DOI 项目
- Neural Graph Revealers
  
  Harsh Shrivastava, Urszula Chajewska
  
  Workshop Machine Learning for Multimodal Healthcare Data (ICML 2023) | July 2023
  
  Publication DOI 项目
- Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
  
  Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin , Rich Caruana
  
  Conference on Health, Inference, and Learning (CHIL) 2023 | June 2023
  
  Publication

- Methods for recovering conditional independence graphs (Abstract Reprint)
  
  Harsh Shrivastava, Urszula Chajewska
  
  IJCAI 2024 | July 2024
  
  DOI
- Federated Learning with Neural Graphical Models
  
  Urszula Chajewska, Harsh Shrivastava
  
  Workshop on Advancements in Federated Learning (ECML-PKDD 2023) | September 2023
  
  Publication DOI
- Neural Graphical Models
  
  Harsh Shrivastava, Urszula Chajewska
  
  European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023) | September 2023
  
  Publication DOI 项目
- Neural Graph Revealers
  
  Harsh Shrivastava, Urszula Chajewska
  
  Workshop Machine Learning for Multimodal Healthcare Data (ICML 2023) | July 2023
  
  Publication DOI 项目
- Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
  
  Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin , Rich Caruana
  
  Conference on Health, Inference, and Learning (CHIL) 2023 | June 2023
  
  Publication
- uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs
  
  Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen
  
  Workshop on New Frontiers in Graph Learning (NeurIPS 2022) | December 2022
  
  PDF 项目
- Axiomatic Interpretability for Multiclass Additive Models
  
  Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
  
  The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19) | August 2019
  
  Publication
- Knowledge Propagation over Conditional Independence Graphs
  
  Urszula Chajewska, Harsh Shrivastava
  
  August 2023
  
  Arxiv preprint | August 2023
  
  Publication DOI 项目
- Discovering Distribution Shifts using Latent Space Representations
  
  Leo Betthauser, Urszula Chajewska, Maurice Diesendruck, Rohith Pesala
  
  February 2022
  
  arXiv | February 2022
  
  Publication 项目
- Methods for Recovering Conditional Independence Graphs: A Survey
  
  Harsh Shrivastava, Urszula Chajewska
  
  The Journal of Artificial Intelligence Research (JAIR) | September 2023
  
  Publication DOI 项目

Urszula Chajewska

论文与出版物

Methods for recovering conditional independence graphs (Abstract Reprint)

Federated Learning with Neural Graphical Models

Neural Graphical Models

Methods for Recovering Conditional Independence Graphs: A Survey

Knowledge Propagation over Conditional Independence Graphs

Neural Graph Revealers

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?

uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs

Discovering Distribution Shifts using Latent Space Representations

Axiomatic Interpretability for Multiclass Additive Models

Methods for recovering conditional independence graphs (Abstract Reprint)

Federated Learning with Neural Graphical Models

Neural Graphical Models

Methods for Recovering Conditional Independence Graphs: A Survey

Knowledge Propagation over Conditional Independence Graphs

Neural Graph Revealers

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?

uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs

Discovering Distribution Shifts using Latent Space Representations

Axiomatic Interpretability for Multiclass Additive Models

Federated Learning with Neural Graphical Models

Knowledge Propagation over Conditional Independence Graphs

uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs

Federated Learning with Neural Graphical Models

Neural Graphical Models

Knowledge Propagation over Conditional Independence Graphs

Neural Graph Revealers

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?

Methods for recovering conditional independence graphs (Abstract Reprint)

Federated Learning with Neural Graphical Models

Neural Graphical Models

Neural Graph Revealers

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?

uGLAD: A Deep Learning Model to Recover Conditional Independence Graphs

Axiomatic Interpretability for Multiclass Additive Models

Knowledge Propagation over Conditional Independence Graphs

Discovering Distribution Shifts using Latent Space Representations

Methods for Recovering Conditional Independence Graphs: A Survey