Urszula Chajewska

28 papers A* 3A 4C 1Journal 15Unranked 4

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Methods for Recovering Conditional Independence Graphs: A Survey. J. Artif. Intell. Res. Harsh Shrivastava, Urszula Chajewska
2024	A*	`conf`	Methods for recovering conditional independence graphs (Abstract Reprint). IJCAI Harsh Shrivastava, Urszula Chajewska
2023	—	`conf`	Federated Learning with Neural Graphical Models. PKDD/ECML Workshops (5) Urszula Chajewska, Harsh Shrivastava
2023	J	`jnl`	Federated Learning with Neural Graphical Models. CoRR Urszula Chajewska, Harsh Shrivastava
2023	J	`jnl`	Knowledge Propagation over Conditional Independence Graphs. CoRR Urszula Chajewska, Harsh Shrivastava
2023	—	`conf`	Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana
2023	J	`jnl`	Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana
2023	—	`conf`	Neural Graph Revealers. ML4MHD Harsh Shrivastava, Urszula Chajewska
2023	J	`jnl`	Neural Graph Revealers. CoRR Harsh Shrivastava, Urszula Chajewska
2023	C	`conf`	Neural Graphical Models. ECSQARU Harsh Shrivastava, Urszula Chajewska
2022	J	`jnl`	Discovering Distribution Shifts using Latent Space Representations. CoRR Leo Betthauser, Urszula Chajewska, Maurice Diesendruck, Rohith Pesala
2022	J	`jnl`	Methods for Recovering Conditional Independence Graphs: A Survey. CoRR Harsh Shrivastava, Urszula Chajewska
2022	J	`jnl`	Neural Graphical Models. CoRR Harsh Shrivastava, Urszula Chajewska
2022	J	`jnl`	uGLAD: Sparse graph recovery by optimizing deep unrolled networks. CoRR Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen
2019	A*	`conf`	Axiomatic Interpretability for Multiclass Additive Models. KDD Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
2018	J	`jnl`	Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models. CoRR Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana
2013	J	`jnl`	Aggregating Learned Probabilistic Beliefs CoRR Pedrito Maynard-Reid II, Urszula Chajewska
2013	J	`jnl`	Defining Explanation in Probabilistic Systems CoRR Urszula Chajewska, Joseph Y. Halpern
2013	J	`jnl`	Utilities as Random Variables: Density Estimation and Structure Discovery CoRR Urszula Chajewska, Daphne Koller
2013	J	`jnl`	Utility Elicitation as a Classification Problem CoRR Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar
2008	J	`jnl`	Case Report: Identifying Smokers with a Medical Extraction System. J. Am. Medical Informatics Assoc. Cheryl Clark, Kathleen Good, Lesley Jezierny, Melissa Macpherson, Brian Wilson, Urszula Chajewska
2002	—	`—`	Acting rationally with incomplete utility information. Urszula Chajewska
2001	A	`conf`	Aggregating Learned Probabilistic Beliefs. UAI Pedrito Maynard-Reid II, Urszula Chajewska
2001	A*	`conf`	Learning an Agent's Utility Function by Observing Behavior. ICML Urszula Chajewska, Daphne Koller, Dirk Ormoneit
2000	—	`conf`	Making Rational Decisions Using Adaptive Utility Elicitation. AAAI/IAAI Urszula Chajewska, Daphne Koller, Ronald Parr
2000	A	`conf`	Utilities as Random Variables: Density Estimation and Structure Discovery. UAI Urszula Chajewska, Daphne Koller
1998	A	`conf`	Utility Elicitation as a Classification Problem. UAI Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar
1997	A	`conf`	Defining Explanation in Probabilistic Systems. UAI Urszula Chajewska, Joseph Y. Halpern