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Urszula Chajewska
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2020 – today
- 2024
- [j2]Harsh Shrivastava, Urszula Chajewska:
Methods for Recovering Conditional Independence Graphs: A Survey. J. Artif. Intell. Res. 80: 593-612 (2024) - [c11]Harsh Shrivastava, Urszula Chajewska:
Methods for recovering conditional independence graphs (Abstract Reprint). IJCAI 2024: 8484 - 2023
- [c10]Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL 2023: 86-99 - [c9]Harsh Shrivastava, Urszula Chajewska:
Neural Graphical Models. ECSQARU 2023: 284-307 - [c8]Harsh Shrivastava, Urszula Chajewska:
Neural Graph Revealers. ML4MHD 2023: 7-25 - [i13]Harsh Shrivastava, Urszula Chajewska:
Neural Graph Revealers. CoRR abs/2302.13582 (2023) - [i12]Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR abs/2304.11749 (2023) - [i11]Urszula Chajewska, Harsh Shrivastava:
Knowledge Propagation over Conditional Independence Graphs. CoRR abs/2308.05857 (2023) - [i10]Urszula Chajewska, Harsh Shrivastava:
Federated Learning with Neural Graphical Models. CoRR abs/2309.11680 (2023) - 2022
- [i9]Leo Betthauser, Urszula Chajewska, Maurice Diesendruck, Rohith Pesala:
Discovering Distribution Shifts using Latent Space Representations. CoRR abs/2202.02339 (2022) - [i8]Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen:
uGLAD: Sparse graph recovery by optimizing deep unrolled networks. CoRR abs/2205.11610 (2022) - [i7]Harsh Shrivastava, Urszula Chajewska:
Neural Graphical Models. CoRR abs/2210.00453 (2022) - [i6]Harsh Shrivastava, Urszula Chajewska:
Methods for Recovering Conditional Independence Graphs: A Survey. CoRR abs/2211.06829 (2022)
2010 – 2019
- 2019
- [c7]Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana:
Axiomatic Interpretability for Multiclass Additive Models. KDD 2019: 226-234 - 2018
- [i5]Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana:
Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models. CoRR abs/1810.09092 (2018) - 2013
- [i4]Pedrito Maynard-Reid II, Urszula Chajewska:
Aggregating Learned Probabilistic Beliefs. CoRR abs/1301.2293 (2013) - [i3]Urszula Chajewska, Daphne Koller:
Utilities as Random Variables: Density Estimation and Structure Discovery. CoRR abs/1301.3840 (2013) - [i2]Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar:
Utility Elicitation as a Classification Problem. CoRR abs/1301.7367 (2013) - [i1]Urszula Chajewska, Joseph Y. Halpern:
Defining Explanation in Probabilistic Systems. CoRR abs/1302.1526 (2013)
2000 – 2009
- 2008
- [j1]Cheryl Clark, Kathleen Good, Lesley Jezierny, Melissa Macpherson, Brian Wilson, Urszula Chajewska:
Case Report: Identifying Smokers with a Medical Extraction System. J. Am. Medical Informatics Assoc. 15(1): 36-39 (2008) - 2002
- [b1]Urszula Chajewska:
Acting rationally with incomplete utility information. Stanford University, USA, 2002 - 2001
- [c6]Urszula Chajewska, Daphne Koller, Dirk Ormoneit:
Learning an Agent's Utility Function by Observing Behavior. ICML 2001: 35-42 - [c5]Pedrito Maynard-Reid II, Urszula Chajewska:
Aggregating Learned Probabilistic Beliefs. UAI 2001: 354-361 - 2000
- [c4]Urszula Chajewska, Daphne Koller, Ronald Parr:
Making Rational Decisions Using Adaptive Utility Elicitation. AAAI/IAAI 2000: 363-369 - [c3]Urszula Chajewska, Daphne Koller:
Utilities as Random Variables: Density Estimation and Structure Discovery. UAI 2000: 63-71
1990 – 1999
- 1998
- [c2]Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar:
Utility Elicitation as a Classification Problem. UAI 1998: 79-88 - 1997
- [c1]Urszula Chajewska, Joseph Y. Halpern:
Defining Explanation in Probabilistic Systems. UAI 1997: 62-71
Coauthor Index
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last updated on 2024-10-21 20:27 CEST by the dblp team
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