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Predictive models often need to work with incomplete information in real-world tasks.
Judgment under uncertainty: Heuristics and biases
AMOS TVERSKY and DANIEL KAHNEMAN · 1974
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Some philosophical problems from the standpoint of artificial intelligence
John McCarthy and Patrick J Hayes · 1981
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A multiplicative formula for aggregating probability assessments
Robert F Bordley · 1982
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Bayesian Data Analysis
Andrew B. Gelman, John B. Carlin, Hal S. Stern, and Donald B. Rubin · 1995
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Bayesian network classifiers
Nir Friedman, Dan Geiger, and Moises Goldszmidt · 1997
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A perspective on judgment and choice: mapping bounded rationality
Daniel Kahneman · 2003
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Integer linear programming inference for conditional random fields
Dan Roth and Wen-tau Yih · 2005
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Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Daphne Koller and Nir Friedman · 2009
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Thinking fast and slow
N. F. McGlynn · 2014
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A multi-axis annotation scheme for event temporal relations
Qiang Ning, Hao Wu, and Dan Roth · 2018
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Uncertain natural language inference
Tongfei Chen, Zhengping Jiang, Adam Poliak, Keisuke Sakaguchi, and Benjamin Van Durme · 2020
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Leveraging unstructured statistical knowledge in a probabilistic language of thought
Alexander K Lew, Michael Henry Tessler, Vikash K Mansinghka, and Joshua B Tenenbaum · 2020
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Break it down: A question understanding benchmark
Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, and Jonathan Berant · 2020
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Temporal common sense acquisition with minimal supervision
Ben Zhou, Qiang Ning, Daniel Khashabi, and Dan Roth · 2020
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COM2SENSE: A commonsense reasoning benchmark with complementary sentences
Shikhar Singh, Nuan Wen, Yu Hou, Pegah Alipoormolabashi, Te-lin Wu, Xuezhe Ma, and Nanyun Peng · 2021
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Temporal reasoning on implicit events from distant supervision
Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, and Dan Roth · 2021
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Entailer: Answering questions with faithful and truthful chains of reasoning
Oyvind Tafjord, Bhavana Dalvi Mishra, and Peter Clark · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou · 2022
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Learning to decompose: Hypothetical question decomposition based on comparable texts
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Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback
Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher Manning · 2023
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
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Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, and Joshua B. Tenenbaum · 2023
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Towards uncertainty-aware language agent, 2024
Jiuzhou Han, Wray Buntine, and Ehsan Shareghi · 2024
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Bairu Hou, Yujian Liu, Kaizhi Qian, Jacob Andreas, Shiyu Chang, and Yang Zhang · 2024
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Generic temporal reasoning with differential analysis and explanation
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Training chain-of-thought via latent-variable inference
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