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Concept bottleneck models (CBMs) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions.
Learning cost-sensitive active classifiers
Greiner, R.; Grove, A. J.; and Roth, D. 2002 · 2002
Earlier work this paper cites.
Pruning Improves Heuristic Search for Cost-Sensitive Learning
Zubek, V. B.; and Dietterich, T. G. 2002 · 2002
Earlier work this paper cites.
Prediction-time active feature-value acquisition for cost-effective customer targeting
Kanani, P.; and Melville, P. 2008 · 2008
Earlier work this paper cites.
Visual recognition with humans in the loop
Branson, S.; Wah, C.; Schroff, F.; Babenko, B.; Welinder, P.; Perona, P.; and Belongie, S. 2010 · 2010
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Wah, C.; Branson, S.; Welinder, P.; Perona, P.; and Belongie, S. 2011 · 2011
Earlier work this paper cites.
Eddi: Efficient dynamic discovery of high-value information with partial vae
Ma, C.; Tschiatschek, S.; Palla, K.; Hernández-Lobato, J. M.; Nowozin, S.; and Zhang, C. 2018 · 2018
Cited alongside, same era.
Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding
Shim, H.; Hwang, S. J.; and Yang, E. 2018 · 2018
Cited alongside, same era.
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Irvin, J.; Rajpurkar, P.; Ko, M.; Yu, Y.; Ciurea-Ilcus, S.; Chute, C.; Marklund, H.; Haghgoo, B.; Ball, R.; Shpanskaya, K.; et al. 2019 · 2019
Cited alongside, same era.
Odin: Optimal discovery of high-value information using model-based deep reinforcement learning
Zannone, S.; Hernández-Lobato, J. M.; Zhang, C.; and Palla, K. 2019 · 2019
Cited alongside, same era.
Concept Bottleneck Models
Koh, P. W.; Nguyen, T.; Tang, Y. S.; Mussmann, S.; Pierson, E.; Kim, B.; and Liang, P. 2020 · 2020
Cited alongside, same era.
Rationalization through Concepts
Antognini, D.; and Faltings, B. 2021 · 2021
Later among the works it cites.
Debiasing concept-based explanations with causal analysis
Bahadori, M. T.; and Heckerman, D. E. 2021 · 2021
Later among the works it cites.
Does the whole exceed its parts? the effect of ai explanations on complementary team performance
Bansal, G.; Wu, T.; Zhou, J.; Fok, R.; Nushi, B.; Kamar, E.; Ribeiro, M. T.; and Weld, D. 2021 · 2021
Later among the works it cites.
A Human-AI Collaborative Approach for Clinical Decision Making on Rehabilitation Assessment
Lee, M. H.; Siewiorek, D. P.; Smailagic, A.; Bernardino, A.; and Bermúdez i Badia, S. 2021 · 2021
Later among the works it cites.
Active feature acquisition with generative surrogate models
Li, Y.; and Oliva, J. 2021 · 2021
Later among the works it cites.
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