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Machine learning models frequently experience performance drops under distribution shifts.
A value for n-person games
Shapley, L. S. et al · 1953
Earlier work this paper cites.
Divergence estimation of continuous distributions based on data-dependent partitions
Wang, Q., Kulkarni, S. R., and Verdú, S · 2005
Earlier work this paper cites.
A nearest-neighbor approach to estimating divergence between continuous random vectors
Wang, Q., Kulkarni, S. R., and Verdú, S · 2006
Earlier work this paper cites.
Direct importance estimation for covariate shift adaptation
Sugiyama, M., Suzuki, T., Nakajima, S., Kashima, H., Von Bünau, P., and Kawanabe, M · 2008
Earlier work this paper cites.
Polynomial calculation of the shapley value based on sampling
Castro, J., Gómez, D., and Tejada, J · 2009
Earlier work this paper cites.
Causality
Pearl, J · 2009
Earlier work this paper cites.
Divergence estimation for multidimensional densities via k k -nearest-neighbor distances
Wang, Q., Kulkarni, S. R., and Verdú, S · 2009
Earlier work this paper cites.
Transportability of causal and statistical relations: A formal approach
Pearl, J. and Bareinboim, E · 2011
Earlier work this paper cites.
Kernel-based conditional independence test and application in causal discovery
Zhang, K., Peters, J., Janzing, D., and Schölkopf, B · 2011
Earlier work this paper cites.
Density-ratio matching under the bregman divergence: a unified framework of density-ratio estimation
Sugiyama, M., Suzuki, T., and Kanamori, T · 2012
Earlier work this paper cites.
Domain adaptation under target and conditional shift
Zhang, K., Schölkopf, B., Muandet, K., and Wang, Z · 2013
Earlier work this paper cites.
Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E. and Kononenko, I · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Earlier work this paper cites.
Elements of causal inference: foundations and learning algorithms
Peters, J., Janzing, D., and Schölkopf, B · 2017
Earlier work this paper cites.
Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2017
Earlier work this paper cites.
From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
Bandi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Bejnordi, B. E., Lee, B., Paeng, K., Zhong, A., et al · 2018
Earlier work this paper cites.
Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate mri
Chirra, P., Leo, P., Yim, M., Bloch, B. N., Rastinehad, A. R., Purysko, A., Rosen, M., Madabhushi, A., and Viswanath, S · 2018
Earlier work this paper cites.
Generalizability of predictive models for intensive care unit patients, 2018
Johnson, A. E. W., Pollard, T. J., and Naumann, T · 2018
Cited alongside, same era.
CausalGAN: Learning causal implicit generative models with adversarial training
Kocaoglu, M., Snyder, C., Dimakis, A. G., and Vishwanath, S · 2018
Cited alongside, same era.
Bagan: Data augmentation with balancing gan
Mariani, G., Scheidegger, F., Istrate, R., Bekas, C., and Malossi, C · 2018
Cited alongside, same era.
The eicu collaborative research database, a freely available multi-center database for critical care research
Pollard, T. J., Johnson, A. E., Raffa, J. D., Celi, L. A., Mark, R. G., and Badawi, O · 2018
Cited alongside, same era.
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
Evaluating model robustness and stability to dataset shift
Subbaswamy, A., Adams, R., and Saria, S · 2021
Later among the works it cites.
Shapley flow: A graph-based approach to interpreting model predictions
Wang, J., Wiens, J., and Lundberg, S · 2021
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Wu, E., Wu, K., and Zou, J · 2021
Later among the works it cites.
An empirical framework for domain generalization in clinical settings
Zhang, H., Dullerud, N., Seyyed-Kalantari, L., Morris, Q., Joshi, S., and Ghassemi, M · 2021
Later among the works it cites.
The lifecycle of a statistical model: Model failure detection, identification, and refitting, 2022
Ali, A., Cauchois, M., and Duchi, J. C · 2022
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Review of causal discovery methods based on graphical models
Glymour, C., Zhang, K., and Spirtes, P · 2019
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Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks
Nestor, B., McDermott, M. B., Boag, W., Berner, G., Naumann, T., Hughes, M. C., Goldenberg, A., and Ghassemi, M · 2019
Cited alongside, same era.
Failing loudly: An empirical study of methods for detecting dataset shift
Rabanser, S., Günnemann, S., and Lipton, Z · 2019
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Preventing failures due to dataset shift: Learning predictive models that transport
Subbaswamy, A., Schulam, P., and Saria, S · 2019
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Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2020
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Feature relevance quantification in explainable ai: A causal problem
Janzing, D., Minorics, L., and Blöbaum, P · 2020
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Feature shift detection: Localizing which features have shifted via conditional distribution tests
Kulinski, S., Bagchi, S., and Inouye, D. I · 2020
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The spotlight: A general method for discovering systematic errors in deep learning models
d’Eon, G., d’Eon, J., Wright, J. R., and Leyton-Brown, K · 2022
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Domino: Discovering systematic errors with cross-modal embeddings
Eyuboglu, S., Varma, M., Saab, K. K., Delbrouck, J.-B., Lee-Messer, C., Dunnmon, J., Zou, J., and Re, C · 2022
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Out-of-distribution robustness via targeted augmentations
Gao, I., Sagawa, S., Koh, P. W., Hashimoto, T., and Liang, P · 2022
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Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine
Guo, L. L., Pfohl, S. R., Fries, J., Johnson, A. E., Posada, J., Aftandilian, C., Shah, N., and Sung, L · 2022
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On measuring causal contributions via do-interventions
Jung, Y., Kasiviswanathan, S., Tian, J., Janzing, D., Blöbaum, P., and Bareinboim, E · 2022
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Towards explaining distribution shifts
Kulinski, S. and Inouye, D. I · 2022
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Tracking the risk of a deployed model and detecting harmful distribution shifts
Podkopaev, A. and Ramdas, A · 2022
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Generalizability challenges of mortality risk prediction models: A retrospective analysis on a multi-center database
Singh, H., Mhasawade, V., and Chunara, R · 2022
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Evaluating robustness to dataset shift via parametric robustness sets
Thams, N., Oberst, M., and Sontag, D · 2022
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A fine-grained analysis on distribution shift
Wiles, O., Gowal, S., Stimberg, F., Rebuffi, S.-A., Ktena, I., Dvijotham, K. D., and Cemgil, A. T · 2022
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Diagnosing model performance under distribution shift, 2023
Cai, T. T., Namkoong, H., and Yadlowsky, S · 2023
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A learning based hypothesis test for harmful covariate shift
Ginsberg, T., Liang, Z., and Krishnan, R. G · 2023
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Distilling model failures as directions in latent space
Jain, S., Lawrence, H., Moitra, A., and Madry, A · 2023
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Trak: Attributing model behavior at scale
Park, S. M., Georgiev, K., Ilyas, A., Leclerc, G., and Madry, A · 2023
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Change is hard: A closer look at subpopulation shift
Yang, Y., Zhang, H., Katabi, D., and Ghassemi, M · 2023
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