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We introduce a fine-grained framework for uncertainty quantification of predictive models under distributional shifts.
Distributionally robust optimization: A review
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A distributional approach for causal inference using propensity scores
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Scikit-learn: Machine learning in python
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Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L. (2018) · 2018
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Bounds on the conditional and average treatment effect with unobserved confounding factors
Yadlowsky, S., Namkoong, H., Basu, S., Duchi, J., and Tian, L. (2018) · 2018
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Quantifying distributional model risk via optimal transport
Blanchet, J. and Murthy, K. (2019) · 2019
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Assessing treatment effect variation in observational studies: Results from a data challenge
Carvalho, C., Feller, A., Murray, J., Woody, S., and Yeager, D. (2019) · 2019
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Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V. (2019) · 2019
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Conformalized quantile regression
Romano, Y., Patterson, E., and Candes, E. (2019) · 2019
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Conformal prediction under covariate shift
Tibshirani, R. J., Foygel Barber, R., Candes, E., and Ramdas, A. (2019) · 2019
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A national experiment reveals where a growth mindset improves achievement
Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., Tipton, E., Schneider, B., Hulleman, C. S., Hinojosa, C. P., et al. (2019) · 2019
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qosa-indices
Elie-Dit-Cosaque, K. (2020) · 2020
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Confounding-robust policy evaluation in infinite-horizon reinforcement learning
Kallus, N. and Zhou, A. (2020) · 2020
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The effect of natural distribution shift on question answering models
Miller, J., Krauth, K., Recht, B., and Schmidt, L. (2020) · 2020
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Nested conformal prediction and quantile out-of-bag ensemble methods
Gupta, C., Kuchibhotla, A. K., and Ramdas, A. (2022) · 2022
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Sensitivity analysis under the f f -sensitivity models: a distributional robustness perspective
Jin, Y., Ren, Z., and Zhou, Z. (2022) · 2022
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Factored DRO: Factored distributionally robust policies for contextual bandits
Mu, T., Chandak, Y., Hashimoto, T. B., and Brunskill, E. (2022) · 2022
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PAC prediction sets under covariate shift
Park, S., Dobriban, E., Lee, I., and Bastani, O. (2022) · 2022
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Distribution-free prediction sets adaptive to unknown covariate shift
Qiu, H., Dobriban, E., and Tchetgen, E. T. (2022) · 2022
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Fighting covid-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention
Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., and Rand, D. G. (2020) · 2020
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Classification with valid and adaptive coverage
Romano, Y., Sesia, M., and Candès, E. J. (2020) · 2020
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How accurate are accuracy-nudge interventions? a preregistered direct replication of pennycook et al.(2020)
Roozenbeek, J., Freeman, A. L., and van der Linden, S. (2021) · 2020
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Using publicly available satellite imagery and deep learning to understand economic well-being in africa
Yeh, C., Perez, A., Driscoll, A., Azzari, G., Tang, Z., Lobell, D., Ermon, S., and Burke, M. (2020) · 2020
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The limits of distribution-free conditional predictive inference
Barber, R. F., Candes, E. J., Ramdas, A., and Tibshirani, R. J. (2021) · 2021
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Distributional conformal prediction
Chernozhukov, V., Wüthrich, K., and Zhu, Y. (2021) · 2021
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Sahoo, R., Lei, L., and Wager, S. (2022) · 2022
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Doubly robust calibration of prediction sets under covariate shift
Yang, Y., Kuchibhotla, A. K., and Tchetgen, E. T. (2022) · 2022
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Conformal sensitivity analysis for individual treatment effects
Yin, M., Shi, C., Wang, Y., and Blei, D. M. (2022) · 2022
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Conformal prediction: A gentle introduction
Angelopoulos, A. N., Bates, S., et al. (2023) · 2023
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Conformal prediction beyond exchangeability
Barber, R. F., Candes, E. J., Ramdas, A., and Tibshirani, R. J. (2023) · 2023
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Robust validation: Confident predictions even when distributions shift
Cauchois, M., Gupta, S., Ali, A., and Duchi, J. C. (2023) · 2023
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Distributionally robust losses for latent covariate mixtures
Duchi, J., Hashimoto, T., and Namkoong, H. (2023) · 2023
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Probabilistically robust conformal prediction
Ghosh, S., Shi, Y., Belkhouja, T., Yan, Y., Doppa, J., and Jones, B. (2023) · 2023
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Localized conformal prediction: A generalized inference framework for conformal prediction
Guan, L. (2023) · 2023
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Conformalized survival analysis with adaptive cutoffs
Gui, Y., Hore, R., Ren, Z., and Barber, R. F. (2023) · 2023
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Policy learning under biased sample selection
Lei, L., Sahoo, R., and Wager, S. (2023) · 2023
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On the need for a language describing distribution shifts: Illustrations on tabular datasets
Liu, J., Wang, T., Cui, P., and Namkoong, H. (2023) · 2023
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Diagnosing model performance under distribution shift
Namkoong, H., Yadlowsky, S., et al. (2023) · 2023
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Distributionally robust batch contextual bandits
Si, N., Zhang, F., Zhou, Z., and Blanchet, J. (2023) · 2023
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Optimal multi-distribution learning
Zhang, Z., Zhan, W., Chen, Y., Du, S. S., and Lee, J. D. (2023) · 2023
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