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The problem of spurious correlations (SCs) arises when a classifier relies on non-predictive features that happen to be correlated with the labels in the training data.
Benchmarking neural network robustness to common corruptions and perturbations, 2019
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Multi-stage prediction networks for data harmonization, 2019
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Imagenet: A large-scale hierarchical image database
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Understanding the failure modes of out-of-distribution generalization, 2020
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Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Unsupervised domain adaptation with residual transfer networks
Long, M., Zhu, H., Wang, J., and Jordan, M. I · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Sun, B. and Saenko, K · 2016
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Functional map of the world, 2017
Christie, G., Fendley, N., Wilson, J., and Mukherjee, R · 2017
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Elements of causal inference: foundations and learning algorithms
Peters, J., Janzing, D., and Schölkopf, B · 2017
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Double/debiased machine learning for treatment and structural parameters, 2018
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., and Robins, J · 2018
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Domain generalization with adversarial feature learning
Li, H., Pan, S. J., Wang, S., and Kot, A. C · 2018
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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Domain generalization via model-agnostic learning of semantic features
Dou, Q., Coelho de Castro, D., Kamnitsas, K., and Glocker, B · 2019
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Metalearners for estimating heterogeneous treatment effects using machine learning
Künzel, S. R., Sekhon, J. S., Bickel, P. J., and Yu, B · 2019
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Episodic training for domain generalization
Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.-Z., and Hospedales, T. M · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Task-robust model-agnostic meta-learning
Collins, L., Mokhtari, A., and Shakkottai, S · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
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Underspecification presents challenges for credibility in modern machine learning
D’Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al · 2020
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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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The origins and prevalence of texture bias in convolutional neural networks
Hermann, K., Chen, T., and Kornblith, S · 2020
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Probabilistic Active Meta-Learning
Kaddour, J., Saemundsson, S., and Deisenroth (he/him), M · 2020
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The risks of invariant risk minimization
Rosenfeld, E., Ravikumar, P., and Risteski, A · 2020
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Noise or signal: The role of image backgrounds in object recognition
Xiao, K., Engstrom, L., Ilyas, A., and Madry, A · 2020
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Adaptive risk minimization: A meta-learning approach for tackling group shift
Zhang, M., Marklund, H., Gupta, A., Levine, S., and Finn, C · 2020
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On feature learning in the presence of spurious correlations
Izmailov, P., Kirichenko, P., Gruver, N., and Wilson, A. G · 2022
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Stop wasting my time! saving days of imagenet and BERT training with latest weight averaging
Kaddour, J · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
Kirichenko, P., Izmailov, P., and Wilson, A. G · 2022
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Dropout disagreement: A recipe for group robustness with fewer annotations
LaBonte, T., Muthukumar, V., and Kumar, A · 2022
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Metashift: A dataset of datasets for evaluating contextual distribution shifts and training conflicts
Liang, W. and Zou, J · 2022
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Swad: Domain generalization by seeking flat minima
Cha, J., Chun, S., Lee, K., Cho, H.-C., Park, S., Lee, Y., and Park, S · 2021
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Improving robustness using generated data
Gowal, S., Rebuffi, S.-A., Wiles, O., Stimberg, F., Calian, D. A., and Mann, T. A · 2021
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2021
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Causal effect inference for structured treatments
Kaddour, J., Zhu, Y., Liu, Q., Kusner, M. J., and Silva, R · 2021
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Does invariant risk minimization capture invariance?
Kamath, P., Tangella, A., Sutherland, D., and Srebro, N · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al · 2021
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Out-of-distribution generalization via risk extrapolation (rex)
Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Le Priol, R., and Courville, A · 2021
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Evaluating the impact of geometric and statistical skews on out-of-distribution generalization performance
Lynch, A., Kaddour, J., and Silva, R · 2022
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Causal transportability for visual recognition
Mao, C., Xia, K., Wang, J., Wang, H., Yang, J., Bareinboim, E., and Vondrick, C · 2022
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You only need a good embeddings extractor to fix spurious correlations
Mehta, R., Albiero, V., Chen, L., Evtimov, I., Glaser, T., Li, Z., and Hassner, T · 2022
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Probabilistic Machine Learning: An introduction
Murphy, K. P · 2022
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Spurious features everywhere – large-scale detection of harmful spurious features in imagenet, 2022
Neuhaus, Y., Augustin, M., Boreiko, V., and Hein, M · 2022
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vit-gpt2-image-captioning (revision 0e334c7), 2022
NLP Connect · 2022
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High-resolution image synthesis with latent diffusion models
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Laion-5b: An open large-scale dataset for training next generation image-text models, 2022
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S., Crowson, K., Schmidt, L., Kaczmarczyk, R., and Jitsev, J · 2022
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Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization
Teney, D., Abbasnejad, E., Lucey, S., and Van den Hengel, A · 2022
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Discovering bugs in vision models using off-the-shelf image generation and captioning
Wiles, O., Albuquerque, I., and Gowal, S · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al · 2022
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Improving Out-of-Distribution Robustness via Selective Augmentation
Yao, H., Wang, Y., Li, S., Zhang, L., Liang, W., Zou, J., and Finn, C · 2022
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Invariant meta learning for out-of-distribution generalization
Jiang, P., Xin, K., Wang, Z., and Li, C · 2023
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Surgical Fine-Tuning Improves Adaptation to Distribution Shifts, March 2023
Lee, Y., Chen, A. S., Tajwar, F., Kumar, A., Yao, H., Liang, P., and Finn, C · 2023
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Dataset interfaces: Diagnosing model failures using controllable counterfactual generation
Vendrow, J., Jain, S., Engstrom, L., and Madry, A · 2023
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Ttida: Controllable generative data augmentation via text-to-text and text-to-image models, 2023
Yin, Y., Kaddour, J., Zhang, X., Nie, Y., Liu, Z., Kong, L., and Liu, Q · 2023
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Nico challenge: Out-of-distribution generalization for image recognition challenges
Zhang, X., He, Y., Wang, T., Qi, J., Yu, H., Wang, Z., Peng, J., Xu, R., Shen, Z., Niu, Y., et al · 2023
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