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Deep neural networks often rely on spurious correlations to make predictions, which hinders generalization beyond training environments.
Attribute and simile classifiers for face verification
Kumar, N., Berg, A. C., Belhumeur, P. N., and Nayar, S. K · 2009
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Learning to detect unseen object classes by between-class attribute transfer
Lampert, C. H., Nickisch, H., and Harmeling, S · 2009
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Robust solutions of optimization problems affected by uncertain probabilities
Ben-Tal, A., den Hertog, D., Waegenaere, A. D., Melenberg, B., and Rennen, G · 2013
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Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., , and Vedaldi, A · 2014
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Mathematical Methods of Statistics (PMS-9), Volume 9
Cramér, H · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Stochastic gradient methods for distributionally robust optimization with f-divergences
Namkoong, H. and Duchi, J. C · 2016
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”why should I trust you?”: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Sun, B. and Saenko, K · 2016
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Network dissection: Quantifying interpretability of deep visual representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
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Diving into the shallows: a computational perspective on large-scale shallow learning
Ma, S. and Belkin, M · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
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Towards robust interpretability with self-explaining neural networks
Alvarez-Melis, D. and Jaakkola, T. S · 2018
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Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Fong, R. and Vedaldi, A · 2018
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Does distributionally robust supervised learning give robust classifiers?
Hu, W., Niu, G., Sato, I., and Sugiyama, M · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)
Kim, B., Wattenberg, M., Gilmer, J., Cai, C. J., Wexler, J., Viégas, F. B., and Sayres, R · 2018
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mixup: Beyond empirical risk minimization
Zhang, H., Cissé, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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What is the effect of importance weighting in deep learning?
Byrd, J. and Lipton, Z. C · 2019
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High dimensional linear discriminant analysis: optimality, adaptive algorithm and missing data
Cai, T. T. and Zhang, L · 2019
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This looks like that: Deep learning for interpretable image recognition
Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., and Su, J · 2019
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Clark, C., Yatskar, M., and Zettlemoyer, L · 2019
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Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC)
Codella, N. C. F., Rotemberg, V., Tschandl, P., Celebi, M. E., Dusza, S. W., Gutman, D. A., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M. A., Kittler, H., and Halpern, A · 2019
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
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Montanari, A., Ruan, F., Sohn, Y., and Yan, J · 2019
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Distributionally robust language modeling
Oren, Y., Sagawa, S., Hashimoto, T. B., and Liang, P · 2019
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Explanatory interactive machine learning
Teso, S. and Kersting, K · 2019
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Debiasing skin lesion datasets and models? not so fast
Bissoto, A., Valle, E., and Avila, S · 2020
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Invariance, causality and robustness
Bühlmann, P · 2020
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Learning the difference that makes A difference with counterfactually-augmented data
Kaushik, D., Hovy, E. H., and Lipton, Z. C · 2020
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Concept bottleneck models
Koh, P. W., Nguyen, T., Tang, Y. S., Mussmann, S., Pierson, E., Kim, B., and Liang, P · 2020
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Explaining in style: Training a GAN to explain a classifier in stylespace
Lang, O., Gandelsman, Y., Yarom, M., Wald, Y., Elidan, G., Hassidim, A., Freeman, W. T., Isola, P., Globerson, A., Irani, M., and Mosseri, I · 2021
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Discover the unknown biased attribute of an image classifier
Li, Z. and Xu, C · 2021
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Interpolating classifiers make few mistakes
Liang, T. and Recht, B · 2021
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Uncovering and correcting shortcut learning in machine learning models for skin cancer diagnosis
Nauta, M., Walsh, R., Dubowski, A., and Seifert, C · 2021
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Finding and fixing spurious patterns with explanations
Plumb, G., Ribeiro, M. T., and Talwalkar, A · 2021
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Large-scale methods for distributionally robust optimization
Levy, D., Carmon, Y., Duchi, J. C., and Sidford, A · 2020
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Learning from failure: De-biasing classifier from biased classifier
Nam, J., Cha, H., Ahn, S.-S., Lee, J., and Shin, J · 2020
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2020
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Neural kernels without tangents
Shankar, V., Fang, A., Guo, W., Fridovich-Keil, S., Ragan-Kelley, J., Schmidt, L., and Recht, B · 2020
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No subclass left behind: Fine-grained robustness in coarse-grained classification problems
Sohoni, N. S., Dunnmon, J., Angus, G., Gu, A., and Ré, C · 2020
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Unshuffling data for improved generalization
Teney, D., Abbasnejad, E., and van den Hengel, A · 2020
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Santurkar, S., Tsipras, D., and Madry, A · 2021
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Gradient matching for domain generalization
Shi, Y., Seely, J., Torr, P. H., Siddharth, N., Hannun, A., Usunier, N., and Synnaeve, G · 2021
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Increasing robustness to spurious correlations using forgettable examples
Yaghoobzadeh, Y., Mehri, S., des Combes, R. T., Hazen, T. J., and Sordoni, A · 2021
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Meaningfully debugging model mistakes using conceptual counterfactual explanations
Abid, A., Yüksekgönül, M., and Zou, J · 2022
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Concept-level debugging of part-prototype networks
Bontempelli, A., Teso, S., Giunchiglia, F., and Passerini, A · 2022
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Domino: Discovering systematic errors with cross-modal embeddings
Eyuboglu, S., Varma, M., Saab, K. K., Delbrouck, J., Lee-Messer, C., Dunnmon, J., Zou, J., and Ré, C · 2022
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Identifying spurious correlations and correcting them with an explanation-based learning
Hagos, M. T., Curran, K. M., and Namee, B. M · 2022
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Distilling model failures as directions in latent space
Jain, S., Lawrence, H., Moitra, A., and Madry, A · 2022
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Discover and mitigate unknown biases with debiasing alternate networks
Li, Z., Hoogs, A., and Xu, C · 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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Regmixup: Mixup as a regularizer can surprisingly improve accuracy and out distribution robustness
Pinto, F., Yang, H., Lim, S., Torr, P. H. S., and Dokania, P. K · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Unsupervised learning of debiased representations with pseudo-attributes
Seo, S., Lee, J., and Han, B · 2022
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Salient imagenet: How to discover spurious features in deep learning?
Singla, S. and Feizi, S · 2022
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Core risk minimization using salient imagenet
Singla, S., Moayeri, M., and Feizi, S · 2022
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Discovering invariant rationales for graph neural networks
Wu, Y., Wang, X., Zhang, A., He, X., and Chua, T · 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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Post-hoc concept bottleneck models
Yüksekgönül, M., Wang, M., and Zou, J · 2022
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Understanding multimodal contrastive learning and incorporating unpaired data
Nakada, R., Gulluk, H. I., Deng, Z., Ji, W., Zou, J., and Zhang, L · 2023
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Leveraging explanations in interactive machine learning: An overview
Teso, S., Alkan, Ö., Stammer, W., and Daly, E · 2023
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Freeze then train: Towards provable representation learning under spurious correlations and feature noise
Ye, H., Zou, J., and Zhang, L · 2023
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