← Back to Main Page ... Go to Next Page (More Related work)
References
Ahuja, K., Mahajan, D., Wang, Y., & Bengio, Y.
Interventional causal representation learning
International Conference on Machine Learning, 2023
Ahuja, K., Mansouri, A., & Wang, Y.
Multi-domain causal representation learning via weak distributional invariances
Artificial Intelligence and Statistics, 2024
Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D.
Invariant risk minimization
arXiv preprint arXiv:1907.02893, 2019
Arevalo, C. A., Noorbakhsh, S. L., Dong, Y., Hong, Y., & Wang, B.
Task-agnostic privacy-preserving representation learning for federated learning against attribute inference attacks
AAAI Conference on Artificial Intelligence, 2024
Beckers, S., & Halpern, J. Y.
Abstracting causal models
Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2678-2685, 2019
Bentkus, V.
On Hoeffding's inequalities
The Annals of Probability, 2004
Brehmer, J., De Haan, P., Lippe, P., & Cohen, T. S.
Weakly supervised causal representation learning
Advances in Neural Information Processing Systems, 2022
Cha, J., Chun, S., Lee, K., Cho, H.-C., Park, S., Lee, Y., & Park, S.
SWAD: Domain generalization by seeking flat minima
Advances in Neural Information Processing Systems, vol. 34, pp. 22405-22418, 2021
Chen, Y., Huang, W., Zhou, K., Bian, Y., Han, B., & Cheng, J.
Understanding and improving feature learning for out-of-distribution generalization
Advances in Neural Information Processing Systems, vol. 36, 2024
Gal, Y., & Ghahramani, Z.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
International Conference on Machine Learning, pp. 1050-1059, 2016
Gamella, J. L., & Heinze-Deml, C.
Active invariant causal prediction: Experiment selection through stability
Advances in Neural Information Processing Systems, 2020
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., March, M., & Lempitsky, V.
Domain-adversarial training of neural networks
Journal of Machine Learning Research, vol. 17, no. 59, pp. 1-35, 2016
Geiger, A., Lu, H., Icard, T., & Potts, C.
Causal abstractions of neural networks
Advances in Neural Information Processing Systems, vol. 34, pp. 9574-9586, 2021
Gui, S., Liu, M., Li, X., Luo, Y., & Ji, S.
Joint learning of label and environment causal independence for graph out-of-distribution generalization
Advances in Neural Information Processing Systems, vol. 36, 2024
Heinze-Deml, C., Peters, J., & Meinshausen, N.
Invariant causal prediction for nonlinear models
Journal of Causal Inference, vol. 6, no. 2, pp. 20170016, 2018
Jalaldoust, K., Bellot, A., & Bareinboim, E.
Partial transportability for domain generalization
The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024
Janzing, D., & Schölkopf, B.
Semi-supervised interpolation in an anticausal learning scenario
The Journal of Machine Learning Research, vol. 16, no. 1, pp. 1923-1948, 2015
Kim, B., Kim, H., Kim, K., Kim, S., & Kim, J.
Learning not to learn: Training deep neural networks with biased data
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9012-9020, 2019
Klenke, A.
Probability Theory: A Comprehensive Course (2nd ed.)
Springer, Universitext, 2014
Kocaoglu, M., Jaber, A., Shanmugam, K., & Bareinboim, E.
Characterization and learning of causal graphs with latent variables from soft interventions
Advances in Neural Information Processing Systems, vol. 32, 2019
Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Le Priol, R., & Courville, A.
Out-of-distribution generalization via risk extrapolation (REx)
International Conference on Machine Learning, 2021
Li, H., Pan, S. J., Wang, S., & Kot, A. C.
Domain generalization with adversarial feature learning
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5400-5409, 2018
Liu, C., Sun, X., Wang, J., Tang, H., Li, T., Qin, T., Chen, W., & Liu, T.-Y.
Learning causal semantic representation for out-of-distribution prediction
Advances in Neural Information Processing Systems, vol. 34, pp. 6155-6170, 2021
Lu, C., Wu, Y., Hernández-Lobato, J. M., & Schölkopf, B.
Invariant causal representation learning for out-of-distribution generalization
International Conference on Learning Representations, 2021
Makar, M., & D'Amour, A.
Fairness and robustness in anti-causal prediction
arXiv preprint arXiv:2209.09423, 2022
Mitrovic, J., Sejdinovic, D., & Teh, Y. W.
Causal inference via kernel deviance measures
Advances in Neural Information Processing Systems, vol. 31, 2018
Nam, H., Lee, H., Park, J., Yoon, W., & Yoo, D.
Reducing domain gap by reducing style bias
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8690-8699, 2021
Noorbakhsh, S. L., Zhang, B., Hong, Y., & Wang, B.
Inf2Guard: An information-theoretic framework for learning privacy-preserving representations against inference attacks
USENIX Security Symposium, 2024
Park, J., Buchholz, S., Schölkopf, B., & Muandet, K.
A measure-theoretic axiomatisation of causality
Advances in Neural Information Processing Systems, vol. 36, pp. 28510-28540, 2023
Pearl, J.
Causality
Cambridge University Press, 2009
Peters, J., Bühlmann, P., & Meinshausen, N.
Causal inference by using invariant prediction: Identification and confidence intervals
Journal of the Royal Statistical Society Series B: Statistical Methodology, vol. 78, no. 5, pp. 947-1012, 2016
Ramé, A., Ahuja, K., Zhang, J., Cord, M., Bottou, L., & Lopez-Paz, D.
Model ratatouille: Recycling diverse models for out-of-distribution generalization
International Conference on Machine Learning, pp. 28656-28679, 2023
Scherrer, N., Bilaniuk, O., Annadani, Y., Goyal, A., Schwab, P., Schölkopf, B., Mozer, M. C., Bengio, Y., Bauer, S., & Ke, N. R.
Learning neural causal models with active interventions
arXiv preprint arXiv:2109.02429, 2021
Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., & Mooij, J.
On causal and anticausal learning
arXiv preprint arXiv:1206.6471, 2012
Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y.
Toward causal representation learning
Proceedings of the IEEE, vol. 109, no. 5, pp. 612-634, 2021
Sriperumbudur, B. K., Fukumizu, K., Gretton, A., Schölkopf, B., & Lanckriet, G. R. G.
On integral probability metrics, φ-divergences and binary classification
arXiv preprint arXiv:0901.2698, 2009
Sui, Y., Wu, Q., Wu, J., Cui, Q., Li, L., Zhou, J., Wang, X., & He, X.
Unleashing the power of graph data augmentation on covariate distribution shift
Advances in Neural Information Processing Systems, vol. 36, 2024
Sun, B., & Saenko, K.
Deep CORAL: Correlation alignment for deep domain adaptation
Computer Vision - ECCV 2016 Workshops, 2016
Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.-M., Ciompi, F., & Van Der Laak, J.
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Medical Image Analysis, vol. 58, pp. 101544, 2019
von Kügelgen, J., Besserve, M., Wendong, L., Gresele, L., Kekić, A., Bareinboim, E., Blei, D., & Schölkopf, B.
Nonparametric identifiability of causal representations from unknown interventions
Advances in Neural Information Processing Systems, 2023
Wang, B., Guo, J., Li, A., Chen, Y., & Li, H.
Privacy-preserving representation learning on graphs: A mutual information perspective
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2021
Wang, X., Chen, H., Wu, Z., & Zhu, W.
Disentangled representation learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024
Wang, Z., & Veitch, V.
A unified causal view of domain invariant representation learning
arXiv preprint arXiv:2208.06987, 2022
Wendong, L., Kekić, A., von Kügelgen, J., Buchholz, S., Besserve, M., Gresele, L., & Schölkopf, B.
Causal component analysis
Advances in Neural Information Processing Systems, vol. 36, pp. 32481-32520, 2023
Worrall, D. E., Garbin, S. J., Turmukhambetov, D., & Brostow, G. J.
Harmonic networks: Deep translation and rotation equivariance
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5028-5037, 2017
Yao, W., Chen, G., & Zhang, K.
Learning latent causal dynamics
arXiv preprint arXiv:2202.04828, 2022
Ye, H., Xie, C., Cai, T., Li, R., Li, Z., & Wang, L.
Towards a theoretical framework of out-of-distribution generalization
Advances in Neural Information Processing Systems, vol. 34, pp. 23519-23531, 2021
Zhang, B., Noorbakhsh, S. L., Dong, Y., Hong, Y., & Wang, B.
Learning robust and privacy-preserving representations via information theory
Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 22363-22371, 2025
Zhang, X., Cui, P., Xu, R., Zhou, L., He, Y., & Shen, Z.
Deep stable learning for out-of-distribution generalization
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5372-5382, 2021
Zhou, D., Wang, N., Gao, X., Han, B., Wang, X., Zhan, Y., & Liu, T.
Improving adversarial robustness via mutual information estimation
International Conference on Machine Learning, 2022
Zhou, K., Yang, Y., Hospedales, T., & Xiang, T.
Learning to generate novel domains for domain generalization
ECCV, pp. 561-578, 2020
Zhu, S., Zhang, X., & Evans, D.
Learning adversarially robust representations via worst-case mutual information maximization
International Conference on Machine Learning, 2020