SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Alquier, P. (2021). User-friendly introduction to PAC-Bayes bounds. arXiv preprint arXiv:2110.11216.
Amit, R. and Meir, R. (2018). Meta-learning by adjusting priors based on extended PAC-Bayes theory. In International Conference on Machine Learning, pages 205-214. PMLR.
An, J. and Cho, S. (2015). Variational autoencoder based anomaly detection using reconstruction probability. *Special Lecture on IE*, 2(1):1-18.
Anil, C., Lucas, J., and Grosse, R. (2019). Sorting out Lipschitz function approximation. In International Conference on Machine Learning, pages 291-301. PMLR.
Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein generative adversarial networks. In International Conference on Machine Learning, pages 214-223. PMLR.
Rivasplata, O., Kuzborskij, I., Szepesvári, C., and Shawe-Taylor, J. (2020). PAC-Bayes analysis beyond the usual bounds. In Advances in Neural Information Processing Systems, volume 33, pages 16833-16845.
Schreuder, N., Brunel, V.-E., and Dalalyan, A. (2021). Statistical guarantees for generative models without domination. In Algorithmic Learning Theory, pages 1051-1071. PMLR.
Seeger, M. (2002). PAC-Bayesian generalisation error bounds for Gaussian process classification. Journal of Machine Learning Research, 3(Oct):233-269.
Seldin, Y. and Tishby, N. (2010). PAC-Bayesian analysis of co-clustering and beyond. Journal of Machine Learning Research, 11(12).
Singh, S., Uppal, A., Li, B., Li, C.-L., Zaheer, M., and Poczos, B. (2018). Nonparametric density estimation under adversarial losses. In Advances in Neural Information Processing Systems, volume 31.
Thiemann, N., Igel, C., Wintenberger, O., and Seldin, Y. (2017). A strongly quasiconvex PAC-Bayesian bound. In International Conference on Algorithmic Learning Theory, volume 76, pages 466-492. PMLR.
Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B. (2018). Wasserstein auto-encoders. In International Conference on Learning Representations.
Vahdat, A. and Kautz, J. (2020). NVAE: A deep hierarchical variational autoencoder. In Advances in Neural Information Processing Systems, volume 33, pages 19667-19679.
Villani, C. (2009). Optimal transport: old and new, volume 338. Springer.
Zhang, P., Liu, Q., Zhou, D., Xu, T., and He, X. (2018). On the discrimination-generalization tradeoff in GANs. In International Conference on Learning Representations.
Zhao, J., Mathieu, M., and LeCun, Y. (2017). Energy-based generative adversarial networks. In International Conference on Learning Representations.
Zhao, S., Song, J., and Ermon, S. (2019). InfoVAE: Balancing learning and inference in variational autoencoders. In AAAI Conference on Artificial Intelligence, volume 33, pages 5885-5892.