Meronen, L., Trapp, M., Pilzer, A., Yang, L., and Solin, A. Fixing overconfidence in dynamic neural networks. arXiv preprint arXiv:2302.06359, 2023.
Morettin, P., Passerini, A., and Sebastiani, R. Efficient weighted model integration via SMT-based predicate abstraction. In Proceedings of IJCAI, pp. 720-728, 2017.
Morettin, P., Passerini, A., and Sebastiani, R. Advanced smt techniques for weighted model integration. Artificial Intelligence, 275:1-27, 2019.
Naeini, M. P., Cooper, G., and Hauskrecht, M. Obtaining well calibrated probabilities using bayesian binning. In Proceedings of the AAAI conference on artificial intelligence, volume 29, 2015.
Nguyen, A., Yosinski, J., and Clune, J. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 427-436, 2015.
Nguyen, S., Nguyen, D., Nguyen, K., Than, K., Bui, H., and Ho, N. Structured dropout variational inference for bayesian neural networks. Advances in Neural Information Processing Systems, 34, 2021.
Riquelme, C., Tucker, G., and Snoek, J. Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling. arXiv preprint arXiv:1802.09127, 2018.
Ritter, H., Botev, A., and Barber, D. A scalable laplace approximation for neural networks. In 6th International Conference on Learning Representations, ICLR 2018-Conference Track Proceedings, volume 6. International Conference on Representation Learning, 2018.
Salimbeni, H., Cheng, C.-A., Boots, B., and Deisenroth, M. Orthogonally decoupled variational gaussian processes. Advances in neural information processing systems, 31, 2018.
Sang, T., Beame, P., and Kautz, H. A. Performing bayesian inference by weighted model counting. In AAAI, volume 5, pp. 475-481, 2005.
Sanner, S. and Abbasnejad, E. Symbolic variable elimination for discrete and continuous graphical models. In Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012.
Sanner, S., Delgado, K. V., and De Barros, L. N. Symbolic dynamic programming for discrete and continuous state mdps. arXiv preprint arXiv:1202.3762, 2012.
Sivaraman, A., Farnadi, G., Millstein, T., and Van den Broeck, G. Counterexample-guided learning of monotonic neural networks. In Advances in Neural Information Processing Systems 33 (NeurIPS), dec 2020.
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., and Adams, R. Scalable bayesian optimization using deep neural networks. In International conference on machine learning, pp. 2171-2180. PMLR, 2015.
Valiant, L. G. The complexity of enumeration and reliability problems. SIAM Journal on Computing, 8(3):410-421, 1979.
Wasserman, L. Bayesian model selection and model averaging. Journal of mathematical psychology, 44(1):92-107, 2000.
Welling, M. and Teh, Y. W. Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th international conference on machine learning (ICML-11), pp. 681-688, 2011.
Wilson, A. G., Hu, Z., Salakhutdinov, R., and Xing, E. P. Deep kernel learning. In Artificial intelligence and statistics, pp. 370-378. PMLR, 2016.
Wu, A., Nowozin, S., Meeds, E., Turner, R. E., Hernandez-Lobato, J. M., and Gaunt, A. L. Deterministic variational inference for robust bayesian neural networks. In International Conference on Learning Representations, 2019.