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Results: 10
Number of items: 10
  • Open Access
    Romijnders, R., Louizos, C., Asano, Y. M., & Welling, M. (2024). Protect Your Score: Contact-tracing With Differential Privacy Guarantees. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), Proceedings of the 38th AAAI Conference on Artificial Intelligence: AAAI-2024 (Vol. 13, pp. 14829-14837). AAAI Press. https://doi.org/10.1609/aaai.v38i13.29402
  • Open Access
    Romijnders, R., Asano, Y. M., Louizos, C., & Welling, M. (2023). No time to waste: practical statistical contact tracing with few low-bit messages. Proceedings of Machine Learning Research, 206, 7943-7960. https://proceedings.mlr.press/v206/romijnders23a.html
  • Open Access
    Louizos, C. (2022). Probabilistic reasoning for uncertainty & compression in deep learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Helwegen, R., Louizos, C., & Forré, P. (2020). Improving Fair Predictions Using Variational Inference In Causal Models. (v1 ed.) ArXiv. https://arxiv.org/abs/2008.10880
  • Open Access
    Louizos, C., Reisser, M., Blankevoort, T., Gavves, E., & Welling, M. (2019). Relaxed Quantization for Discretized Neural Networks. In ICLR 2019: International Conference on Learning Representations : New Orleans, Louisiana, United States, May 6-May 9, 2019 OpenReview. https://openreview.net/forum?id=HkxjYoCqKX
  • Open Access
    Louizos, C., Shalit, U., Mooij, J., Sontag, D., Zemel, R., & Welling, M. (2018). Causal Effect Inference with Deep Latent-Variable Models. In U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), 31st Conference on Advances in Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017 (Vol. 10, pp. 6447-6457). (Advances in Neural Information Processing Systems; Vol. 30). Neural Information Processing Systems. https://papers.nips.cc/paper/2017/file/94b5bde6de888ddf9cde6748ad2523d1-Paper.pdf
  • Open Access
    Louizos, C., Ullrich, K., & Welling, M. (2018). Bayesian Compression for Deep Learning. In U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), 31st Conference on Advances in Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017 (Vol. 5, pp. 3289-3299). (Advances in Neural Information Processing Systems; Vol. 30). Neural Information Processing Systems. https://papers.nips.cc/paper/6921-bayesian-compression-for-deep-learning
  • Open Access
    Louizos, C., & Welling, M. (2017). Multiplicative Normalizing Flows for Variational Bayesian Neural Networks. Proceedings of Machine Learning Research, 70, 2218-2227. http://proceedings.mlr.press/v70/louizos17a.html
  • Open Access
    Louizos, C., & Welling, M. (2016). Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. JMLR Workshop and Conference Proceedings, 48, 1708-1716. http://proceedings.mlr.press/v48/louizos16.html
  • Open Access
    Louizos, C., Swersky, K., Li, Y., Welling, M., & Zemel, R. (2016). The Variational Fair Autoencoder. In ICLR 2016: International Conference on Learning Representations: May 2-4, 2016, San Juan, Puerto Rico. Accepted papers (Conference Track) Computational and Biological Learning Society. https://arxiv.org/abs/1511.00830
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