Bibliography

Reinforcement learning

• 1. M. Riedmiller. Neural fitted Q iteration–First Experiences with a Data Efficient Neural Reinforcement Learning Method. Proceedings of the European Conference on Machine Learning, 2005.

  2. M. P. Deisenroth, D. Fox, C. E. Rasmussen, Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 37, pp. 408–423, 2015

  3. M. P. Deisenroth and C. E. Rasmussen. PILCO: A Model-based and Data-efficient Approach to Policy Search. Proceedings of the International Conference on Machine Learning, 2011.

  4. T. Jung and P. Stone. Gaussian Processes for Sample Efficient Reinforcement Learning with RMAX-like Exploration. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2010.

  5. J. A. M. Assael, N. Wahlström, T. B. Schön, M. P. Deisenroth, Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models, arXiv pre-print, arxiv: 1510.02173, 2015

Deep learning

• 1. O. Ronneberger, F. Philipp, T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation, Medical Image Computing and Computer-Assisted Intervention–MICCAI, pp. 234–241, 2015.

  2. L. v. d.Maaten, M. Chen, S. Tyree, and K. Q. Weinberger. Learning with Marginalized Corrupted Features. In Proceedings of the International Conference on Machine Learning, 2013.

  3. A. Rasmus, T. Raiko, H. Valpola. Denoising Autoencoder with Modulated Lateral Connections Learns Invariant Representations of Natural Images, arXiv preprint arXiv:1412.7210, 2014

  4. J. Jacobsen, J. C. van Gemert, Z. Lou, A. Smeulders. Structured Receptive Fields in CNNs, Computer Vision and Pattern Recognition (CVPR), 2016.

  5. R. Gens, P. Domingos. Deep Symmetry Networks, Advances in Neural Information Processing Systems. 2014.

  6. T. Cohen, M. Welling. Group Equivariant Convolutional Neural Networks. In Proceedings of the International Conference on Machine Learning (ICML), 2016.

  7. S. Dieleman, J. De Fauw, K. Kavukcuoglu. Exploiting Cyclic Symmetry in Convolutional Neural Networks. In Proceedings of the International Conference on Machine Learning (ICML), 2016.

Semi-supervised learning

1. D. P. Kingma, S. Mohamed, D. Jimenez Rezende, M. Welling, Semi-supervised learning with deep generative models, Advances in Neural Information Processing Systems, 2014

   1. A. Rasmus, H. Valpola, M. Honkala, M.Berglund, T. Raiko. Semi-Supervised Learning with Ladder Networks. Advances in Neural Information Processing Systems. 2015.

   2. A. Rasmus, M. Berglund, M. Honkala, H. Valpola, T. Raiko. Semi-Supervised Learning with Ladder Networks. In Advances in Neural Information Processing Systems (pp. 3532-3540). 2015

   3. R. Fergus, Y. Weiss, A. Torralba. Semi-Supervised Learning in Gigantic Image Collections. Advances in Neural Information Processing Systems. 2009.

Bayesian analysis, non-parametrics, one-shot learning

• 1. N. D. Lawrence, Probabilistic non-linear principal component analysis with Gaussian process latent variable models, Journal of Machine Learning Research, vol. 6, pp 1783–1816, 2005

  2. B. M. Lake, R. Salakhutdinov, J. B. Tenenbaum. Human-Level Concept Learning Through Probabilistic Program Induction, Science, vol. 350, pp. 1332–1338, 2015

  3. A. C. Damianou, N. D. Lawrence. Deep Gaussian Processes, Proceedings of the International Conference on Artificial Intelligence and Statistics, 2013

  4. R. Salakhutdinov, J. B. Tenenbaum, and A. Torralba. Learning with Hierarchical-deep Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35(8), pp. 1958-1971, 2013

  5. F.-F. Li, and R. Fergus, and P. Perona. A Bayesian approach to unsupervised one-shot learning of object categories. Proceedings of the International Conference on Computer Vision, 2003

  6. M. Kopicki, R. Detry, M. Adjigble, R. Stolkin, A. Leonardis and J. L. Wyatt. One Shot Learning and Generation of Dexterous Grasps for Novel Objects. The International Journal of Robotics Research. 2015.

Active learning, bandits, Bayesian optimization

• 1. A. Krause, A. Singh, C. Guestrin, Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies, Journal of Machine Learning Research, vol. 9, pp. 235–284, 2009

  2. E. Brochu, V. M. Cora, N. de Freitas, A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning, arXiv pre-print arXiv:1012:2599, 2010.

  3. D. Ulmasov, C. Baroukh, B. Chachuat, M. P. Deisenroth, R. Misener, Bayesian Optimization with Dimension Scheduling: Application to Biological Systems, Proceedings of the European Symposium on Computer Aided Process Engineering, 2016

  4. A. Cully, J. Clune, D. Tarapore, J. B. Mouret, Robots that Can Adapt Like Animals, Nature, vol. 521 (7553), pp. 503–507, 2015