Deep learning

Gupta, Prateek, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, and Yoshua Bengio. "Hybrid models for learning to branch" NeurIPS 2020.

Bengio, Yoshua, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller et al. "Predicting Infectiousness for Proactive Contact Tracing." ICLR 2020.

Camuto, A., Willetts, M., Paige, B., Holmes, C., Roberts, S., (2020) Learning Bijective Feature Maps for Linear ICA

Willetts, M., Camuto, A., Rainforth, T., Roberts, S., Holmes, C., (2021) Improving VAEs’ Robustness to Adversarial Attack. International Conference of Learning Representations (ICLR)

Willetts, M., Roberts, S., Holmes, C. (2019) Disentangling to Cluster: Gaussian Mixture Variational Ladder Autoencoders. NeurIPS Bayesian Deep Learning Workshop 2019

J. T. Meech and P. Stanley-Marbell, "Efficient Programmable Random Variate Generation Accelerator from Sensor Noise," in IEEE Embedded Systems Letters, doi: 10.1109/LES.2020.3007005

Bica, I., Jordon, J., & van der Schaar, M. (2020). Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks. arXiv preprint arXiv:2002.12326.

Bica, I., Alaa, A. M., & van der Schaar, M. (2020). Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders. International Conference on Machine Learning (ICML) 2020.

A. Ragano, E. Benetos, and A. Hines, "Audio Impairment Recognition Using a Correlation-Based Feature Representation", 12th International Conference on Quality of Multimedia Experience (QoMEX), May 2020.