| Beam sampling for the infinite hidden Markov model J Van Gael, Y Saatci, YW Teh, Z Ghahramani Proceedings of the 25th international conference on Machine learning, 1088-1095, 2008 | 343 | 2008 |
| Scalable inference for structured Gaussian process models Y Saatçi University of Cambridge, 2012 | 221 | 2012 |
| Gaussian process change point models Y Saatçi, RD Turner, CE Rasmussen Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010 | 214 | 2010 |
| Cascaded classification of gender and facial expression using active appearance models Y Saatci, C Town 7th International Conference on Automatic Face and Gesture Recognition …, 2006 | 186 | 2006 |
| Bayesian gan Y Saatci, AG Wilson Advances in neural information processing systems 30, 2017 | 171 | 2017 |
| Learning scalable deep kernels with recurrent structure M Al-Shedivat, AG Wilson, Y Saatchi, Z Hu, EP Xing Journal of Machine Learning Research 18 (82), 1-37, 2017 | 122 | 2017 |
| Metropolis-hastings generative adversarial networks R Turner, J Hung, E Frank, Y Saatchi, J Yosinski International Conference on Machine Learning, 6345-6353, 2019 | 103 | 2019 |
| Scaling multidimensional inference for structured Gaussian processes E Gilboa, Y Saatçi, JP Cunningham IEEE transactions on pattern analysis and machine intelligence 37 (2), 424-436, 2013 | 77 | 2013 |
| Adaptive sequential Bayesian change point detection R Turner, Y Saatci, CE Rasmussen Temporal Segmentation Workshop at NIPS, 1-4, 2009 | 60 | 2009 |
| Scaling multidimensional Gaussian processes using projected additive approximations E Gilboa, Y Saatçi, J Cunningham International Conference on Machine Learning, 454-461, 2013 | 31 | 2013 |
| Data, modelling and inference in road traffic networks RJ Gibbens, Y Saatci Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2008 | 14 | 2008 |
| Submission for the MLSP 2008 Data Analysis Competition Y Saatçi, R Turner | | 2008 |
| Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning. F Doshi-Velez, D Pfau, F Wood, N Roy, MP Deisenroth, D Fox, ... | | |
| Scalable GP-LSTMs with Semi-Stochastic Gradients M Al-Shedivat, AG Wilson, Y Saatchi, Z Hu, EP Xing | | |
