| High-dimensional feature selection by feature-wise kernelized lasso M Yamada, W Jitkrittum, L Sigal, EP Xing, M Sugiyama Neural computation 26 (1), 185-207, 2014 | 140 | 2014 |
| Interpretable distribution features with maximum testing power W Jitkrittum, Z Szabó, KP Chwialkowski, A Gretton Advances in Neural Information Processing Systems, 181-189, 2016 | 44 | 2016 |
| A Linear-Time Kernel Goodness-of-Fit Test W Jitkrittum, W Xu, Z Szabo, K Fukumizu, A Gretton Advances in Neural Information Processing Systems, 2017 | 38 | 2017 |
| Squared-loss mutual information regularization: A novel information-theoretic approach to semi-supervised learning G Niu, W Jitkrittum, B Dai, H Hachiya, M Sugiyama International Conference on Machine Learning, 10-18, 2013 | 37 | 2013 |
| K2-ABC: Approximate Bayesian computation with kernel embeddings M Park, W Jitkrittum, D Sejdinovic AISTATS 51, 398-407, 2016 | 32 | 2016 |
| Kernel-based just-in-time learning for passing expectation propagation messages W Jitkrittum, A Gretton, N Heess, SM Eslami, B Lakshminarayanan, ... The Conference on Uncertainty in Artificial Intelligence, 2015 | 26 | 2015 |
| Implementing news article category browsing based on text categorization technique C Haruechaiyasak, W Jitkrittum, C Sangkeettrakarn, C Damrongrat 2008 IEEE/WIC/ACM International Conference on Web Intelligence and …, 2008 | 23 | 2008 |
| Bayesian manifold learning: the locally linear latent variable model (LL-LVM) M Park, W Jitkrittum, A Qamar, Z Szabó, L Buesing, M Sahani Advances in neural information processing systems, 154-162, 2015 | 21 | 2015 |
| An adaptive test of independence with analytic kernel embeddings W Jitkrittum, Z Szabó, A Gretton Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 19 | 2017 |
| Feature Selection via L1-Penalized Squared-Loss Mutual Information W Jitkrittum, H Hachiya, M Sugiyama IEICE Transactions on Information and Systems, 1513-1524, 2013 | 16* | 2013 |
| Large sample analysis of the median heuristic D Garreau, W Jitkrittum, M Kanagawa https://arxiv.org/abs/1707.07269, 2017 | 10 | 2017 |
| Informative Features for Model Comparison W Jitkrittum, H Kanagawa, P Sangkloy, J Hays, B Schölkopf, A Gretton Advances in Neural Information Processing Systems, 2018 | 9 | 2018 |
| Cognitive Bias in Ambiguity Judgements: Using Computational Models to Dissect the Effects of Mild Mood Manipulation in Humans K Iigaya, A Jolivald, W Jitkrittum, I Gilchrist, P Dayan, E Paul, M Mendl Plos One, 2016 | 8 | 2016 |
| QAST: question answering system for Thai Wikipedia W Jitkrittum, C Haruechaiyasak, T Theeramunkong Proceedings of the 2009 Workshop on Knowledge and Reasoning for Answering …, 2009 | 8 | 2009 |
| Just-in-time kernel regression for expectation propagation W Jitkrittum, COMA Gretton, SMA Eslami, COMB Lakshminarayanan, ... Large-Scale Kernel Learning: Challenges and New Opportunities workshop at …, 2015 | 3 | 2015 |
| A Kernel Stein Test for Comparing Latent Variable Models H Kanagawa, W Jitkrittum, L Mackey, K Fukumizu, A Gretton arXiv preprint arXiv:1907.00586, 2019 | 2 | 2019 |
| K2-ABC: Approximate Bayesian Computation with infinite dimensional summary statistics via kernel embeddings M Park, UCL AC, W Jitkrittum, D Sejdinovic stat 1050, 9, 2015 | 2 | 2015 |
| Kernel Stein Tests for Multiple Model Comparison J Lim, M Yamada, B Schölkopf, W Jitkrittum Neural Information Processing Systems Year (2019), 2019 | 1 | 2019 |
| Kernel-Guided Training of Implicit Generative Models with Stability Guarantees A Mehrjou, W Jitkrittum, K Muandet, B Schölkopf arXiv preprint arXiv:1910.14428, 2019 | 1 | 2019 |
| Fisher efficient inference of intractable models S Liu, T Kanamori, W Jitkrittum, Y Chen Advances in Neural Information Processing Systems, 8790-8800, 2019 | 1 | 2019 |
arthur grettonGatsby Computational Neuroscience Unit, UCLVerified email at gatsby.ucl.ac.uk
