| 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 | 386 | 2014 |
| Large sample analysis of the median heuristic D Garreau, W Jitkrittum, M Kanagawa https://arxiv.org/abs/1707.07269, 2017 | 149 | 2017 |
| Interpretable distribution features with maximum testing power W Jitkrittum, Z Szabó, KP Chwialkowski, A Gretton Advances in Neural Information Processing Systems 29, 2016 | 138 | 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 | 122 | 2017 |
| K2-ABC: Approximate Bayesian computation with kernel embeddings M Park, W Jitkrittum, D Sejdinovic AISTATS 51, 398-407, 2016 | 112 | 2016 |
| 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 | 70 | 2013 |
| Kernel distributionally robust optimization: Generalized duality theorem and stochastic approximation JJ Zhu, W Jitkrittum, M Diehl, B Schölkopf International Conference on Artificial Intelligence and Statistics, 280-288, 2021 | 53* | 2021 |
| An adaptive test of independence with analytic kernel embeddings W Jitkrittum, Z Szabó, A Gretton International Conference on Machine Learning, 1742-1751, 2017 | 53 | 2017 |
| 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 | 43 | 2015 |
| 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 | 40 | 2016 |
| A sketch is worth a thousand words: Image retrieval with text and sketch P Sangkloy, W Jitkrittum, D Yang, J Hays European Conference on Computer Vision, 251-267, 2022 | 32 | 2022 |
| Informative Features for Model Comparison W Jitkrittum, H Kanagawa, P Sangkloy, J Hays, B Schölkopf, A Gretton Advances in Neural Information Processing Systems, 2018 | 30 | 2018 |
| 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 28, 2015 | 30 | 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 | 30 | 2008 |
| Testing Goodness of Fit of Conditional Density Models with Kernels W Jitkrittum, H Kanagawa, B Schölkopf Conference on Uncertainty in Artificial Intelligence (UAI), 2020 | 28 | 2020 |
| Kernel conditional moment test via maximum moment restriction K Muandet, W Jitkrittum, J Kübler Conference on Uncertainty in Artificial Intelligence, 41-50, 2020 | 27 | 2020 |
| A witness two-sample test JM Kübler, W Jitkrittum, B Schölkopf, K Muandet International Conference on Artificial Intelligence and Statistics, 1403-1419, 2022 | 25 | 2022 |
| Post-hoc Estimators for Learning to Defer to an Expert H Narasimhan, W Jitkrittum, A Menon, A Rawat, S Kumar Advances in Neural Information Processing Systems, 2022 | 23 | 2022 |
| Feature Selection via L1-Penalized Squared-Loss Mutual Information W Jitkrittum, H Hachiya, M Sugiyama IEICE Transactions on Information and Systems, 1513-1524, 2013 | 23* | 2013 |
| Learning kernel tests without data splitting J Kübler, W Jitkrittum, B Schölkopf, K Muandet Advances in Neural Information Processing Systems 33, 6245-6255, 2020 | 21 | 2020 |
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk