Wittawat Jitkrittum
Wittawat Jitkrittum
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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
Large sample analysis of the median heuristic
D Garreau, W Jitkrittum, M Kanagawa, 2017
Interpretable distribution features with maximum testing power
W Jitkrittum, Z Szabó, KP Chwialkowski, A Gretton
Advances in Neural Information Processing Systems 29, 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
K2-ABC: Approximate Bayesian computation with kernel embeddings
M Park, W Jitkrittum, D Sejdinovic
AISTATS 51, 398-407, 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
An adaptive test of independence with analytic kernel embeddings
W Jitkrittum, Z Szabó, A Gretton
International Conference on Machine Learning, 1742-1751, 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
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
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
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
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
Informative Features for Model Comparison
W Jitkrittum, H Kanagawa, P Sangkloy, J Hays, B Schölkopf, A Gretton
Advances in Neural Information Processing Systems, 2018
Kernel conditional moment test via maximum moment restriction
K Muandet, W Jitkrittum, J Kübler
Conference on Uncertainty in Artificial Intelligence, 41-50, 2020
Feature Selection via L1-Penalized Squared-Loss Mutual Information
W Jitkrittum, H Hachiya, M Sugiyama
IEICE Transactions on Information and Systems, 1513-1524, 2013
Testing Goodness of Fit of Conditional Density Models with Kernels
W Jitkrittum, H Kanagawa, B Schölkopf
Conference on Uncertainty in Artificial Intelligence (UAI), 2020
Learning kernel tests without data splitting
J Kübler, W Jitkrittum, B Schölkopf, K Muandet
Advances in Neural Information Processing Systems 33, 6245-6255, 2020
A kernel Stein test for comparing latent variable models
H Kanagawa, W Jitkrittum, L Mackey, K Fukumizu, A Gretton
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2023
Worst-case risk quantification under distributional ambiguity using kernel mean embedding in moment problem
JJ Zhu, W Jitkrittum, M Diehl, B Schölkopf
2020 59th IEEE Conference on Decision and Control (CDC), 3457-3463, 2020
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
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