Wittawat Jitkrittum
Wittawat Jitkrittum
Max Planck Institute for Intelligent Systems
Verifierad e-postadress på tuebingen.mpg.de - Startsida
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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
Interpretable distribution features with maximum testing power
W Jitkrittum, Z Szabó, KP Chwialkowski, A Gretton
Advances in Neural Information Processing Systems, 181-189, 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
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
K2-ABC: Approximate Bayesian computation with kernel embeddings
M Park, W Jitkrittum, D Sejdinovic
AISTATS 51, 398-407, 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
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
An adaptive test of independence with analytic kernel embeddings
W Jitkrittum, Z Szabó, A Gretton
JMLR 70, 1742-1751, 2017
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
Feature Selection via L1-Penalized Squared-Loss Mutual Information
W Jitkrittum, H Hachiya, M Sugiyama
IEICE Transactions on Information and Systems, 1513-1524, 2013
Informative Features for Model Comparison
W Jitkrittum, H Kanagawa, P Sangkloy, J Hays, B Schölkopf, A Gretton
Advances in Neural Information Processing Systems, 2018
Large sample analysis of the median heuristic
D Garreau, W Jitkrittum, M Kanagawa
https://arxiv.org/abs/1707.07269, 2017
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
Qast: Question answering system for thaiwikipedia
W Jitkrittum, C Haruechaiyasak, T Theeramunkong
Proceedings of the 2009 Workshop on Knowledge and Reasoning for Answering …, 2009
Kernel Stein Tests for Multiple Model Comparison
J Lim, M Yamada, B Schölkopf, W Jitkrittum
Neural Information Processing Systems Year (2019), 2019
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
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
Kernel-guided training of implicit generative models with stability guarantees
A Mehrjou, W Jitkrittum, K Muandet, B Schölkopf
arXiv preprint arXiv:1910.14428, 2019
More Powerful Selective Kernel Tests for Feature Selection
JN Lim, M Yamada, W Jitkrittum, Y Terada, S Matsui, H Shimodaira
https://arxiv.org/abs/1910.06134, 2019
Fisher efficient inference of intractable models
S Liu, T Kanamori, W Jitkrittum, Y Chen
Advances in Neural Information Processing Systems, 8793-8803, 2019
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Artiklar 1–20