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Journal Articles
- 2021
[j3]Mijung Park
, Margarita Vinaroz, Wittawat Jitkrittum:
ABCDP: Approximate Bayesian Computation with Differential Privacy. Entropy 23(8): 961 (2021)- 2014
- [j2]Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P. Xing, Masashi Sugiyama:
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso. Neural Comput. 26(1): 185-207 (2014) - 2013
- [j1]Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama:
Feature Selection via l1-Penalized Squared-Loss Mutual Information. IEICE Trans. Inf. Syst. 96-D(7): 1513-1524 (2013)
Conference and Workshop Papers
- 2024
- [c29]Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar:
On Bias-Variance Alignment in Deep Models. ICLR 2024 - [c28]Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Language Model Cascades: Token-Level Uncertainty And Beyond. ICLR 2024 - [c27]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Neha Gupta, Sanjiv Kumar:
Learning to Reject Meets Long-tail Learning. ICLR 2024 - [c26]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar:
Plugin estimators for selective classification with out-of-distribution detection. ICLR 2024 - [c25]Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Wittawat Jitkrittum, Veeranjaneyulu Sadhanala, Sadeep Jayasumana, Aditya Krishna Menon, Rob Fergus, Sanjiv Kumar:
USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval. ICML 2024 - 2023
- [c24]Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar:
When Does Confidence-Based Cascade Deferral Suffice? NeurIPS 2023 - 2022
- [c23]Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
A Witness Two-Sample Test. AISTATS 2022: 1403-1419 - [c22]Patsorn Sangkloy, Wittawat Jitkrittum, Diyi Yang, James Hays:
A Sketch is Worth a Thousand Words: Image Retrieval with Text and Sketch. ECCV (38) 2022: 251-267 - [c21]Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
Post-hoc estimators for learning to defer to an expert. NeurIPS 2022 - 2021
- [c20]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation. AISTATS 2021: 280-288 - [c19]Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces. ICML 2021: 8890-8901 - 2020
- [c18]Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira:
More Powerful Selective Kernel Tests for Feature Selection. AISTATS 2020: 820-830 - [c17]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. CDC 2020: 3457-3463 - [c16]Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
Learning Kernel Tests Without Data Splitting. NeurIPS 2020 - [c15]Krikamol Muandet, Wittawat Jitkrittum, Jonas M. Kübler:
Kernel Conditional Moment Test via Maximum Moment Restriction. UAI 2020: 41-50 - [c14]Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf:
Testing Goodness of Fit of Conditional Density Models with Kernels. UAI 2020: 221-230 - 2019
- [c13]Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf:
Kernel Mean Matching for Content Addressability of GANs. ICML 2019: 3140-3151 - [c12]Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum:
Kernel Stein Tests for Multiple Model Comparison. NeurIPS 2019: 2240-2250 - [c11]Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen:
Fisher Efficient Inference of Intractable Models. NeurIPS 2019: 8790-8800 - 2018
- [c10]Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton:
Informative Features for Model Comparison. NeurIPS 2018: 816-827 - 2017
- [c9]Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton:
An Adaptive Test of Independence with Analytic Kernel Embeddings. ICML 2017: 1742-1751 - [c8]Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton:
A Linear-Time Kernel Goodness-of-Fit Test. NIPS 2017: 262-271 - 2016
- [c7]Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic:
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. AISTATS 2016: 398-407 - [c6]Wittawat Jitkrittum, Zoltán Szabó, Kacper P. Chwialkowski, Arthur Gretton:
Interpretable Distribution Features with Maximum Testing Power. NIPS 2016: 181-189 - 2015
- [c5]Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltán Szabó, Lars Buesing, Maneesh Sahani:
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM). NIPS 2015: 154-162 - [c4]Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó:
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. UAI 2015: 405-414 - 2013
- [c3]Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama:
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. ICML (3) 2013: 10-18 - 2008
- [c2]Choochart Haruechaiyasak, Wittawat Jitkrittum, Chatchawal Sangkeettrakarn, Chaianun Damrongrat:
Implementing News Article Category Browsing Based on Text Categorization Technique. Web Intelligence/IAT Workshops 2008: 143-146 - 2007
- [c1]Choochart Haruechaiyasak, Chatchawal Sangkeettrakarn, Wittawat Jitkrittum:
Managing Offline Educational Web Contents with Search Engine Tools. ICADL 2007: 444-453
Informal and Other Publications
- 2024
- [i34]Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Language Model Cascades: Token-level uncertainty and beyond. CoRR abs/2404.10136 (2024) - [i33]Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Seungyeon Kim, Neha Gupta, Aditya Krishna Menon, Sanjiv Kumar:
Faster Cascades via Speculative Decoding. CoRR abs/2405.19261 (2024) - [i32]Congchao Wang, Sean Augenstein, Keith Rush, Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Aditya Krishna Menon, Alec Go:
Cascade-Aware Training of Language Models. CoRR abs/2406.00060 (2024) - 2023
- [i31]Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Sadeep Jayasumana, Veeranjaneyulu Sadhanala, Wittawat Jitkrittum, Aditya Krishna Menon, Rob Fergus, Sanjiv Kumar:
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval. CoRR abs/2301.12005 (2023) - [i30]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar:
Learning to reject meets OOD detection: Are all abstentions created equal? CoRR abs/2301.12386 (2023) - [i29]Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar:
When Does Confidence-Based Cascade Deferral Suffice? CoRR abs/2307.02764 (2023) - [i28]Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar:
It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models. CoRR abs/2310.09250 (2023) - 2022
- [i27]Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
ELM: Embedding and Logit Margins for Long-Tail Learning. CoRR abs/2204.13208 (2022) - [i26]Antonin Schrab, Wittawat Jitkrittum, Zoltán Szabó, Dino Sejdinovic, Arthur Gretton:
Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'. CoRR abs/2206.11142 (2022) - [i25]Patsorn Sangkloy, Wittawat Jitkrittum, Diyi Yang, James Hays:
A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch. CoRR abs/2208.03354 (2022) - 2021
- [i24]Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
An Optimal Witness Function for Two-Sample Testing. CoRR abs/2102.05573 (2021) - [i23]Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces. CoRR abs/2105.05736 (2021) - [i22]Wittawat Jitkrittum, Michal Lukasik, Ananda Theertha Suresh, Felix X. Yu, Gang Wang:
HD-cos Networks: Efficient Neural Architectures for Secure Multi-Party Computation. CoRR abs/2110.15440 (2021) - 2020
- [i21]Krikamol Muandet, Wittawat Jitkrittum, Jonas M. Kübler:
Kernel Conditional Moment Test via Maximum Moment Restriction. CoRR abs/2002.09225 (2020) - [i20]Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf:
Testing Goodness of Fit of Conditional Density Models with Kernels. CoRR abs/2002.10271 (2020) - [i19]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. CoRR abs/2004.00166 (2020) - [i18]Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
Learning Kernel Tests Without Data Splitting. CoRR abs/2006.02286 (2020) - [i17]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Kernel Distributionally Robust Optimization. CoRR abs/2006.06981 (2020) - 2019
- [i16]Arash Mehrjou, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces. CoRR abs/1901.09206 (2019) - [i15]Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf:
Kernel Mean Matching for Content Addressability of GANs. CoRR abs/1905.05882 (2019) - [i14]Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton:
A Kernel Stein Test for Comparing Latent Variable Models. CoRR abs/1907.00586 (2019) - [i13]Mijung Park, Wittawat Jitkrittum:
ABCDP: Approximate Bayesian Computation Meets Differential Privacy. CoRR abs/1910.05103 (2019) - [i12]Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira:
More Powerful Selective Kernel Tests for Feature Selection. CoRR abs/1910.06134 (2019) - [i11]Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum:
Kernel Stein Tests for Multiple Model Comparison. CoRR abs/1910.12252 (2019) - 2018
- [i10]Song Liu, Wittawat Jitkrittum, Carl Henrik Ek:
Model Inference with Stein Density Ratio Estimation. CoRR abs/1805.07454 (2018) - [i9]Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton:
Informative Features for Model Comparison. CoRR abs/1810.11630 (2018) - 2017
- [i8]Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton:
A Linear-Time Kernel Goodness-of-Fit Test. CoRR abs/1705.07673 (2017) - 2016
- [i7]Wittawat Jitkrittum, Zoltán Szabó, Kacper Chwialkowski, Arthur Gretton:
Interpretable Distribution Features with Maximum Testing Power. CoRR abs/1605.06796 (2016) - [i6]Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton:
An Adaptive Test of Independence with Analytic Kernel Embeddings. CoRR abs/1610.04782 (2016) - 2015
- [i5]Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess:
Passing Expectation Propagation Messages with Kernel Methods. CoRR abs/1501.00375 (2015) - [i4]Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic:
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. CoRR abs/1502.02558 (2015) - [i3]Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó:
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. CoRR abs/1503.02551 (2015) - 2012
- [i2]Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Masashi Sugiyama:
High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso. CoRR abs/1202.0515 (2012) - [i1]Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama:
Feature Selection via L1-Penalized Squared-Loss Mutual Information. CoRR abs/1210.1960 (2012)
Coauthor Index
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