Managing Offline Educational Web Contents with Search Engine Tools. - Choochart Haruechaiyasak; Chatchawal Sangkeettrakarn; Wittawat Jitkrittum (ICADL, 2007)

Implementing News Article Category Browsing Based on Text Categorization Technique. - Choochart Haruechaiyasak; Wittawat Jitkrittum; Chatchawal Sangkeettrakarn; Chaianun Damrongrat (Web Intelligence/IAT Workshops, 2008)

High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso - Makoto Yamada; Wittawat Jitkrittum; Leonid Sigal; Masashi Sugiyama (CoRR, 2012)

Feature Selection via L1-Penalized Squared-Loss Mutual Information - Wittawat Jitkrittum; Hirotaka Hachiya; Masashi Sugiyama (CoRR, 2012)

Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. - Gang Niu 001; Wittawat Jitkrittum; Bo Dai 001; Hirotaka Hachiya; Masashi Sugiyama (ICML (3), 2013)

Feature Selection via l1-Penalized Squared-Loss Mutual Information. - Wittawat Jitkrittum; Hirotaka Hachiya; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2013)

High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso. - Makoto Yamada; Wittawat Jitkrittum; Leonid Sigal; Eric P. Xing; Masashi Sugiyama (Neural Comput., 2014)

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM). - Mijung Park; Wittawat Jitkrittum; Ahmad Qamar; Zoltán Szabó 001; Lars Buesing; Maneesh Sahani (NIPS, 2015)

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. - Wittawat Jitkrittum; Arthur Gretton; Nicolas Heess; S. M. Ali Eslami; Balaji Lakshminarayanan; Dino Sejdinovic; Zoltán Szabó 001 (UAI, 2015)

Passing Expectation Propagation Messages with Kernel Methods. - Wittawat Jitkrittum; Arthur Gretton; Nicolas Heess (CoRR, 2015)

K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. - Mijung Park; Wittawat Jitkrittum; Dino Sejdinovic (AISTATS, 2016)

Interpretable Distribution Features with Maximum Testing Power. - Wittawat Jitkrittum; Zoltán Szabó 001; Kacper P. Chwialkowski; Arthur Gretton (NIPS, 2016)

An Adaptive Test of Independence with Analytic Kernel Embeddings. - Wittawat Jitkrittum; Zoltán Szabó 001; Arthur Gretton (ICML, 2017)

A Linear-Time Kernel Goodness-of-Fit Test. - Wittawat Jitkrittum; Wenkai Xu; Zoltán Szabó 001; Kenji Fukumizu; Arthur Gretton (NIPS, 2017)

Informative Features for Model Comparison. - Wittawat Jitkrittum; Heishiro Kanagawa; Patsorn Sangkloy; James Hays; Bernhard Schölkopf; Arthur Gretton (NeurIPS, 2018)

Model Inference with Stein Density Ratio Estimation. - Song Liu 002; Wittawat Jitkrittum; Carl Henrik Ek (CoRR, 2018)

Kernel Mean Matching for Content Addressability of GANs. - Wittawat Jitkrittum; Patsorn Sangkloy; Muhammad Waleed Gondal; Amit Raj; James Hays; Bernhard Schölkopf (ICML, 2019)

Fisher Efficient Inference of Intractable Models. - Song Liu 002; Takafumi Kanamori; Wittawat Jitkrittum; Yu Chen (NeurIPS, 2019)

Kernel Stein Tests for Multiple Model Comparison. - Jen Ning Lim; Makoto Yamada; Bernhard Schölkopf; Wittawat Jitkrittum (NeurIPS, 2019)

Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces. - Arash Mehrjou; Wittawat Jitkrittum; Bernhard Schölkopf; Krikamol Muandet (CoRR, 2019)

A Kernel Stein Test for Comparing Latent Variable Models. - Heishiro Kanagawa; Wittawat Jitkrittum; Lester Mackey; Kenji Fukumizu; Arthur Gretton (CoRR, 2019)

ABCDP: Approximate Bayesian Computation Meets Differential Privacy. - Mijung Park; Wittawat Jitkrittum (CoRR, 2019)

More Powerful Selective Kernel Tests for Feature Selection. - Jen Ning Lim; Makoto Yamada; Wittawat Jitkrittum; Yoshikazu Terada; Shigeyuki Matsui; Hidetoshi Shimodaira (AISTATS, 2020)

Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. - Jia-Jie Zhu; Wittawat Jitkrittum; Moritz Diehl; Bernhard Schölkopf (CDC, 2020)

Learning Kernel Tests Without Data Splitting. - Jonas M. Kübler; Wittawat Jitkrittum; Bernhard Schölkopf; Krikamol Muandet (NeurIPS, 2020)

Testing Goodness of Fit of Conditional Density Models with Kernels. - Wittawat Jitkrittum; Heishiro Kanagawa; Bernhard Schölkopf (UAI, 2020)

Kernel Conditional Moment Test via Maximum Moment Restriction. - Krikamol Muandet; Wittawat Jitkrittum; Jonas M. Kübler (UAI, 2020)

Kernel Distributionally Robust Optimization. - Jia-Jie Zhu; Wittawat Jitkrittum; Moritz Diehl; Bernhard Schölkopf (CoRR, 2020)

Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation. - Jia-Jie Zhu; Wittawat Jitkrittum; Moritz Diehl; Bernhard Schölkopf (AISTATS, 2021)

Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces. - Ankit Singh Rawat; Aditya Krishna Menon; Wittawat Jitkrittum; Sadeep Jayasumana; Felix X. Yu; Sashank J. Reddi; Sanjiv Kumar (ICML, 2021)

An Optimal Witness Function for Two-Sample Testing. - Jonas M. Kübler; Wittawat Jitkrittum; Bernhard Schölkopf; Krikamol Muandet (CoRR, 2021)

HD-cos Networks: Efficient Neural Architectures for Secure Multi-Party Computation. - Wittawat Jitkrittum; Michal Lukasik; Ananda Theertha Suresh; Felix X. Yu; Gang Wang (CoRR, 2021)

ABCDP: Approximate Bayesian Computation with Differential Privacy. - Mijung Park; Margarita Vinaroz; Wittawat Jitkrittum (Entropy, 2021)

A Witness Two-Sample Test. - Jonas M. Kübler; Wittawat Jitkrittum; Bernhard Schölkopf; Krikamol Muandet (AISTATS, 2022)

A Sketch is Worth a Thousand Words: Image Retrieval with Text and Sketch. - Patsorn Sangkloy; Wittawat Jitkrittum; Diyi Yang; James Hays (ECCV (38), 2022)

Post-hoc estimators for learning to defer to an expert. - Harikrishna Narasimhan; Wittawat Jitkrittum; Aditya Krishna Menon; Ankit Singh Rawat; Sanjiv Kumar (NeurIPS, 2022)

ELM: Embedding and Logit Margins for Long-Tail Learning. - Wittawat Jitkrittum; Aditya Krishna Menon; Ankit Singh Rawat; Sanjiv Kumar (CoRR, 2022)

Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'. - Antonin Schrab; Wittawat Jitkrittum; Zoltán Szabó 001; Dino Sejdinovic; Arthur Gretton (CoRR, 2022)

When Does Confidence-Based Cascade Deferral Suffice? - Wittawat Jitkrittum; Neha Gupta; Aditya Krishna Menon; Harikrishna Narasimhan; Ankit Singh Rawat; Sanjiv Kumar (NeurIPS, 2023)

EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval. - Seungyeon Kim 001; Ankit Singh Rawat; Manzil Zaheer; Sadeep Jayasumana; Veeranjaneyulu Sadhanala; Wittawat Jitkrittum; Aditya Krishna Menon; Rob Fergus; Sanjiv Kumar (CoRR, 2023)

Learning to reject meets OOD detection: Are all abstentions created equal? - Harikrishna Narasimhan; Aditya Krishna Menon; Wittawat Jitkrittum; Sanjiv Kumar (CoRR, 2023)

It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models. - Lin Chen; Michal Lukasik; Wittawat Jitkrittum; Chong You; Sanjiv Kumar (CoRR, 2023)

On Bias-Variance Alignment in Deep Models. - Lin Chen; Michal Lukasik; Wittawat Jitkrittum; Chong You; Sanjiv Kumar (ICLR, 2024)

Language Model Cascades: Token-Level Uncertainty And Beyond. - Neha Gupta; Harikrishna Narasimhan; Wittawat Jitkrittum; Ankit Singh Rawat; Aditya Krishna Menon; Sanjiv Kumar (ICLR, 2024)

Learning to Reject Meets Long-tail Learning. - Harikrishna Narasimhan; Aditya Krishna Menon; Wittawat Jitkrittum; Neha Gupta; Sanjiv Kumar (ICLR, 2024)

Plugin estimators for selective classification with out-of-distribution detection. - Harikrishna Narasimhan; Aditya Krishna Menon; Wittawat Jitkrittum; Sanjiv Kumar (ICLR, 2024)

USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval. - Seungyeon Kim 001; Ankit Singh Rawat; Manzil Zaheer; Wittawat Jitkrittum; Veeranjaneyulu Sadhanala; Sadeep Jayasumana; Aditya Krishna Menon; Rob Fergus; Sanjiv Kumar (ICML, 2024)

Cascade-Aware Training of Language Models. - Congchao Wang; Sean Augenstein; Keith Rush; Wittawat Jitkrittum; Harikrishna Narasimhan; Ankit Singh Rawat; Aditya Krishna Menon; Alec Go (CoRR, 2024)

A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs. - Ankit Singh Rawat; Veeranjaneyulu Sadhanala; Afshin Rostamizadeh; Ayan Chakrabarti; Wittawat Jitkrittum; Vladimir Feinberg; Seungyeon Kim 001; Hrayr Harutyunyan; Nikunj Saunshi; Zachary Nado; Rakesh Shivanna; Sashank J. Reddi; et al. (CoRR, 2024)

Faster Cascades via Speculative Decoding. - Harikrishna Narasimhan; Wittawat Jitkrittum; Ankit Singh Rawat; Seungyeon Kim 001; Neha Gupta; Aditya Krishna Menon; Sanjiv Kumar (ICLR, 2025)

Universal Model Routing for Efficient LLM Inference. - Wittawat Jitkrittum; Harikrishna Narasimhan; Ankit Singh Rawat; Jeevesh Juneja; Zifeng Wang; Chen-Yu Lee; Pradeep Shenoy; Rina Panigrahy; Aditya Krishna Menon; Sanjiv Kumar (CoRR, 2025)

I Know What I Don't Know: Improving Model Cascades Through Confidence Tuning. - Stephan Rabanser; Nathalie Rauschmayr; Achin Kulshrestha; Petra Poklukar; Wittawat Jitkrittum; Sean Augenstein; Congchao Wang; Federico Tombari (CoRR, 2025)

Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation. - Michal Lukasik; Lin Chen; Harikrishna Narasimhan; Aditya Krishna Menon; Wittawat Jitkrittum; Felix X. Yu; Sashank J. Reddi; Gang Fu; MohammadHossein Bateni; Sanjiv Kumar (CoRR, 2025)

Cost-Aware Routing for Efficient Text-To-Image Generation. - Qinchan Li; Kenneth Chen; Changyue Su; Wittawat Jitkrittum; Qi Sun; Patsorn Sangkloy (CoRR, 2025)