A Linear Ensemble of Individual and Blended Models for Music Rating Prediction. - Po-Lung Chen; Chen-Tse Tsai; Yao-Nan Chen; Ku-Chun Chou; Chun-Liang Li; Cheng-Hao Tsai; Kuan-Wei Wu; Yu-Cheng Chou; Chung-Yi Li; Wei-Shih Lin; Shu-Hao Yu; Rong-Bing Chiu; et al. (KDD Cup, 2012)

Active Learning with Hinted Support Vector Machine. - Chun-Liang Li; Chun-Sung Ferng; Hsuan-Tien Lin (ACML, 2012)

Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation. - Todd G. McKenzie; Chun-Sung Ferng; Yao-Nan Chen; Chun-Liang Li; Cheng-Hao Tsai; Kuan-Wei Wu; Ya-Hsuan Chang; Chung-Yi Li; Wei-Shih Lin; Shu-Hao Yu; Chieh-Yen Lin; Po-Wei Wang; et al. (KDD Cup, 2012)

POSTER: Scanning-free Personalized Malware Warning System by Learning Implicit Feedback from Detection Logs. - Jyun-Yu Jiang; Chun-Liang Li; Chun-Pai Yang; Chung-Tsai Su (CCS, 2014)

Condensed Filter Tree for Cost-Sensitive Multi-Label Classification. - Chun-Liang Li; Hsuan-Tien Lin (ICML, 2014)

Effective string processing and matching for author disambiguation. - Wei-Sheng Chin; Yong Zhuang; Yu-Chin Juan; Felix Wu; Hsiao-Yu Tung; Tong Yu 001; Jui-Pin Wang; Cheng-Xia Chang; Chun-Pai Yang; Wei-Cheng Chang; Kuan-Hao Huang; Tzu-Ming Kuo; et al. (J. Mach. Learn. Res., 2014)

Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013. - Chun-Liang Li; Yu-Chuan Su; Ting-Wei Lin; Cheng-Hao Tsai; Wei-Cheng Chang; Kuan-Hao Huang; Tzu-Ming Kuo; Shan-Wei Lin; Young-San Lin; Yu-Chen Lu; Chun-Pai Yang; Cheng-Xia Chang; et al. (J. Mach. Learn. Res., 2015)

Active Learning Using Hint Information. - Chun-Liang Li; Chun-Sung Ferng; Hsuan-Tien Lin (Neural Comput., 2015)

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models. - Chun-Liang Li; Kirthevasan Kandasamy; Barnabás Póczos; Jeff G. Schneider (AISTATS, 2016)

Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA. - Chun-Liang Li; Hsuan-Tien Lin; Chi-Jen Lu (AISTATS, 2016)

Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods. - Chun-Liang Li; Barnabás Póczos (UAI, 2016)

Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM. - Chun-Liang Li; Siamak Ravanbakhsh; Barnabás Póczos (CoRR, 2016)

Polynomial Optimization Methods for Matrix Factorization. - Po-Wei Wang; Chun-Liang Li; J. Zico Kolter (AAAI, 2017)

One Network to Solve Them All - Solving Linear Inverse Problems Using Deep Projection Models. - Jen-Hao Rick Chang; Chun-Liang Li; Barnabás Póczos; B. V. K. Vijaya Kumar (ICCV, 2017)

Data-driven Random Fourier Features using Stein Effect. - Wei-Cheng Chang; Chun-Liang Li; Yiming Yang; Barnabás Póczos (IJCAI, 2017)

MMD GAN: Towards Deeper Understanding of Moment Matching Network. - Chun-Liang Li; Wei-Cheng Chang; Yu Cheng; Yiming Yang; Barnabás Póczos (NIPS, 2017)

One Network to Solve Them All - Solving Linear Inverse Problems using Deep Projection Models. - Jen-Hao Rick Chang; Chun-Liang Li; Barnabás Póczos; B. V. K. Vijaya Kumar; Aswin C. Sankaranarayanan (CoRR, 2017)

Classifier Two Sample Test for Video Anomaly Detections. - Yusha Liu; Chun-Liang Li; Barnabás Póczos (BMVC, 2018)

Sobolev GAN. - Youssef Mroueh; Chun-Liang Li; Tom Sercu; Anant Raj; Yu Cheng (ICLR (Poster), 2018)

Nonparametric Density Estimation under Adversarial Losses. - Shashank Singh 005; Ananya Uppal; Boyue Li; Chun-Liang Li; Manzil Zaheer; Barnabás Póczos (NeurIPS, 2018)

Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond. - Xi Ouyang; Yu Cheng 001; Yifan Jiang 001; Chun-Liang Li; Pan Zhou 001 (CoRR, 2018)

Adversarial Geometry and Lighting using a Differentiable Renderer. - Hsueh-Ti Derek Liu; Michael Tao 001; Chun-Liang Li; Derek Nowrouzezahrai; Alec Jacobson (CoRR, 2018)

Hallucinating Point Cloud into 3D Sculptural Object. - Chun-Liang Li; Eunsu Kang 001; Songwei Ge; Lingyao Zhang; Austin Dill; Manzil Zaheer; Barnabás Póczos (CoRR, 2018)

Bayesian CycleGAN via Marginalizing Latent Sampling. - Haoran You; Yu Cheng 001; Tianheng Cheng; Chun-Liang Li; Pan Zhou 001 (CoRR, 2018)

Implicit Kernel Learning. - Chun-Liang Li; Wei-Cheng Chang; Youssef Mroueh; Yiming Yang; Barnabás Póczos (AISTATS, 2019)

LBS Autoencoder: Self-Supervised Fitting of Articulated Meshes to Point Clouds. - Chun-Liang Li; Tomas Simon; Jason M. Saragih; Barnabás Póczos; Yaser Sheikh (CVPR, 2019)

Kernel Change-point Detection with Auxiliary Deep Generative Models. - Wei-Cheng Chang; Chun-Liang Li; Yiming Yang; Barnabás Póczos (ICLR (Poster), 2019)

Point Cloud GAN. - Chun-Liang Li; Manzil Zaheer; Yang Zhang; Barnabás Póczos; Ruslan Salakhutdinov (DGS@ICLR, 2019)

Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer. - Hsueh-Ti Derek Liu; Michael Tao 001; Chun-Liang Li; Derek Nowrouzezahrai; Alec Jacobson (ICLR (Poster), 2019)

Developing Creative AI to Generate Sculptural Objects. - Songwei Ge; Austin Dill; Eunsu Kang 001; Chun-Liang Li; Lingyao Zhang; Manzil Zaheer; Barnabás Póczos (CoRR, 2019)

On Concept-Based Explanations in Deep Neural Networks. - Chih-Kuan Yeh; Been Kim; Sercan Ömer Arik; Chun-Liang Li; Pradeep Ravikumar; Tomas Pfister (CoRR, 2019)

Getting Topology and Point Cloud Generation to Mesh. - Austin Dill; Chun-Liang Li; Songwei Ge; Eunsu Kang 001 (CoRR, 2019)

Learned Interpolation for 3D Generation. - Austin Dill; Songwei Ge; Eunsu Kang 001; Chun-Liang Li; Barnabás Póczos (CoRR, 2019)

Interpretable Sequence Learning for Covid-19 Forecasting. - Sercan Ömer Arik; Chun-Liang Li; Jinsung Yoon; Rajarishi Sinha; Arkady Epshteyn; Long T. Le; Vikas Menon; Shashank Singh 005; Leyou Zhang; Martin Nikoltchev; Yash Sonthalia; Hootan Nakhost; et al. (NeurIPS, 2020)

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. - Kihyuk Sohn; David Berthelot; Nicholas Carlini; Zizhao Zhang; Han Zhang 010; Colin Raffel; Ekin Dogus Cubuk; Alexey Kurakin; Chun-Liang Li (NeurIPS, 2020)

On Completeness-aware Concept-Based Explanations in Deep Neural Networks. - Chih-Kuan Yeh; Been Kim; Sercan Ömer Arik; Chun-Liang Li; Tomas Pfister; Pradeep Ravikumar (NeurIPS, 2020)

Unsupervised Program Synthesis for Images using Tree-Structured LSTM. - Chenghui Zhou; Chun-Liang Li; Barnabás Póczos (CoRR, 2020)

A Simple Semi-Supervised Learning Framework for Object Detection. - Kihyuk Sohn; Zizhao Zhang; Chun-Liang Li; Han Zhang 010; Chen-Yu Lee; Tomas Pfister (CoRR, 2020)

Kernel Stein Generative Modeling. - Wei-Cheng Chang; Chun-Liang Li; Youssef Mroueh; Yiming Yang (CoRR, 2020)

Interpretable Sequence Learning for COVID-19 Forecasting. - Sercan Ömer Arik; Chun-Liang Li; Jinsung Yoon; Rajarishi Sinha; Arkady Epshteyn; Long T. Le; Vikas Menon; Shashank Singh 005; Leyou Zhang; Nate Yoder; Martin Nikoltchev; Yash Sonthalia; et al. (CoRR, 2020)

i-Mix: A Strategy for Regularizing Contrastive Representation Learning. - Kibok Lee 003; Yian Zhu; Kihyuk Sohn; Chun-Liang Li; Jinwoo Shin; Honglak Lee (CoRR, 2020)

Learning Generative Models using Transformations. - Chun-Liang Li (2020)

ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction. - Chen-Yu Lee; Chun-Liang Li; Chu Wang; Renshen Wang; Yasuhisa Fujii; Siyang Qin; Ashok C. Popat; Tomas Pfister (ACL/IJCNLP (2), 2021)

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization. - Chun-Liang Li; Kihyuk Sohn; Jinsung Yoon; Tomas Pfister (CVPR, 2021)

i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning. - Kibok Lee 003; Yian Zhu; Kihyuk Sohn; Chun-Liang Li; Jinwoo Shin; Honglak Lee (ICLR, 2021)

Learning and Evaluating Representations for Deep One-Class Classification. - Kihyuk Sohn; Chun-Liang Li; Jinsung Yoon; Minho Jin; Tomas Pfister (ICLR, 2021)

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. - Yuliang Zou; Zizhao Zhang; Han Zhang 010; Chun-Liang Li; Xiao Bian; Jia-Bin Huang 001; Tomas Pfister (ICLR, 2021)

A Unified View of cGANs with and without Classifiers. - Si-An Chen; Chun-Liang Li; Hsuan-Tien Lin (NeurIPS, 2021)

Robust Contrastive Learning Using Negative Samples with Diminished Semantics. - Songwei Ge; Shlok Mishra; Chun-Liang Li; Haohan Wang; David Jacobs 001 (NeurIPS, 2021)

Object-aware Contrastive Learning for Debiased Scene Representation. - Sangwoo Mo; Hyunwoo Kang; Kihyuk Sohn; Chun-Liang Li; Jinwoo Shin (NeurIPS, 2021)

Unsupervised program synthesis for images by sampling without replacement. - Chenghui Zhou; Chun-Liang Li; Barnabás Póczos (UAI, 2021)

Self-Trained One-class Classification for Unsupervised Anomaly Detection. - Jinsung Yoon; Kihyuk Sohn; Chun-Liang Li; Sercan Ö. Arik; Chen-Yu Lee; Tomas Pfister (CoRR, 2021)

Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration. - Si-An Chen; Chun-Liang Li; Hsuan-Tien Lin (CoRR, 2021)

A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. - Sercan Ö. Arik; Joel Shor; Rajarishi Sinha; Jinsung Yoon; Joseph R. Ledsam; Long T. Le; Michael W. Dusenberry; Nathanael C. Yoder; Kris Popendorf; Arkady Epshteyn; Johan Euphrosine; Elli Kanal; et al. (npj Digit. Medicine, 2021)

Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling. - Haoran You; Yu Cheng 001; Tianheng Cheng; Chun-Liang Li; Pan Zhou 001 (IEEE Trans. Neural Networks Learn. Syst., 2021)

FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction. - Chen-Yu Lee; Chun-Liang Li; Timothy Dozat; Vincent Perot; Guolong Su; Nan Hua; Joshua Ainslie; Renshen Wang; Yasuhisa Fujii; Tomas Pfister (ACL (1), 2022)

Decoupling Local and Global Representations of Time Series. - Sana Tonekaboni; Chun-Liang Li; Sercan Ö. Arik; Anna Goldenberg; Tomas Pfister (AISTATS, 2022)

Learning Instance-Specific Adaptation for Cross-Domain Segmentation. - Yuliang Zou; Zizhao Zhang; Chun-Liang Li; Han Zhang 010; Tomas Pfister; Jia-Bin Huang 001 (ECCV (33), 2022)

DISSECT: Disentangled Simultaneous Explanations via Concept Traversals. - Asma Ghandeharioun; Been Kim; Chun-Liang Li; Brendan Jou; Brian Eoff; Rosalind W. Picard (ICLR, 2022)

Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection. - Jinsung Yoon; Kihyuk Sohn; Chun-Liang Li; Sercan Ö. Arik; Chen-Yu Lee; Tomas Pfister (Trans. Mach. Learn. Res., 2022)

Neural Spline Search for Quantile Probabilistic Modeling. - Ruoxi Sun 002; Chun-Liang Li; Sercan Ö. Arik; Michael W. Dusenberry; Chen-Yu Lee; Tomas Pfister (AAAI, 2023)

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. - Cheng-Yu Hsieh; Chun-Liang Li; Chih-Kuan Yeh; Hootan Nakhost; Yasuhisa Fujii; Alex Ratner; Ranjay Krishna; Chen-Yu Lee; Tomas Pfister (ACL (Findings), 2023)

FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction. - Chen-Yu Lee; Chun-Liang Li; Hao Zhang; Timothy Dozat; Vincent Perot; Guolong Su; Xiang Zhang; Kihyuk Sohn; Nikolay Glushnev; Renshen Wang; Joshua Ainslie; Shangbang Long; et al. (ACL (1), 2023)

Hyperbolic Contrastive Learning for Visual Representations beyond Objects. - Songwei Ge; Shlok Mishra; Simon Kornblith; Chun-Liang Li; David Jacobs 001 (CVPR, 2023)

Prefix Conditioning Unifies Language and Label Supervision. - Kuniaki Saito; Kihyuk Sohn; Xiang Zhang; Chun-Liang Li; Chen-Yu Lee; Kate Saenko; Tomas Pfister (CVPR, 2023)

Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval. - Kuniaki Saito; Kihyuk Sohn; Xiang Zhang; Chun-Liang Li; Chen-Yu Lee; Kate Saenko; Tomas Pfister (CVPR, 2023)

Unifying Distribution Alignment as a Loss for Imbalanced Semi-supervised Learning. - Justin Lazarow; Kihyuk Sohn; Chen-Yu Lee; Chun-Liang Li; Zizhao Zhang; Tomas Pfister (WACV, 2023)

Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types. - Kihyuk Sohn; Jinsung Yoon; Chun-Liang Li; Chen-Yu Lee; Tomas Pfister (WACV, 2023)

TSMixer: An all-MLP Architecture for Time Series Forecasting. - Si-An Chen; Chun-Liang Li; Nate Yoder; Sercan Ö. Arik; Tomas Pfister (CoRR, 2023)

Re-Benchmarking Pool-Based Active Learning for Binary Classification. - Po-Yi Lu; Chun-Liang Li; Hsuan-Tien Lin (CoRR, 2023)

Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models. - Cheng-Yu Hsieh; Si-An Chen; Chun-Liang Li; Yasuhisa Fujii; Alexander Ratner; Chen-Yu Lee; Ranjay Krishna; Tomas Pfister (CoRR, 2023)

TSMixer: An All-MLP Architecture for Time Series Forecast-ing. - Si-An Chen; Chun-Liang Li; Sercan Ö. Arik; Nathanael C. Yoder; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. - Jinsung Yoon; Kihyuk Sohn; Chun-Liang Li; Sercan Ö. Arik; Tomas Pfister (Trans. Mach. Learn. Res., 2023)

Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization. - Cheng-Yu Hsieh; Yung-Sung Chuang; Chun-Liang Li; Zifeng Wang 002; Long T. Le; Abhishek Kumar; James R. Glass; Alexander Ratner; Chen-Yu Lee; Ranjay Krishna; Tomas Pfister (ACL (Findings), 2024)

MUSCLE: A Model Update Strategy for Compatible LLM Evolution. - Jessica Maria Echterhoff; Fartash Faghri; Raviteja Vemulapalli; Ting-Yao Hu; Chun-Liang Li; Oncel Tuzel; Hadi Pouransari (EMNLP (Findings), 2024)

Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding. - Zilong Wang 002; Hao Zhang; Chun-Liang Li; Julian Martin Eisenschlos; Vincent Perot; Zifeng Wang 002; Lesly Miculicich; Yasuhisa Fujii; Jingbo Shang; Chen-Yu Lee; Tomas Pfister (ICLR, 2024)

CodecLM: Aligning Language Models with Tailored Synthetic Data. - Zifeng Wang 002; Chun-Liang Li; Vincent Perot; Long T. Le; Jin Miao; Zizhao Zhang; Chen-Yu Lee; Tomas Pfister (NAACL-HLT (Findings), 2024)

The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better. - Scott Geng; Cheng-Yu Hsieh; Vivek Ramanujan; Matthew Wallingford; Chun-Liang Li; Pang Wei Koh; Ranjay Krishna (NeurIPS, 2024)

Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum. - Hadi Pouransari; Chun-Liang Li; Jen-Hao Rick Chang; Pavan Kumar Anasosalu Vasu; Cem Koc; Vaishaal Shankar; Oncel Tuzel (NeurIPS, 2024)

Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions. - Yu-Guan Hsieh; Cheng-Yu Hsieh; Shih-Ying Yeh; Louis Béthune; Hadipour Ansari; Pavan Kumar Anasosalu Vasu; Chun-Liang Li; Ranjay Krishna; Oncel Tuzel; Marco Cuturi (CoRR, 2024)

FastVLM: Efficient Vision Encoding for Vision Language Models. - Pavan Kumar Anasosalu Vasu; Fartash Faghri; Chun-Liang Li; Cem Koc; Nate True; Albert Antony; Gokula Santhanam; James Gabriel; Peter Grasch; Oncel Tuzel; Hadi Pouransari (CVPR, 2025)

An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets. - Po-Yi Lu; Yi-Jie Cheng; Chun-Liang Li; Hsuan-Tien Lin (Trans. Mach. Learn. Res., 2025)

FocalLens: Instruction Tuning Enables Zero-Shot Conditional Image Representations. - Cheng-Yu Hsieh; Pavan Kumar Anasosalu Vasu; Fartash Faghri; Raviteja Vemulapalli; Chun-Liang Li; Ranjay Krishna; Oncel Tuzel; Hadi Pouransari (CoRR, 2025)

The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains. - Scott Geng; Hamish Ivison; Chun-Liang Li; Maarten Sap; Jerry Li; Ranjay Krishna; Pang Wei Koh (CoRR, 2025)