Chun-Liang Li
李俊良
I am a research scientist at Apple MLR and an affiliate assistant professor at the Allen School of Computer Science University of Washington. I received my PhD in Machine Learning from Carnegie Mellon University supervised by Prof. Barnabás Póczos. Prior to joining CMU, I received my B.S. and M.S. degree at National Taiwan University under supervision of Prof. Hsuan-Tien Lin. In the past years, I was fortunate to work with many talented students and learn from them. Feel free to drop me an email if you are interested in an internship or collaborating with me.
Contact
Education
2014/09 -- 2019/08
Carnegie Mellon University, Pittsburgh, USA
Ph.D. in Machine Learning Department
2008/09 -- 2013/06
National Taiwan University, Taipei, Taiwan
B.S. / M.S. in Computer Science and Information Engineering
Selected Awards
Publications (* denotes equal contribution)
- 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 In Advances in Neural Information Processing Systems (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 In Advances in Neural Information Processing Systems (NeurIPS), 2024
- MUSCLE: A Model Update Strategy for Compatible LLM Evolution Jessica Echterhoff, Fartash Faghri, Raviteja Vemulapalli, Ting-Yao Hu, Chun-Liang Li, Oncel Tuzel, Hadi Pouransari In Empirical Methods in Natural Language Processing (EMNLP), 2024
- Found in the middle: Calibrating positional attention bias improves long context utilization Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long T. Le, Abhishek Kumar, James Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister In Annual Meeting of the Association for Computational Linguistics (ACL), 2024
- CodecLM: Aligning Language Models with Tailored Synthetic Data Zifeng Wang, Chun-Liang Li, Vincent Perot, Long T. Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024
- Chain-of-table: Evolving tables in the reasoning chain for table understanding Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, Tomas Pfister In International Conference on Learning Representations (ICLR), 2024
- 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, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee and Tomas Pfister In Annual Meeting of the Association for Computational Linguistics (ACL), 2023 (Google ACL 2023 Spotlight) [code]
- 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, Nikolai Glushnev, Renshen Wang, Joshua Ainslie, Shangbang Long, Siyang Qin, Yasuhisa Fujii, Nan Hua and Tomas Pfister In Annual Meeting of the Association for Computational Linguistics (ACL), 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 and Tomas Pfister In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023 [code]
- Prefix conditioning unifies language and label supervision Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko and Tomas Pfister In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023
- Hyperbolic contrastive learning for visual representations beyond objects Songwei Ge, Shlok Mishra, Simon Kornblith, Chun-Liang Li and David Jacobs In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023 [code]
- Learning Instance-Specific Adaptation for Cross-Domain Segmentation Yuliang Zou, Zizhao Zhang, Chun-Liang Li, Han Zhang, Tomas Pfister and Jia-Bin Huang In European Conference on Computer Vision (ECCV), 2022 [code]
- 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 and Tomas Pfister In Annual Meeting of the Association for Computational Linguistics (ACL), 2022 [blog]
- Decoupling Local and Global Representations of Time Series Sana Tonekaboni, Chun-Liang Li, Sercan Arik, Anna Goldenberg and Tomas Pfister In International Conference on Artificial Intelligence and Statistic (AISTATS), 2022 [code]
- DISSECT: Disentangled Simultaneous Explanations via Concept Traversals Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, and Rosalind W. Picard In International Conference on Learning Representations (ICLR), 2022 [code]
- A Unified View of cGANs with and without Classifiers Si-An Chen, Chun-Liang Li and Hsuan-Tien Lin In Advances in Neural Information Processing Systems (NeurIPS), 2021 [code]
- Robust Contrastive Learning Using Negative Samples with Diminished Semantics Songwei Ge, Shlok Mishra, Haohan Wang, Chun-Liang Li and David Jacobs In Advances in Neural Information Processing Systems (NeurIPS), 2021 [code]
- Object-aware Contrastive Learning for Debiased Scene Representation Sangwoo Mo, Hyunwoo Kang, Kihyuk Sohn, Chun-Liang Li and Jinwoo Shin In Advances in Neural Information Processing Systems (NeurIPS), 2021 [code]
- 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 Popat and Tomas Pfister In Annual Meeting of the Association for Computational Linguistics (ACL), 2021 (Oral)
- Unsupervised Program Synthesis for Images using Tree-Structured LSTM Chenghui Zou, Chun-Liang Li and Barnabás Póczos In Uncertainty in Artificial Intelligence (UAI), 2021
- Deep Generative Models for Galaxy Image Simulations François Lanusse, Rachel Mandelbaum, Siamak Ravanbakhsh, Chun-Liang Li, Peter Freeman and Barnabás Póczos In Monthly Notices of the Royal Astronomical Society (MNRAS), 2021. [code]
- CutPaste: Self-Supervised Learning for Anomaly Detection and Localization Chun-Liang Li*, Kihyuk Sohn*, Jinsung Yoon, and Tomas Pfister In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 [blog]
- Learning and Evaluating Representations for Deep One-class Classification Kihyuk Sohn*, Chun-Liang Li*, Jinsung Yoon, Minho Jin, and Tomas Pfister In International Conference on Learning Representations (ICLR), 2021 [code]
- PseudoSeg: Designing Pseudo Labels for Semantic Segmentation Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister In International Conference on Learning Representations (ICLR), 2021 [code]
- i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, and Honglak Lee In International Conference on Learning Representations (ICLR), 2021 [code]
- Interpretable Sequence Learning for Covid-19 Forecasting Sercan Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long Le, Vikas Menon, Shashank Singh, Leyou Zhang, Nate Yoder, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal and Tomas Pfister In Advances in Neural Information Processing Systems (NeurIPS), 2020 (Spotlight)
- FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang and Colin Raffel In Advances in Neural Information Processing Systems (NeurIPS), 2020
- On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister and Pradeep Ravikumar In Advances in Neural Information Processing Systems (NeurIPS), 2020
- Learning Generative Models using Transformations Chun-Liang Li PhD Thesis, Carnegie Mellon University, 2019
- LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds Chun-Liang Li, Tomas Simon, Jason Saragih, Barnabás Póczos and Yaser Sheikh In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [video]
- Implicit Kernel Learning Chun-Liang Li, Wei-Chen Chang, Youssef Mroueh, Yiming Yang and Barnabás Póczos In International Conference on Artificial Intelligence and Statistic (AISTATS), 2019
- Kernel Change-Point Detection with Auxiliary Deep Generative Models Wei-Chen Chang, Chun-Liang Li, Yiming Yang and Barnabás Póczos In International Conference on Learning Representations (ICLR), 2019 [code]
- Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer Hsueh-Ti Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai and Alec Jacobson In International Conference on Learning Representations (ICLR), 2019
- Point Cloud GAN Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabás Póczos and Ruslan Salakhutdinov In ICLR Workshop on Deep Generative Models for Highly Structured Data, 2019 [code]
- Nonparametric Density Estimation with Adversarial Losses Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer and Barnabás Póczos In Advances in Neural Information Processing Systems (NIPS), 2018
- Classifier Two-Sample Test for Video Anomaly Detections Yusha Liu*, Chun-Liang Li*, and Barnabás Póczos In British Machine Vision Conference (BMVC), 2018 [code]
- Sobolev GAN Youssef Mroueh, Chun-Liang Li*, Tom Sercu*, Anant Raj*, and Yu Cheng In International Conference on Learning Representations (ICLR), 2018 [code]
- CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding Francois Lanusse, Quanbin Ma, Nan Li, Thomas E. Collett, Chun-Liang Li, Siamak Ravanbakhsh, Rachel Mandelbaum and Barnabás Póczos In Monthly Notices of the Royal Astronomical Society (MNRAS), 2018. [code]
- MMD GAN: Towards Deeper Understanding of Moment Matching Network Chun-Liang Li*, Wei-Chen Chang*, Yu Cheng, Yiming Yang and Barnabás Póczos In Advances in Neural Information Processing Systems (NIPS), 2017 [code]
- One Network to Solve Them All — Solving Linear Inverse Problems using Deep Projection Models J. H. Rick Chang, Chun-Liang Li, Barnabás Póczos, B. V. K. Vijaya Kumar and Aswin C. Sankaranarayanan In International Conference on Computer Vision (ICCV), 2017 (Oral) [code]
- Data-driven Random Fourier Feature using Stein Effect Wei-Chen Chang, Chun-Liang Li, Yiming Yang and Barnabás Póczos In International Joint Conference on Artificial Intelligence (IJCAI), 2017 (Best student paper runner-up)
- Polynomial Optimization Methods for Matrix Factorization Po-Wei Wang, Chun-Liang Li, and J. Zico Kolter In AAAI Conference on Artificial Intelligence (AAAI), 2017
- Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods Chun-Liang Li and Barnabás Póczos In Uncertainty in Artificial Intelligence (UAI), 2016
- High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos and Jeff Schneider In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016
- Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA Chun-Liang Li, Hsuan-Tien Lin and Chi-Jen Lu In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016
- Active Learning with Hint Information Chun-Liang Li, Chun-Sung Ferng, and Hsuan-Tien Lin In Neural Computation, 2015
- Condensed Filter Tree for Cost-Sensitive Multi-Label Classification Chun-Liang Li and Hsuan-Tien Lin In International Conference on Machine Learning (ICML), 2014 [slide]
- Active Learning with Hinted Support Vector Machine Chun-Liang Li, Chun-Sung Ferng, and Hsuan-Tien Lin In Asian Conference on Machine Learning (ACML), 2012 [slide]