Kaidi Cao

I am a final-year Computer Science Ph.D. candidate at Stanford University, advised by Prof. Jure Leskovec. I received B.E. from Tsinghua University.

My research interests lie generally in the area of machine learning, with focus on efficient, robust learning algorithms. I am also interested in retrieval-augmented LLMs and multi-modal alignment.

Email / LinkedIn / Google Scholar / Github

Selected Publications
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Learning Large Graph Property Prediction via Graph Segment Training
Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis, Jure Leskovec, Bryan Perozzi
Neural Information Processing Systems (NeurIPS), 2023
[pdf] [code]

We propose Graph Segment Training (GST), which is able to train on large graphs with constant (GPU) memory footprint.

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TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao, Bahare Fatemi, Charith Mendis, Bryan Perozzi
Neural Information Processing Systems (NeurIPS), 2023
[pdf]

We present a dataset for TPU-based performance prediction, offering new scalability challenges.

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AutoTransfer: AutoML with Knowledge Transfer - An Application to Graph Neural Networks
Kaidi Cao, Jiaxuan You, Jiaju Liu, Jure Leskovec
International Conference on Learning Representations (ICLR), 2023
[pdf]

We propose AutoTransfer, an AutoML solution that improves search efficiency by transferring the known architectural design knowledge to the novel task of interest.

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Annotation of Spatially Resolved Single-cell Data with STELLAR
Maria Brbić*, Kaidi Cao*, John W. Hickey*, Yuqi Tan, Michael P. Snyder, Garry P. Nolan & Jure Leskovec
Nature Methods, 2022
[pdf] [code]

We present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets.

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Relational Multi-Task Learning: Modeling Relations between Data and Tasks
Kaidi Cao*, Jiaxuan You*, Jure Leskovec
International Conference on Learning Representations (ICLR), 2022
[pdf] [code]

We propose MetaLink to solve a variety of multi-task learning settings, by constructing a knowledge graph over data points and tasks.

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Open-World Semi-Supervised Learning
Kaidi Cao*, Maria Brbić*, Jure Leskovec
International Conference on Learning Representations (ICLR), 2022
[pdf] [code]

We propose a pipeline that recognizes previously seen classes and discovers novel, never-before-seen classes at the same time.

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Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, Tengyu Ma
International Conference on Learning Representations (ICLR), 2021
[pdf] [code]

We propose a data-dependent regularization technique for heteroskedastic and imbalanced datasets.

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Concept Learners for Few-Shot Learning
Kaidi Cao*, Maria Brbić*, Jure Leskovec
International Conference on Learning Representations (ICLR), 2021
[pdf] [code]

COMET learns generalizable representations along human-understandable concept dimensions.

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Coresets for Robust Training of Neural Networks against Noisy Labels
Baharan Mirzasoleiman, Kaidi Cao, Jure Leskovec
Neural Information Processing Systems (NeurIPS), 2020
[pdf] [code]

We propose a theoretically-principled method to create sets of clean data to train a model with noisy labels.

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Few-Shot Video Classification via Temporal Alignment
Kaidi Cao, Jingwei Ji*, Zhangjie Cao*, Chien-Yi Chang, Juan Carlos Niebles
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
[pdf] [split]

We propose a video few-shot learning framework that explicitly leverages the temporal ordering information in video data through temporal alignment.

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Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, Tengyu Ma
Neural Information Processing Systems (NeurIPS), 2019
Oral presentation at the Bay Area Machine Learning Symposium
[pdf] [code]

We design two novel methods to improve imbalanced training.

sigasia2018

CariGANs: Unpaired Photo-to-Caricature Translation
Kaidi Cao, Jing Liao, Lu Yuan
ACM Transactions on Graphics, (Proc. of Siggraph Asia), 2018
[pdf] [project page]

We present the first deep learning-based approach to automatically generate the facial caricature for a given portrait photo.

Press Coverage:
dream_mapping

Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
Kaidi Cao*, Yu Rong*, Cheng Li, Xiaoou Tang, Chen Change Loy
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
[pdf][project page][code]

We presented a Deep Residual EquivAriant Mapping (DREAM) block to improve the performance of face recognition on profile faces.

cluster_pipeline

Merge or Not? Learning to Group Faces via Imitation Learning
Yue He*, Kaidi Cao*, Cheng Li, Chen Change Loy
AAAI Conference on Artificial Intelligence (AAAI), 2018
[pdf][code]

We proposed a novel face grouping framework that makes sequential merging decision based on short- and long-term rewards via inverse reinforcement learning.

Academic Services
  • Conference Reviewer: CVPR, ICCV, ECCV, AAAI, SIGGRAPH, NeurIPS, ICLR, ICML

  • Journal Reviewer: IJCV, TNNLS, PAMI

Teaching