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On the Unlikelihood of D-Separation


Mar 10, 2023
Itai Feigenbaum, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Devansh Arpit

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Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data


Jan 25, 2023
Devansh Arpit, Matthew Fernandez, Chenghao Liu, Weiran Yao, Wenzhuo Yang, Paul Josel, Shelby Heinecke, Eric Hu, Huan Wang, Stephen Hoi, Caiming Xiong, Kun Zhang, Juan Carlos Niebles

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Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving


Dec 16, 2022
Jonathan Francis, Bingqing Chen, Weiran Yao, Eric Nyberg, Jean Oh

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* Accepted: 1st Workshop on Safe Learning for Autonomous Driving, at the International Conference on Machine Learning (ICML 2022); Best Paper Award 

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Temporally Disentangled Representation Learning


Oct 24, 2022
Weiran Yao, Guangyi Chen, Kun Zhang

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* NeurIPS 2022. arXiv admin note: text overlap with arXiv:2202.04828 

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Prompt Learning with Optimal Transport for Vision-Language Models


Oct 03, 2022
Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, Kun Zhang

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Learning Latent Causal Dynamics


Feb 23, 2022
Weiran Yao, Guangyi Chen, Kun Zhang

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Learning Temporally Causal Latent Processes from General Temporal Data


Oct 11, 2021
Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang

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From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data


Sep 29, 2020
Weiran Yao, Sean Qian

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Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction


May 21, 2020
Weiran Yao, Sean Qian

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* To be published in Transportation Research Record (2020) 

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