Kaixiang Lin
Applied scientist at Amazon Web Services.
Before joinng AWS, I was an applied scientist at Amazon Lab126, working on embodied AI. Previously, I completed my Ph.D. in computer science at MSU and my B.S. in electronic engineering and information science at USTC.
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Publications
2023
Automated Few-Shot Classification with Instruction-Finetuned Language Models
Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon Wilson.
PDF / EMNLP Findings .
A Unified Linear Speedup Analysis of Stochastic FedAvg and Nesterov Accelerated FedAvg
Zhaonan Qu*,
Kaixiang Lin*,
Zhaojian Li,
Jiayu Zhou,
Zhengyuan Zhou.
PDF / Journal of Artificial Intelligence Research (JAIR) / * denotes equal contribution.
Transfer Learning in Deep Reinforcement Learning: A Survey
Zhuangdi Zhu, Kaixiang Lin, Jiayu Zhou.
PDF / Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
2022
Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning
Tongzhou Mu, Kaixiang Lin, Feiyang Niu, Govind Thattai
PDF / Transactions on Machine Learning Research (TMLR)
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
Xiaofeng Gao, Qiaozi Gao, Ran Gong, Kaixiang Lin, Govind Thattai,
Gaurav Sukhatme
PDF / IEEE Robotics and Automation
Letters (RA-L)
Learning to Act with Affordance-Aware Multimodal Neural SLAM
Zhiwei Jia, Kaixiang Lin, Yizhou Zhao, Qiaozi Gao, Govind Thattai, Gaurav Sukhatme
PDF / IROS 2022
2021
RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection
Boyang Liu, Ding Wang,
Kaixiang Lin,
Pang-Ning Tan,
Jiayu Zhou
The 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021
PDF / code
2020
2018
2017
2016
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