Efficient mini-batch training for stochastic optimization. - Mu Li 003; Tong Zhang 001; Yuqiang Chen; Alexander J. Smola (KDD, 2014)

Communication Efficient Distributed Machine Learning with the Parameter Server. - Mu Li 003; David G. Andersen; Alexander J. Smola; Kai Yu (NIPS, 2014)

Scaling Distributed Machine Learning with the Parameter Server. - Mu Li 003; David G. Andersen; Jun Woo Park; Alexander J. Smola; Amr Ahmed 001; Vanja Josifovski; James Long; Eugene J. Shekita; Bor-Yiing Su (OSDI, 2014)

Cuckoo Linear Algebra. - Li Zhou 006; David G. Andersen; Mu Li 003; Alexander J. Smola (KDD, 2015)

Inferring Movement Trajectories from GPS Snippets. - Mu Li 003; Amr Ahmed 001; Alexander J. Smola (WSDM, 2015)

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. - Tianqi Chen; Mu Li 003; Yutian Li; Min Lin; Naiyan Wang; Minjie Wang; Tianjun Xiao; Bing Xu; Chiyuan Zhang; Zheng Zhang 001 (CoRR, 2015)

Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning. - Mu Li 003; Dave G. Andersen; Alexander J. Smola (CoRR, 2015)

AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization. - Suvrit Sra; Adams Wei Yu; Mu Li 003; Alexander J. Smola (CoRR, 2015)

Empirical Evaluation of Rectified Activations in Convolutional Network. - Bing Xu; Naiyan Wang; Tianqi Chen; Mu Li 003 (CoRR, 2015)

AdaDelay: Delay Adaptive Distributed Stochastic Optimization. - Suvrit Sra; Adams Wei Yu; Mu Li 003; Alexander J. Smola (AISTATS, 2016)

DiFacto: Distributed Factorization Machines. - Mu Li 003; Ziqi Liu; Alexander J. Smola; Yu-Xiang Wang 003 (WSDM, 2016)

On the Powerball Method. - Ye Yuan 002; Mu Li 003; Claire J. Tomlin (CoRR, 2016)

Data Driven Resource Allocation for Distributed Learning. - Travis Dick; Mu Li 003; Venkata Krishna Pillutla; Colin White; Maria-Florina Balcan; Alexander J. Smola (AAAI Workshops, 2017)

Bag of Tricks for Image Classification with Convolutional Neural Networks. - Tong He 002; Zhi Zhang 005; Hang Zhang 005; Zhongyue Zhang; Junyuan Xie; Mu Li 003 (CVPR, 2019)

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs. - Leyuan Wang; Zhi Chen 030; Yizhi Liu; Yao Wang; Lianmin Zheng; Mu Li 003; Yida Wang 003 (ICPP, 2019)

Optimizing CNN Model Inference on CPUs. - Yizhi Liu; Yao Wang; Ruofei Yu; Mu Li 003; Vin Sharma; Yida Wang 003 (USENIX ATC, 2019)

Bag of Freebies for Training Object Detection Neural Networks. - Zhi Zhang 005; Tong He 002; Hang Zhang 005; Zhongyue Zhang; Junyuan Xie; Mu Li 003 (CoRR, 2019)

Language Models with Transformers. - Chenguang Wang 001; Mu Li 003; Alexander J. Smola (CoRR, 2019)

Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources. - Haibin Lin; Hang Zhang 005; Yifei Ma; Tong He 002; Zhi Zhang 005; Sheng Zha; Mu Li 003 (CoRR, 2019)

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. - Jian Guo; He He 001; Tong He 002; Leonard Lausen; Mu Li 003; Haibin Lin; Xingjian Shi; Chenguang Wang 001; Junyuan Xie; Sheng Zha; Aston Zhang; Hang Zhang 005; et al. (CoRR, 2019)

On the Powerball Method: Variants of Descent Methods for Accelerated Optimization. - Ye Yuan 002; Mu Li 003; Jun Liu 015; Claire J. Tomlin (IEEE Control. Syst. Lett., 2019)

CSER: Communication-efficient SGD with Error Reset. - Cong Xie; Shuai Zheng 004; Oluwasanmi Koyejo; Indranil Gupta; Mu Li 003; Haibin Lin (NeurIPS, 2020)

FeatGraph: a flexible and efficient backend for graph neural network systems. - Yuwei Hu; Zihao Ye 001; Minjie Wang; Jiali Yu; Da Zheng; Mu Li 003; Zheng Zhang 001; Zhiru Zhang; Yida Wang 003 (SC, 2020)

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. - Nick Erickson; Jonas Mueller 001; Alexander Shirkov; Hang Zhang 005; Pedro Larroy; Mu Li 003; Alexander J. Smola (CoRR, 2020)

Improving Semantic Segmentation via Self-Training. - Yi Zhu 001; Zhongyue Zhang; Chongruo Wu; Zhi Zhang 005; Tong He 002; Hang Zhang 005; R. Manmatha; Mu Li 003; Alexander J. Smola (CoRR, 2020)

Accelerated Large Batch Optimization of BERT Pretraining in 54 minutes. - Shuai Zheng 004; Haibin Lin; Sheng Zha; Mu Li 003 (CoRR, 2020)

A Comprehensive Study of Deep Video Action Recognition. - Yi Zhu 001; Xinyu Li 003; Chunhui Liu 002; Mohammadreza Zolfaghari; Yuanjun Xiong; Chongruo Wu; Zhi Zhang 005; Joseph Tighe; R. Manmatha; Mu Li 003 (CoRR, 2020)

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. - Jian Guo; He He 001; Tong He 002; Leonard Lausen; Mu Li 003; Haibin Lin; Xingjian Shi; Chenguang Wang 001; Junyuan Xie; Sheng Zha; Aston Zhang; Hang Zhang 005; et al. (J. Mach. Learn. Res., 2020)

Lorien: Efficient Deep Learning Workloads Delivery. - Cody Hao Yu; Xingjian Shi; Haichen Shen; Zhi Chen 030; Mu Li 003; Yida Wang 003 (SoCC, 2021)

Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing. - Haoyu He; Xingjian Shi; Jonas Mueller 001; Sheng Zha; Mu Li 003; George Karypis (SustaiNLP@EMNLP, 2021)

CrossNorm and SelfNorm for Generalization under Distribution Shifts. - Zhiqiang Tang 001; Yunhe Gao; Yi Zhu 001; Zhi Zhang 005; Mu Li 003; Dimitris N. Metaxas (ICCV, 2021)

Video Contrastive Learning with Global Context. - Haofei Kuang; Yi Zhu 001; Zhi Zhang 005; Xinyu Li 003; Joseph Tighe; Sören Schwertfeger; Cyrill Stachniss; Mu Li 003 (ICCVW, 2021)

A Unified Efficient Pyramid Transformer for Semantic Segmentation. - Fangrui Zhu; Yi Zhu 001; Li Zhang 040; Chongruo Wu; Yanwei Fu 001; Mu Li 003 (ICCVW, 2021)

Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference. - Haichen Shen; Jared Roesch; Zhi Chen 030; Wei Chen; Yong Wu; Mu Li 003; Vin Sharma; Zachary Tatlock; Yida Wang 003 (MLSys, 2021)

Blending Anti-Aliasing into Vision Transformer. - Shengju Qian; Hao Shao; Yi Zhu 001; Mu Li 003; Jiaya Jia (NeurIPS, 2021)

Benchmarking Multimodal AutoML for Tabular Data with Text Fields. - Xingjian Shi; Jonas Mueller 001; Nick Erickson; Mu Li 003; Alexander J. Smola (NeurIPS Datasets and Benchmarks, 2021)

Progressive Coordinate Transforms for Monocular 3D Object Detection. - Li Wang 033; Li Zhang 040; Yi Zhu 001; Zhi Zhang 005; Tong He 002; Mu Li 003; Xiangyang Xue 001 (NeurIPS, 2021)

SelfNorm and CrossNorm for Out-of-Distribution Robustness. - Zhiqiang Tang 001; Yunhe Gao; Yi Zhu 001; Zhi Zhang 005; Mu Li 003; Dimitris N. Metaxas (CoRR, 2021)

Dive into Deep Learning. - Aston Zhang; Zachary C. Lipton; Mu Li 003; Alexander J. Smola (CoRR, 2021)

ResNeSt: Split-Attention Networks. - Hang Zhang 005; Chongruo Wu; Zhongyue Zhang; Yi Zhu 001; Haibin Lin; Zhi Zhang 005; Yue Sun; Tong He 002; Jonas Mueller 001; R. Manmatha; Mu Li 003; Alexander J. Smola (CVPR Workshops, 2022)

BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training. - Likun Cai; Zhi Zhang 005; Yi Zhu 001; Li Zhang 040; Mu Li 003; Xiangyang Xue 001 (CVPR Workshops, 2022)

Removing Batch Normalization Boosts Adversarial Training. - Haotao Wang; Aston Zhang; Shuai Zheng 004; Xingjian Shi; Mu Li 003; Zhangyang Wang (ICML, 2022)

Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition. - Haotao Wang; Aston Zhang; Yi Zhu 001; Shuai Zheng 004; Mu Li 003; Alex J. Smola; Zhangyang Wang (ICML, 2022)

Earthformer: Exploring Space-Time Transformers for Earth System Forecasting. - Zhihan Gao 001; Xingjian Shi; Hao Wang 014; Yi Zhu 001; Yuyang Wang 001; Mu Li 003; Dit-Yan Yeung (NeurIPS, 2022)

Visual Prompt Tuning for Test-time Domain Adaptation. - Yunhe Gao; Xingjian Shi; Yi Zhu 001; Hao Wang 014; Zhiqiang Tang 001; Xiong Zhou; Mu Li 003; Dimitris N. Metaxas (CoRR, 2022)

Are Multimodal Models Robust to Image and Text Perturbations? - Jielin Qiu; Yi Zhu 001; Xingjian Shi; Florian Wenzel; Zhiqiang Tang 001; Ding Zhao; Bo Li 026; Mu Li 003 (CoRR, 2022)

SPT: Semi-Parametric Prompt Tuning for Multitask Prompted Learning. - M. Saiful Bari; Aston Zhang; Shuai Zheng 004; Xingjian Shi; Yi Zhu 001; Shafiq Joty; Mu Li 003 (CoRR, 2022)

MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud. - Zhen Zhang 063; Shuai Zheng 004; Yida Wang 003; Justin Chiu; George Karypis; Trishul Chilimbi; Mu Li 003; Xin Jin 008 (Proc. VLDB Endow., 2022)

Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation. - Yuxin Ren; Zihan Zhong; Xingjian Shi; Yi Zhu 001; Chun Yuan; Mu Li 003 (ACL (1), 2023)

A Cheaper and Better Diffusion Language Model with Soft-Masked Noise. - Jiaao Chen; Aston Zhang; Mu Li 003; Alex Smola; Diyi Yang (EMNLP, 2023)

Automatic Chain of Thought Prompting in Large Language Models. - Zhuosheng Zhang 001; Aston Zhang; Mu Li 003; Alex Smola (ICLR, 2023)

Parameter-Efficient Fine-Tuning Design Spaces. - Jiaao Chen; Aston Zhang; Xingjian Shi; Mu Li 003; Alex Smola; Diyi Yang (ICLR, 2023)

Learning Multimodal Data Augmentation in Feature Space. - Zichang Liu; Zhiqiang Tang 001; Xingjian Shi; Aston Zhang; Mu Li 003; Anshumali Shrivastava; Andrew Gordon Wilson (ICLR, 2023)

AIM: Adapting Image Models for Efficient Video Action Recognition. - Taojiannan Yang; Yi Zhu 001; Yusheng Xie; Aston Zhang; Chen Chen 001; Mu Li 003 (ICLR, 2023)

XTab: Cross-table Pretraining for Tabular Transformers. - Bingzhao Zhu; Xingjian Shi; Nick Erickson; Mu Li 003; George Karypis; Mahsa Shoaran (ICML, 2023)

PreDiff: Precipitation Nowcasting with Latent Diffusion Models. - Zhihan Gao 001; Xingjian Shi; Boran Han; Hao Wang 014; Xiaoyong Jin; Danielle C. Maddix; Yi Zhu 001; Mu Li 003; Yuyang Wang 001 (NeurIPS, 2023)

Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition. - Shuhuai Ren; Aston Zhang; Yi Zhu 001; Shuai Zhang; Shuai Zheng 004; Mu Li 003; Alexander J. Smola; Xu Sun 001 (NeurIPS, 2023)

MixGen: A New Multi-Modal Data Augmentation. - Xiaoshuai Hao; Yi Zhu 001; Srikar Appalaraju; Aston Zhang; Wanqian Zhang; Bo Li 063; Mu Li 003 (WACV (Workshops), 2023)

GFM: Building Geospatial Foundation Models via Continual Pretraining. - Matías Mendieta; Boran Han; Xingjian Shi; Yi Zhu 001; Chen Chen 001; Mu Li 003 (CoRR, 2023)

LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation. - Jiaxin Cheng; Xiao Liang; Xingjian Shi; Tong He 002; Tianjun Xiao; Mu Li 003 (CoRR, 2023)

RAF: Holistic Compilation for Deep Learning Model Training. - Cody Hao Yu; Haozheng Fan; Guangtai Huang; Zhen Jia 001; Yizhi Liu; Jie Wang 022; Zach Zheng; Yuan Zhou; Haichen Shen; Junru Shao; Mu Li 003; Yida Wang 003 (CoRR, 2023)

What Makes for Good Tokenizers in Vision Transformer? - Shengju Qian; Yi Zhu 001; Wenbo Li 002; Mu Li 003; Jiaya Jia (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Multimodal Chain-of-Thought Reasoning in Language Models. - Zhuosheng Zhang 001; Aston Zhang; Mu Li 003; Hai Zhao 001; George Karypis; Alex Smola (Trans. Mach. Learn. Res., 2024)

Improving Semantic Segmentation via Efficient Self-Training. - Yi Zhu 001; Zhongyue Zhang; Chongruo Wu; Zhi Zhang 005; Tong He 002; Hang Zhang 005; R. Manmatha; Mu Li 003; Alexander J. Smola (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Online learning from capricious data streams via shared and new feature spaces. - Peng Zhou 008; Shuai Zhang; Mu Li 003; Yuanting Yan (Appl. Intell., 2024)

RPGBENCH: Evaluating Large Language Models as Role-Playing Game Engines. - Pengfei Yu; Dongming Shen; Silin Meng; Jaewon Lee; Weisu Yin; Andrea Yaoyun Cui; Zhenlin Xu; Yi Zhu 001; Xingjian Shi; Mu Li 003; Alex Smola (CoRR, 2025)

EmergentTTS-Eval: Evaluating TTS Models on Complex Prosodic, Expressiveness, and Linguistic Challenges Using Model-as-a-Judge. - Ruskin Raj Manku; Yuzhi Tang; Xingjian Shi; Mu Li 003; Alex Smola (CoRR, 2025)