Tianqi Chen

According to our database, Tianqi Chen authored at least 51 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism.

Byungsoo Jeon, Mengdi Wu, Shiyi Cao, Sunghyun Kim, Sunghyun Park, Neeraj Aggarwal, Colin Unger, Daiyaan Arfeen, Peiyuan Liao, Xupeng Miao, Mohammad Alizadeh, Gregory R. Ganger, Tianqi Chen, Zhihao Jia

CoRR, 2024

Emerging Platforms Meet Emerging LLMs: A Year-Long Journey of Top-Down Development.

Siyuan Feng, Jiawei Liu, Ruihang Lai, Charlie F. Ruan, Yong Yu, Lingming Zhang, Tianqi Chen

CoRR, 2024

Atom: Low-Bit Quantization for Efficient and Accurate LLM Serving.

Yilong Zhao, Chien-Yu Lin, Kan Zhu, Zihao Ye, Lequn Chen, Size Zheng, Luis Ceze, Arvind Krishnamurthy, Tianqi Chen, Baris Kasikci

Proceedings of the Seventh Annual Conference on Machine Learning and Systems, 2024

ACROBAT: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time.

Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

Proceedings of the Seventh Annual Conference on Machine Learning and Systems, 2024

2023

Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems.

Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Xinhao Cheng, Hongyi Jin, Tianqi Chen, Zhihao Jia

CoRR, 2023

Relax: Composable Abstractions for End-to-End Dynamic Machine Learning.

Ruihang Lai, Junru Shao, Siyuan Feng, Steven S. Lyubomirsky, Bohan Hou, Wuwei Lin, Zihao Ye, Hongyi Jin, Yuchen Jin, Jiawei Liu, Lesheng Jin, Yaxing Cai, Ziheng Jiang, Yong Wu, Sunghyun Park, Prakalp Srivastava, Jared G. Roesch, Todd C. Mowry, Tianqi Chen

CoRR, 2023

ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines.

Siyuan Chen, Pratik Pramod Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

Proceedings of the International Conference on Machine Learning, 2023

SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning.

Zihao Ye, Ruihang Lai, Junru Shao, Tianqi Chen, Luis Ceze

Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2023

TensorIR: An Abstraction for Automatic Tensorized Program Optimization.

Siyuan Feng, Bohan Hou, Hongyi Jin, Wuwei Lin, Junru Shao, Ruihang Lai, Zihao Ye, Lianmin Zheng, Cody Hao Yu, Yong Yu, Tianqi Chen

Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2023

2022

Tensor Program Optimization with Probabilistic Programs.

Junru Shao, Xiyou Zhou, Siyuan Feng, Bohan Hou, Ruihang Lai, Hongyi Jin, Wuwei Lin, Masahiro Masuda, Cody Hao Yu, Tianqi Chen

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

DietCode: Automatic Optimization for Dynamic Tensor Programs.

Bojian Zheng, Ziheng Jiang, Cody Hao Yu, Haichen Shen, Joshua Fromm, Yizhi Liu, Yida Wang, Luis Ceze, Tianqi Chen, Gennady Pekhimenko

Proceedings of the Fifth Conference on Machine Learning and Systems, 2022

The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding.

Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

Proceedings of the Fifth Conference on Machine Learning and Systems, 2022

Collage: Seamless Integration of Deep Learning Backends with Automatic Placement.

Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia

Proceedings of the International Conference on Parallel Architectures and Compilation Techniques, 2022

2021

Collage: Automated Integration of Deep Learning Backends.

Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia

CoRR, 2021

Automated Backend-Aware Post-Training Quantization.

Ziheng Jiang, Animesh Jain, Andrew Liu, Josh Fromm, Chengqian Ma, Tianqi Chen, Luis Ceze

CoRR, 2021

TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers.

Lianmin Zheng, Ruochen Liu, Junru Shao, Tianqi Chen, Joseph Gonzalez, Ion Stoica, Ameer Haj-Ali

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Cortex: A Compiler for Recursive Deep Learning Models.

Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

Dynamic Tensor Rematerialization.

Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, Zachary Tatlock

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Automatic generation of high-performance quantized machine learning kernels.

Meghan Cowan, Thierry Moreau, Tianqi Chen, James Bornholt, Luis Ceze

Proceedings of the CGO '20: 18th ACM/IEEE International Symposium on Code Generation and Optimization, 2020

2019

Scalable and Intelligent Learning Systems.

Tianqi Chen

PhD thesis, 2019

Irreversible samplers from jump and continuous Markov processes.

Yi-An Ma, Emily B. Fox, Tianqi Chen, Lei Wu

Stat. Comput., 2019

A Hardware-Software Blueprint for Flexible Deep Learning Specialization.

Thierry Moreau, Tianqi Chen, Luis Vega, Jared Roesch, Eddie Q. Yan, Lianmin Zheng, Josh Fromm, Ziheng Jiang, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

IEEE Micro, 2019

Relay: A High-Level IR for Deep Learning.

Jared Roesch, Steven Lyubomirsky, Marisa Kirisame, Josh Pollock, Logan Weber, Ziheng Jiang, Tianqi Chen, Thierry Moreau, Zachary Tatlock

CoRR, 2019

2018

ADARES: Adaptive Resource Management for Virtual Machines.

Ignacio Cano, Lequn Chen, Pedro Fonseca, Tianqi Chen, Chern Cheah, Karan Gupta, Ramesh Chandra, Arvind Krishnamurthy

CoRR, 2018

Automating Generation of Low Precision Deep Learning Operators.

Meghan Cowan, Thierry Moreau, Tianqi Chen, Luis Ceze

CoRR, 2018

VTA: An Open Hardware-Software Stack for Deep Learning.

Thierry Moreau, Tianqi Chen, Ziheng Jiang, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

CoRR, 2018

TVM: End-to-End Optimization Stack for Deep Learning.

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Q. Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

CoRR, 2018

Relay: a new IR for machine learning frameworks.

Jared Roesch, Steven Lyubomirsky, Logan Weber, Josh Pollock, Marisa Kirisame, Tianqi Chen, Zachary Tatlock

Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, 2018

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning.

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Q. Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, 2018

Learning to Optimize Tensor Programs.

Tianqi Chen, Lianmin Zheng, Eddie Q. Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Optimizing Deep Learning Workloads on ARM GPU with TVM.

Lanmin Zheng, Tianqi Chen

Proceedings of the 1st on Reproducible Quality-Efficient Systems Tournament on Co-designing Pareto-efficient Deep Learning, 2018

Leveraging the VTA-TVM Hardware-Software Stack for FPGA Acceleration of 8-bit ResNet-18 Inference.

Thierry Moreau, Tianqi Chen, Luis Ceze

Proceedings of the 1st on Reproducible Quality-Efficient Systems Tournament on Co-designing Pareto-efficient Deep Learning, 2018

PANEL: Open panel and discussion on tackling complexity, reproducibility and tech transfer challenges in a rapidly evolving AI/ML/systems research.

Grigori Fursin, Thierry Moreau, Hillery C. Hunter, Yiran Chen, Charles Qi, Tianqi Chen

Proceedings of the 1st on Reproducible Quality-Efficient Systems Tournament on Co-designing Pareto-efficient Deep Learning, 2018

2017

An end to end IR stack for deep learning systems.

Tianqi Chen

Proceedings of the Workshop on Trends in Machine-Learning (and impact on computer architecture), 2017

2016

Training Deep Nets with Sublinear Memory Cost.

Tianqi Chen, Bing Xu, Chiyuan Zhang, Carlos Guestrin

CoRR, 2016

XGBoost: A Scalable Tree Boosting System.

Tianqi Chen, Carlos Guestrin

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015

A Complete Recipe for Stochastic Gradient MCMC.

Yi-An Ma, Tianqi Chen, Emily B. Fox

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Efficient Second-Order Gradient Boosting for Conditional Random Fields.

Tianqi Chen, Sameer Singh, Ben Taskar, Carlos Guestrin

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Stochastic Gradient Hamiltonian Monte Carlo.

Tianqi Chen, Emily B. Fox, Carlos Guestrin

Proceedings of the 31th International Conference on Machine Learning, 2014

A Parallel and Efficient Algorithm for Learning to Match.

Jingbo Shang, Tianqi Chen, Hang Li, Zhengdong Lu, Yong Yu

Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

2013

Optimizing top-n collaborative filtering via dynamic negative item sampling.

Weinan Zhang, Tianqi Chen, Jun Wang, Yong Yu

Proceedings of the 36th International ACM SIGIR conference on research and development in Information Retrieval, 2013

General Functional Matrix Factorization Using Gradient Boosting.

Tianqi Chen, Hang Li, Qiang Yang, Yong Yu

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Rating Prediction with Informative Ensemble of Multi-Resolution Dynamic Models.

Zhao Zheng, Tianqi Chen, Nathan Nan Liu, Qiang Yang, Yong Yu

Proceedings of KDD Cup 2011 competition, San Diego, CA, USA, 2011, 2012

SVDFeature: a toolkit for feature-based collaborative filtering.

Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu

J. Mach. Learn. Res., 2012

Serendipitous Personalized Ranking for Top-N Recommendation.

Qiuxia Lu, Tianqi Chen, Weinan Zhang, Diyi Yang, Yong Yu

Proceedings of the 2012 IEEE/WIC/ACM International Conferences on Web Intelligence, 2012

Collaborative filtering with short term preferences mining.

Diyi Yang, Tianqi Chen, Weinan Zhang, Yong Yu

Proceedings of the 35th International ACM SIGIR conference on research and development in Information Retrieval, 2012

Collaborative personalized tweet recommendation.

Kailong Chen, Tianqi Chen, Guoqing Zheng, Ou Jin, Enpeng Yao, Yong Yu

Proceedings of the 35th International ACM SIGIR conference on research and development in Information Retrieval, 2012

Local implicit feedback mining for music recommendation.

Diyi Yang, Tianqi Chen, Weinan Zhang, Qiuxia Lu, Yong Yu

Proceedings of the Sixth ACM Conference on Recommender Systems, 2012

Discriminative Factor Alignment across Heterogeneous Feature Space.

Fangwei Hu, Tianqi Chen, Nathan Nan Liu, Qiang Yang, Yong Yu

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Feature Based Informative Model for Discriminating Favorite Items from Unrated Ones.

Bing Cheng, Tianqi Chen, Diyi Yang, Weinan Zhang, Yongqiang Wang, Yong Yu

Proceedings of the Web Technologies and Applications - 14th Asia-Pacific Web Conference, 2012

2011

Feature-Based Matrix Factorization

Tianqi Chen, Zhao Zheng, Qiuxia Lu, Weinan Zhang, Yong Yu

CoRR, 2011