Tianqi Chen

According to our database, Tianqi Chen authored at least 32 papers between 2014 and 2023.

Collaborative distances:

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

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

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

CoRR, 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 Machine Learning and Systems 2022, 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

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