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Tianqi Chen 0001
Person information
- affiliation: Carnegie Mellon University, USA
- affiliation (PhD 2019): University of Washington, USA
Other persons with the same name
- Tianqi Chen (aka: Tian-Qi Chen) — disambiguation page
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2020 – today
- 2024
- [c34]Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
ACROBAT: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time. MLSys 2024 - [c33]Yilong Zhao, Chien-Yu Lin, Kan Zhu, Zihao Ye, Lequn Chen, Size Zheng, Luis Ceze, Arvind Krishnamurthy, Tianqi Chen, Baris Kasikci:
Atom: Low-Bit Quantization for Efficient and Accurate LLM Serving. MLSys 2024 - [i27]Siyuan Feng, Jiawei Liu, Ruihang Lai, Charlie F. Ruan, Yong Yu, Lingming Zhang, Tianqi Chen:
Emerging Platforms Meet Emerging LLMs: A Year-Long Journey of Top-Down Development. CoRR abs/2404.09151 (2024) - [i26]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:
GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism. CoRR abs/2406.17145 (2024) - 2023
- [c32]Zihao Ye
, Ruihang Lai, Junru Shao, Tianqi Chen, Luis Ceze:
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning. ASPLOS (3) 2023: 660-678 - [c31]Siyuan Feng, Bohan Hou, Hongyi Jin, Wuwei Lin, Junru Shao, Ruihang Lai, Zihao Ye
TensorIR: An Abstraction for Automatic Tensorized Program Optimization. ASPLOS (2) 2023: 804-817 - [c30]Siyuan Chen, Pratik Pramod Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines. ICML 2023: 4514-4528 - [e1]Dawn Song, Michael Carbin, Tianqi Chen:
Proceedings of the Sixth Conference on Machine Learning and Systems, MLSys 2023, Miami, FL, USA, June 4-8, 2023. mlsys.org 2023 [contents] - [i25]Siyuan Chen, Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines. CoRR abs/2302.03851 (2023) - [i24]Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time. CoRR abs/2305.10611 (2023) - [i23]Yilong Zhao, Chien-Yu Lin, Kan Zhu, Zihao Ye, Lequn Chen, Size Zheng, Luis Ceze, Arvind Krishnamurthy, Tianqi Chen, Baris Kasikci:
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving. CoRR abs/2310.19102 (2023) - [i22]Ruihang Lai, Junru Shao, Siyuan Feng, Steven S. Lyubomirsky, Bohan Hou, Wuwei Lin, Zihao Ye, Hongyi Jin, Yuchen Jin, Jiawei Liu
Relax: Composable Abstractions for End-to-End Dynamic Machine Learning. CoRR abs/2311.02103 (2023) - [i21]Xupeng Miao, Gabriele Oliaro
Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems. CoRR abs/2312.15234 (2023) - 2022
- [c29]Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia:
Collage: Seamless Integration of Deep Learning Backends with Automatic Placement. PACT 2022: 517-529 - [c28]Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding. MLSys 2022 - [c27]Bojian Zheng, Ziheng Jiang, Cody Hao Yu, Haichen Shen, Joshua Fromm, Yizhi Liu, Yida Wang, Luis Ceze, Tianqi Chen, Gennady Pekhimenko:
DietCode: Automatic Optimization for Dynamic Tensor Programs. MLSys 2022 - [c26]Junru Shao, Xiyou Zhou, Siyuan Feng, Bohan Hou, Ruihang Lai, Hongyi Jin, Wuwei Lin, Masahiro Masuda, Cody Hao Yu, Tianqi Chen:
Tensor Program Optimization with Probabilistic Programs. NeurIPS 2022 - [i20]Junru Shao, Xiyou Zhou, Siyuan Feng, Bohan Hou, Ruihang Lai, Hongyi Jin, Wuwei Lin, Masahiro Masuda, Cody Hao Yu, Tianqi Chen:
Tensor Program Optimization with Probabilistic Programs. CoRR abs/2205.13603 (2022) - [i19]Siyuan Feng, Bohan Hou, Hongyi Jin, Wuwei Lin, Junru Shao, Ruihang Lai, Zihao Ye, Lianmin Zheng, Cody Hao Yu, Yong Yu, Tianqi Chen:
TensorIR: An Abstraction for Automatic Tensorized Program Optimization. CoRR abs/2207.04296 (2022) - [i18]Zihao Ye, Ruihang Lai, Junru Shao, Tianqi Chen, Luis Ceze:
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning. CoRR abs/2207.04606 (2022) - 2021
- [c25]Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, Zachary Tatlock:
Dynamic Tensor Rematerialization. ICLR 2021 - [c24]Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
Cortex: A Compiler for Recursive Deep Learning Models. MLSys 2021 - [c23]Lianmin Zheng, Ruochen Liu, Junru Shao, Tianqi Chen, Joseph Gonzalez, Ion Stoica, Ameer Haj-Ali:
TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers. NeurIPS Datasets and Benchmarks 2021 - [i17]Ziheng Jiang, Animesh Jain, Andrew Liu, Josh Fromm, Chengqian Ma, Tianqi Chen, Luis Ceze:
Automated Backend-Aware Post-Training Quantization. CoRR abs/2103.14949 (2021) - [i16]Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding. CoRR abs/2110.10221 (2021) - [i15]Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia:
Collage: Automated Integration of Deep Learning Backends. CoRR abs/2111.00655 (2021) - 2020
- [c22]Meghan Cowan, Thierry Moreau, Tianqi Chen, James Bornholt, Luis Ceze:
Automatic generation of high-performance quantized machine learning kernels. CGO 2020: 305-316 - [i14]Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, Zachary Tatlock:
Dynamic Tensor Rematerialization. CoRR abs/2006.09616 (2020) - [i13]Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
Cortex: A Compiler for Recursive Deep Learning Models. CoRR abs/2011.01383 (2020)
2010 – 2019
- 2019
- [b1]Tianqi Chen:
Scalable and Intelligent Learning Systems. University of Washington, USA, 2019 - [j3]Thierry Moreau, Tianqi Chen, Luis Vega, Jared Roesch, Eddie Q. Yan, Lianmin Zheng, Josh Fromm, Ziheng Jiang, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy:
A Hardware-Software Blueprint for Flexible Deep Learning Specialization. IEEE Micro 39(5): 8-16 (2019) - [j2]Yi-An Ma
Irreversible samplers from jump and continuous Markov processes. Stat. Comput. 29(1): 177-202 (2019) - [i12]Jared Roesch, Steven Lyubomirsky, Marisa Kirisame, Josh Pollock, Logan Weber, Ziheng Jiang, Tianqi Chen, Thierry Moreau, Zachary Tatlock:
Relay: A High-Level IR for Deep Learning. CoRR abs/1904.08368 (2019) - 2018
- [c21]Lanmin Zheng, Tianqi Chen:
Optimizing Deep Learning Workloads on ARM GPU with TVM. ReQuEST@ASPLOS 2018: 3 - [c20]Thierry Moreau, Tianqi Chen, Luis Ceze:
Leveraging the VTA-TVM Hardware-Software Stack for FPGA Acceleration of 8-bit ResNet-18 Inference. ReQuEST@ASPLOS 2018: 5 - [c19]Grigori Fursin, Thierry Moreau, Hillery C. Hunter, Yiran Chen, Charles Qi, Tianqi Chen:
PANEL: Open panel and discussion on tackling complexity, reproducibility and tech transfer challenges in a rapidly evolving AI/ML/systems research. ReQuEST@ASPLOS 2018: 7 - [c18]Tianqi Chen, Lianmin Zheng, Eddie Q. Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy:
Learning to Optimize Tensor Programs. NeurIPS 2018: 3393-3404 - [c17]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
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. OSDI 2018: 578-594 - [c16]Jared Roesch, Steven Lyubomirsky
Relay: a new IR for machine learning frameworks. MAPL@PLDI 2018: 58-68 - [i11]Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Q. Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy:
TVM: End-to-End Optimization Stack for Deep Learning. CoRR abs/1802.04799 (2018) - [i10]Tianqi Chen, Lianmin Zheng, Eddie Q. Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy:
Learning to Optimize Tensor Programs. CoRR abs/1805.08166 (2018) - [i9]Thierry Moreau, Tianqi Chen, Ziheng Jiang, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy:
VTA: An Open Hardware-Software Stack for Deep Learning. CoRR abs/1807.04188 (2018) - [i8]Jared Roesch, Steven Lyubomirsky, Logan Weber, Josh Pollock, Marisa Kirisame, Tianqi Chen, Zachary Tatlock:
Relay: A New IR for Machine Learning Frameworks. CoRR abs/1810.00952 (2018) - [i7]Meghan Cowan, Thierry Moreau, Tianqi Chen, Luis Ceze:
Automating Generation of Low Precision Deep Learning Operators. CoRR abs/1810.11066 (2018) - [i6]Ignacio Cano, Lequn Chen, Pedro Fonseca, Tianqi Chen, Chern Cheah, Karan Gupta, Ramesh Chandra, Arvind Krishnamurthy:
ADARES: Adaptive Resource Management for Virtual Machines. CoRR abs/1812.01837 (2018) - 2017
- [c15]Tianqi Chen:
An end to end IR stack for deep learning systems. TIML@ISCA 2017: 8 - 2016
- [c14]Tianqi Chen, Carlos Guestrin:
XGBoost: A Scalable Tree Boosting System. KDD 2016: 785-794 - [i5]Tianqi Chen, Carlos Guestrin:
XGBoost: A Scalable Tree Boosting System. CoRR abs/1603.02754 (2016) - [i4]Tianqi Chen, Bing Xu, Chiyuan Zhang, Carlos Guestrin:
Training Deep Nets with Sublinear Memory Cost. CoRR abs/1604.06174 (2016) - 2015
- [c13]Tianqi Chen, Sameer Singh, Ben Taskar, Carlos Guestrin:
Efficient Second-Order Gradient Boosting for Conditional Random Fields. AISTATS 2015 - [c12]Yi-An Ma, Tianqi Chen, Emily B. Fox:
A Complete Recipe for Stochastic Gradient MCMC. NIPS 2015: 2917-2925 - 2014
- [c11]Jingbo Shang, Tianqi Chen, Hang Li, Zhengdong Lu, Yong Yu:
A Parallel and Efficient Algorithm for Learning to Match. ICDM 2014: 971-976 - [c10]Tianqi Chen, Emily B. Fox, Carlos Guestrin:
Stochastic Gradient Hamiltonian Monte Carlo. ICML 2014: 1683-1691 - [i3]Tianqi Chen, Emily B. Fox, Carlos Guestrin:
Stochastic Gradient Hamiltonian Monte Carlo. CoRR abs/1402.4102 (2014) - [i2]Jingbo Shang, Tianqi Chen, Hang Li, Zhengdong Lu, Yong Yu:
A Parallel and Efficient Algorithm for Learning to Match. CoRR abs/1410.6414 (2014) - 2013
- [c9]Tianqi Chen, Hang Li, Qiang Yang, Yong Yu:
General Functional Matrix Factorization Using Gradient Boosting. ICML (1) 2013: 436-444 - [c8]Weinan Zhang, Tianqi Chen, Jun Wang, Yong Yu:
Optimizing top-n collaborative filtering via dynamic negative item sampling. SIGIR 2013: 785-788 - 2012
- [j1]Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu:
SVDFeature: a toolkit for feature-based collaborative filtering. J. Mach. Learn. Res. 13: 3619-3622 (2012) - [c7]Bing Cheng, Tianqi Chen, Diyi Yang, Weinan Zhang, Yongqiang Wang, Yong Yu:
Feature Based Informative Model for Discriminating Favorite Items from Unrated Ones. APWeb 2012: 146-157 - [c6]Fangwei Hu, Tianqi Chen, Nathan Nan Liu, Qiang Yang, Yong Yu:
Discriminative Factor Alignment across Heterogeneous Feature Space. ECML/PKDD (2) 2012: 757-772 - [c5]Diyi Yang, Tianqi Chen, Weinan Zhang, Qiuxia Lu, Yong Yu:
Local implicit feedback mining for music recommendation. RecSys 2012: 91-98 - [c4]Kailong Chen, Tianqi Chen, Guoqing Zheng, Ou Jin, Enpeng Yao, Yong Yu:
Collaborative personalized tweet recommendation. SIGIR 2012: 661-670 - [c3]Diyi Yang, Tianqi Chen, Weinan Zhang, Yong Yu:
Collaborative filtering with short term preferences mining. SIGIR 2012: 1043-1044 - [c2]Qiuxia Lu, Tianqi Chen, Weinan Zhang, Diyi Yang, Yong Yu:
Serendipitous Personalized Ranking for Top-N Recommendation. Web Intelligence 2012: 258-265 - [c1]Zhao Zheng, Tianqi Chen, Nathan Nan Liu, Qiang Yang, Yong Yu:
Rating Prediction with Informative Ensemble of Multi-Resolution Dynamic Models. KDD Cup 2012: 75-97 - 2011
- [i1]Tianqi Chen, Zhao Zheng, Qiuxia Lu, Weinan Zhang, Yong Yu:
Feature-Based Matrix Factorization. CoRR abs/1109.2271 (2011)
Coauthor Index
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