Daniel Israel*, Tian Jin*, Ellie Y. Cheng, Guy Van den Broeck, Aditya Grover, Suvinay Subramanian, Michael Carbin. "Planned Diffusion." International Conference on Learning Representations (ICLR), 2026.
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Tian Jin*, Ellie Y. Cheng*, Zack Ankner, Nikunj Saunshi, Blake M. Elias, Amir Yazdanbakhsh, Jonathan Ragan-Kelley, Suvinay Subramanian, Michael Carbin. "Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding." International Conference on Machine Learning (ICML), 2025.
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Tian Jin, Ahmed Imtiaz Humayun, Utku Evci, Suvinay Subramanian, Amir Yazdanbakhsh, Dan Alistarh, Gintare Karolina Dziugaite. "The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws." International Conference on Learning Representations (ICLR), 2025.
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Seth Lazar, Luke Thorburn, Tian Jin, Luca Belli. "The Moral Case for Using Language Model Agents for Recommendation." Inquiry: An Interdisciplinary Journal of Philosophy, 2025.
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Tian Jin*, Nolan Clement*, Xin Dong*, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite. "The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning." International Conference on Learning Representations (ICLR), 2024.
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Tian Jin, Zhenyu Xu, Sara Sharify, Xin Wang. "Self-Selected Attention Span for Accelerating Large Language Model Inference." arXiv Preprint, 2024.
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William Brandon, Aniruddha Nrusimha, Kevin Qian, Zachary Ankner, Tian Jin, Zhiye Song, Jonathan Ragan-Kelley. "Striped Attention: Faster Ring Attention for Causal Transformers." arXiv Preprint, 2023.
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Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, and Gintare Karolina Dziugaite. "Pruning's Effect on Generalization Through the Lens of Training and Regularization." Conference on Neural Information Processing Systems (NeurIPS), 2022.
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Zachary Ankner, Alex Renda, Gintare Karolina Dziugaite, Jonathan Frankle, Tian Jin. "The Effect of Data Dimensionality on Neural Network Prunability." NeurIPS "I Can't Believe It's Not Better" Workshop, 2022.
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Luke Anderson, Andrew Adams, Karima Ma, Tzu-Mao Li, Tian Jin, Jonathan Ragan-Kelley. "Efficient Automatic Scheduling of Imaging and Vision Pipelines for the GPU." International Conference on Object-Oriented Programming Systems, Languages, and Applications (OOPSLA), 2021.
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Tian Jin*, Zhun Liu*, Shengjia Yan, Alexandre Eichenberger, and Louis-Philippe Morency. "Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions." Annual Meeting of the Association for Computational Linguistics (ACL), 2020.
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Tian Jin, Gheorghe-Teodor Bercea, Tung D. Le, Tong Chen, Gong Su, Haruki Imai, Yasushi Negishi, Anh Leu, Kevin O'Brien, Kiyokuni Kawachiya, Alexandre E. Eichenberger. "Compiling ONNX Neural Network Models Using MLIR." arXiv Preprint, 2020.
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Tian Jin, Seokin Hong. "Split-CNN: Splitting Window-based Operations in Convolutional Neural Networks for Memory System Optimization." International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2019.
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Arpith Chacko Jacob, Alexandre E Eichenberger, Hyojin Sung, Samuel F Antao, Gheorghe-Teodor Bercea, Carlo Bertolli, Alexey Bataev, Tian Jin, Tong Chen, Zehra Sura, Georgios Rokos, and Kevin O'Brien. "Efficient Fork-Join on GPUs Through Warp Specialization." International Conference on High Performance Computing (HiPC), 2017.
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Samuel F. Antão, Alexey Bataev, Arpith C. Jacob, Gheorghe-Teodor Bercea, Alexandre E. Eichenberger, Georgios Rokos, Matt Martineau, Tian Jin, Guray Ozen, Zehra Sura, Tong Chen, Hyojin Sung, Carlo Bertolli, Kevin O'Brien. "Offloading Support for OpenMP in Clang and LLVM." Third Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC, held with SC), 2016.
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Matt Martineau, Simon McIntosh-Smith, Carlo Bertolli, Arpith C. Jacob, Samuel F. Antão, Alexandre E. Eichenberger, Gheorghe-Teodor Bercea, Tong Chen, Tian Jin, Kevin O'Brien, Georgios Rokos, Hyojin Sung, Zehra Sura. "Performance Analysis and Optimization of Clang's OpenMP 4.5 GPU Support." International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS, held with SC), 2016.
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Tian Jin, Nirmal Prajapati, Waruna Ranasinghe, Guillaume Iooss, Yun Zou, Sanjay Rajopadhye, and David G. Wonnacott. "Hybrid Static/Dynamic Schedules for Tiled Polyhedral Programs." arXiv Preprint, 2016.
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David Wonnacott, Tian Jin, and Allison Lake. "Automatic tiling of "mostly-tileable" loop nests." International Workshop on Polyhedral Compilation Techniques (IMPACT), 2015.
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Guest lecturer in High-Performance Machine Learning. New York University, 2019, 2020.