TensorFlow: A System for Large-Scale Machine Learning. - Martín Abadi; Paul Barham 001; Jianmin Chen; Zhifeng Chen; Andy Davis; Jeffrey Dean; Matthieu Devin; Sanjay Ghemawat; Geoffrey Irving; Michael Isard; Manjunath Kudlur; Josh Levenberg; et al. (OSDI, 2016)

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. - Martín Abadi; Ashish Agarwal; Paul Barham 001; Eugene Brevdo; Zhifeng Chen; Craig Citro; Gregory S. Corrado; Andy Davis; Jeffrey Dean; Matthieu Devin; Sanjay Ghemawat; Ian J. Goodfellow; et al. (CoRR, 2016)

TensorFlow: A system for large-scale machine learning. - Martín Abadi; Paul Barham 001; Jianmin Chen; Zhifeng Chen; Andy Davis; Jeffrey Dean; Matthieu Devin; Sanjay Ghemawat; Geoffrey Irving; Michael Isard; Manjunath Kudlur; Josh Levenberg; et al. (CoRR, 2016)

Device Placement Optimization with Reinforcement Learning. - Azalia Mirhoseini; Hieu Pham 001; Quoc V. Le; Benoit Steiner; Rasmus Larsen 002; Yuefeng Zhou; Naveen Kumar; Mohammad Norouzi 002; Samy Bengio; Jeff Dean (ICML, 2017)

A Hierarchical Model for Device Placement. - Azalia Mirhoseini; Anna Goldie; Hieu Pham 001; Benoit Steiner; Quoc V. Le; Jeff Dean (ICLR (Poster), 2018)

PyTorch: An Imperative Style, High-Performance Deep Learning Library. - Adam Paszke; Sam Gross; Francisco Massa; Adam Lerer; James Bradbury; Gregory Chanan; Trevor Killeen; Zeming Lin; Natalia Gimelshein; Luca Antiga; Alban Desmaison; Andreas Köpf; et al. (NeurIPS, 2019)

Learning to optimize halide with tree search and random programs. - Andrew Adams; Karima Ma; Luke Anderson 001; Riyadh Baghdadi; Tzu-Mao Li; Michaël Gharbi; Benoit Steiner; Steven Johnson; Kayvon Fatahalian; Frédo Durand; Jonathan Ragan-Kelley (ACM Trans. Graph., 2019)

Value Function Based Performance Optimization of Deep Learning Workloads. - Benoit Steiner; Chris Cummins; Horace He; Hugh Leather (CoRR, 2020)

Value Learning for Throughput Optimization of Deep Learning Workloads. - Benoit Steiner; Chris Cummins; Horace He; Hugh Leather (MLSys, 2021)

Learning Space Partitions for Path Planning. - Kevin Yang; Tianjun Zhang; Chris Cummins; Brandon Cui; Benoit Steiner; Linnan Wang; Joseph E. Gonzalez; Dan Klein 001; Yuandong Tian (NeurIPS, 2021)

Using Graph Neural Networks to model the performance of Deep Neural Networks. - Shikhar Singh; Benoit Steiner; James Hegarty; Hugh Leather (CoRR, 2021)

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research. - Chris Cummins; Bram Wasti; Jiadong Guo; Brandon Cui; Jason Ansel; Sahir Gomez; Somya Jain; Jia Liu; Olivier Teytaud; Benoit Steiner; Yuandong Tian; Hugh Leather (CGO, 2022)

Flashlight: Enabling Innovation in Tools for Machine Learning. - Jacob D. Kahn; Vineel Pratap; Tatiana Likhomanenko; Qiantong Xu; Awni Y. Hannun; Jeff Cai; Paden Tomasello; Ann Lee 001; Edouard Grave; Gilad Avidov; Benoit Steiner; Vitaliy Liptchinsky; et al. (ICML, 2022)

A graph neural network-based performance model for deep learning applications. - Shikhar Singh; James Hegarty; Hugh Leather; Benoit Steiner (MAPS@PLDI, 2022)

LoopStack: a Lightweight Tensor Algebra Compiler Stack. - Bram Wasti; José Pablo Cambronero; Benoit Steiner; Hugh Leather; Aleksandar Zlateski (CoRR, 2022)

OLLA: Decreasing the Memory Usage of Neural Networks by Optimizing the Lifetime and Location of Arrays. - Benoit Steiner; Mostafa Elhoushi; Jacob Kahn; James Hegarty (CoRR, 2022)

Local Branching Relaxation Heuristics for Integer Linear Programs. - Taoan Huang; Aaron M. Ferber; Yuandong Tian; Bistra Dilkina; Benoit Steiner (CPAIOR, 2023)

SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems. - Aaron M. Ferber; Taoan Huang; Daochen Zha; Martin Schubert; Benoit Steiner; Bistra Dilkina; Yuandong Tian (ICML, 2023)

Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning. - Taoan Huang; Aaron M. Ferber; Yuandong Tian; Bistra Dilkina; Benoit Steiner (ICML, 2023)

Learning Compiler Pass Orders using Coreset and Normalized Value Prediction. - Youwei Liang; Kevin Stone; Ali Shameli; Chris Cummins; Mostafa Elhoushi; Jiadong Guo; Benoit Steiner; Xiaomeng Yang; Pengtao Xie; Hugh James Leather; Yuandong Tian (ICML, 2023)

MODeL: Memory Optimizations for Deep Learning. - Benoit Steiner; Mostafa Elhoushi; Jacob Kahn; James Hegarty (ICML, 2023)

Measuring Faithfulness in Chain-of-Thought Reasoning. - Tamera Lanham; Anna Chen; Ansh Radhakrishnan; Benoit Steiner; Carson Denison; Danny Hernandez; Dustin Li; Esin Durmus; Evan Hubinger; Jackson Kernion; Kamile Lukosiute; Karina Nguyen; et al. (CoRR, 2023)

Studying Large Language Model Generalization with Influence Functions. - Roger B. Grosse; Juhan Bae; Cem Anil; Nelson Elhage; Alex Tamkin; Amirhossein Tajdini; Benoit Steiner; Dustin Li; Esin Durmus; Ethan Perez; Evan Hubinger; Kamile Lukosiute; et al. (CoRR, 2023)