Carbin, Michael

20 publications

NeurIPS 2025 FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents Nandan Thakur, Jimmy Lin, Sam Havens, Michael Carbin, Omar Khattab, Andrew Drozdov

ICML 2025 Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding Tian Jin, Ellie Y Cheng, Zachary Ankner, Nikunj Saunshi, Blake M Elias, Amir Yazdanbakhsh, Jonathan Ragan-Kelley, Suvinay Subramanian, Michael Carbin

ICLRW 2024 Expressing and Exploiting Parallelism in Language Model Decoding Tian Jin, Ellie Y Cheng, Michael Carbin

ICML 2024 Learning to Compile Programs to Neural Networks Logan Weber, Jesse Michel, Alex Renda, Michael Carbin

NeurIPSW 2024 Long Context RAG Performance of Large Language Models Quinn Leng, Jacob Portes, Sam Havens, Matei Zaharia, Michael Carbin

ICLR 2024 The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory Before In-Context Learning Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite

ICMLW 2023 Can LLMs Generate Random Numbers? Evaluating LLM Sampling in Controlled Domains Aspen K Hopkins, Alex Renda, Michael Carbin

AAAI 2023 Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable Cambridge Yang, Michael Littman, Michael Carbin

ICMLW 2023 Distributions for Compositionally Differentiating Parametric Discontinuities Jesse Michel, Kevin Mu, Xuanda Yang, Sai Praveen Bangaru, Elias Rojas Collins, Gilbert Bernstein, Jonathan Ragan-Kelley, Michael Carbin, Tzu-Mao Li

IJCAI 2022 On the (In)Tractability of Reinforcement Learning for LTL Objectives Cambridge Yang, Michael L. Littman, Michael Carbin

NeurIPS 2022 Pruning’s Effect on Generalization Through the Lens of Training and Regularization Tian Jin, Michael Carbin, Dan Roy, Jonathan Frankle, Gintare Karolina Dziugaite

ICML 2021 On the Predictability of Pruning Across Scales Jonathan S Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit

ICLR 2021 Pruning Neural Networks at Initialization: Why Are We Missing the Mark? Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, Michael Carbin

CVPR 2021 The Lottery Tickets Hypothesis for Supervised and Self-Supervised Pre-Training in Computer Vision Models Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang

ICLR 2020 Comparing Rewinding and Fine-Tuning in Neural Network Pruning Alex Renda, Jonathan Frankle, Michael Carbin

ICML 2020 Linear Mode Connectivity and the Lottery Ticket Hypothesis Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, Michael Carbin

NeurIPS 2020 The Lottery Ticket Hypothesis for Pre-Trained BERT Networks Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin

NeurIPS 2019 Compiler Auto-Vectorization with Imitation Learning Charith Mendis, Cambridge Yang, Yewen Pu, Dr.Saman Amarasinghe, Michael Carbin

ICML 2019 Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation Using Deep Neural Networks Charith Mendis, Alex Renda, Dr.Saman Amarasinghe, Michael Carbin

ICLR 2019 The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle, Michael Carbin