Michael Carbin

107 papers A* 24A 5B 3C 1Journal 65Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents. CoRR Nandan Thakur, Jimmy Lin, Sam Havens, Michael Carbin, Omar Khattab, Andrew Drozdov
2025	J	`jnl`	Inference Plans for Hybrid Particle Filtering. Proc. ACM Program. Lang. Ellie Y. Cheng, Eric Atkinson, Guillaume Baudart, Louis Mandel, Michael Carbin
2025	A*	`conf`	Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding. ICML Tian Jin, Ellie Y. Cheng, Zachary Ankner, Nikunj Saunshi, Blake M. Elias, Amir Yazdanbakhsh, Jonathan Ragan-Kelley, Suvinay Subramanian, Michael Carbin
2025	J	`jnl`	Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding. CoRR Tian Jin, Ellie Y. Cheng, Zachary Ankner, Nikunj Saunshi, Blake M. Elias, Amir Yazdanbakhsh, Jonathan Ragan-Kelley, Suvinay Subramanian, Michael Carbin
2025	J	`jnl`	Planned Diffusion. CoRR Daniel Israel, Tian Jin, Ellie Y. Cheng, Guy Van den Broeck, Aditya Grover, Suvinay Subramanian, Michael Carbin
2025	J	`jnl`	Retrieval Capabilities of Large Language Models Scale with Pretraining FLOPs. CoRR Jacob P. Portes, Connor Jennings, Erica Ji Yuen, Sasha Doubov, Michael Carbin
2025	J	`jnl`	Sharing State Between Prompts and Programs. CoRR Ellie Y. Cheng, Logan Weber, Tian Jin, Michael Carbin
2024	J	`jnl`	BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text. CoRR Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, Christopher D. Manning
2024	J	`jnl`	Distributions for Compositionally Differentiating Parametric Discontinuities. Proc. ACM Program. Lang. Jesse Michel, Kevin Mu, Xuanda Yang, Sai Praveen Bangaru, Elias Rojas Collins, Gilbert Bernstein, Jonathan Ragan-Kelley, Michael Carbin, Tzu-Mao Li
2024	J	`jnl`	Drowning in Documents: Consequences of Scaling Reranker Inference. CoRR Mathew Jacob, Erik Lindgren, Matei Zaharia, Michael Carbin, Omar Khattab, Andrew Drozdov
2024	J	`jnl`	Inference Plans for Hybrid Particle Filtering. CoRR Ellie Y. Cheng, Eric Atkinson, Guillaume Baudart, Louis Mandel, Michael Carbin
2024	A*	`conf`	Learning to Compile Programs to Neural Networks. ICML Logan Weber, Jesse Michel, Alex Renda, Michael Carbin
2024	J	`jnl`	Learning to Compile Programs to Neural Networks. CoRR Logan Weber, Jesse Michel, Alex Renda, Michael Carbin
2024	J	`jnl`	Long Context RAG Performance of Large Language Models. CoRR Quinn Leng, Jacob P. Portes, Sam Havens, Matei Zaharia, Michael Carbin
2024	J	`jnl`	Quantum Control Machine: The Limits of Control Flow in Quantum Programming. Proc. ACM Program. Lang. Charles Yuan, Agnes Villanyi, Michael Carbin
2024	A*	`conf`	The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning. ICLR Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite
2024	J	`jnl`	The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation. Proc. ACM Program. Lang. Charles Yuan, Michael Carbin
2023	A*	`conf`	Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable. AAAI Cambridge Yang, Michael Littman, Michael Carbin
2023	J	`jnl`	Computably Continuous Reinforcement-Learning Objectives are PAC-learnable. CoRR Cambridge Yang, Michael Littman, Michael Carbin
2023	—	`ed.`	Proceedings of the Sixth Conference on Machine Learning and Systems, MLSys 2023, Miami, FL, USA, June 4-8, 2023 MLSys Dawn Song, Michael Carbin, Tianqi Chen
2023	J	`jnl`	Quantum Control Machine: The Limits of Quantum Programs as Data. CoRR Charles Yuan, Agnes Villanyi, Michael Carbin
2023	J	`jnl`	The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning. CoRR Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite
2023	J	`jnl`	The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation. CoRR Charles Yuan, Michael Carbin
2023	J	`jnl`	Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs. CoRR Alex Renda, Yi Ding, Michael Carbin
2023	J	`jnl`	Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs. Proc. ACM Program. Lang. Alex Renda, Yi Ding, Michael Carbin
2023	J	`jnl`	Verifying Performance Properties of Probabilistic Inference. CoRR Eric Atkinson, Ellie Y. Cheng, Guillaume Baudart, Louis Mandel, Michael Carbin
2022	J	`jnl`	Acela: Predictable Datacenter-level Maintenance Job Scheduling. CoRR Yi Ding, Aijia Gao, Thibaud Ryden, Kaushik Mitra, Sukumar Kalmanje, Yanai Golany, Michael Carbin, Henry Hoffmann
2022	J	`jnl`	Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression. CoRR Yi Ding, Alex Renda, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann
2022	A*	`conf`	On the (In)Tractability of Reinforcement Learning for LTL Objectives. IJCAI Cambridge Yang, Michael L. Littman, Michael Carbin
2022	A*	`conf`	Pruning's Effect on Generalization Through the Lens of Training and Regularization. NeurIPS Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite
2022	J	`jnl`	Pruning's Effect on Generalization Through the Lens of Training and Regularization. CoRR Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite
2022	J	`jnl`	SCOPE: Safe Exploration for Dynamic Computer Systems Optimization. CoRR Hyunji Kim, Ahsan Pervaiz, Henry Hoffmann, Michael Carbin, Yi Ding
2022	J	`jnl`	Semi-Symbolic Inference for Efficient Streaming Probabilistic Programming. CoRR Eric Atkinson, Charles Yuan, Guillaume Baudart, Louis Mandel, Michael Carbin
2022	J	`jnl`	Semi-symbolic inference for efficient streaming probabilistic programming. Proc. ACM Program. Lang. Eric Atkinson, Charles Yuan, Guillaume Baudart, Louis Mandel, Michael Carbin
2022	J	`jnl`	Tower: Data Structures in Quantum Superposition. CoRR Charles Yuan, Michael Carbin
2022	J	`jnl`	Tower: data structures in Quantum superposition. Proc. ACM Program. Lang. Charles Yuan, Michael Carbin
2022	J	`jnl`	Twist: Sound Reasoning for Purity and Entanglement in Quantum Programs. CoRR Charles Yuan, Christopher McNally, Michael Carbin
2022	J	`jnl`	Twist: sound reasoning for purity and entanglement in Quantum programs. Proc. ACM Program. Lang. Charles Yuan, Christopher McNally, Michael Carbin
2021	J	`jnl`	Exploiting Errors for Efficiency: A Survey from Circuits to Applications. ACM Comput. Surv. Phillip Stanley-Marbell, Armin Alaghi, Michael Carbin, Eva Darulova, Lara Dolecek, Andreas Gerstlauer, Ghayoor Gillani, Djordje Jevdjic, Thierry Moreau, Mattia Cacciotti, Alexandros Daglis, Natalie D. Enright Jerger, Babak Falsafi, Sasa Misailovic, Adrian Sampson, Damien Zufferey
2021	—	`conf`	Generalizable and interpretable learning for configuration extrapolation. ESEC/SIGSOFT FSE Yi Ding, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann
2021	A*	`conf`	On the Predictability of Pruning Across Scales. ICML Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit
2021	J	`jnl`	Programming and Reasoning with Partial Observability. CoRR Eric Atkinson, Michael Carbin
2021	J	`jnl`	Programming with Neural Surrogates of Programs. CoRR Alex Renda, Yi Ding, Michael Carbin
2021	—	`conf`	Programming with neural surrogates of programs. Onward! Alex Renda, Yi Ding, Michael Carbin
2021	A*	`conf`	Pruning Neural Networks at Initialization: Why Are We Missing the Mark? ICLR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2021	J	`jnl`	Reinforcement Learning for General LTL Objectives Is Intractable. CoRR Cambridge Yang, Michael L. Littman, Michael Carbin
2021	J	`jnl`	Simplifying dependent reductions in the polyhedral model. Proc. ACM Program. Lang. Cambridge Yang, Eric Atkinson, Michael Carbin
2021	J	`jnl`	Statically Bounded-Memory Delayed Sampling for Probabilistic Streams. CoRR Eric Atkinson, Guillaume Baudart, Louis Mandel, Charles Yuan, Michael Carbin
2021	J	`jnl`	Statically bounded-memory delayed sampling for probabilistic streams. Proc. ACM Program. Lang. Eric Atkinson, Guillaume Baudart, Louis Mandel, Charles Yuan, Michael Carbin
2021	J	`jnl`	Studying the Consistency and Composability of Lottery Ticket Pruning Masks. CoRR Rajiv Movva, Jonathan Frankle, Michael Carbin
2021	A*	`conf`	The Lottery Tickets Hypothesis for Supervised and Self-Supervised Pre-Training in Computer Vision Models. CVPR Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang
2021	A*	`conf`	VeGen: a vectorizer generator for SIMD and beyond. ASPLOS Yishen Chen, Charith Mendis, Michael Carbin, Saman P. Amarasinghe
2021	J	`jnl`	𝜆ₛ: computable semantics for differentiable programming with higher-order functions and datatypes. Proc. ACM Program. Lang. Benjamin Sherman, Jesse Michel, Michael Carbin
2020	A*	`conf`	Comparing Rewinding and Fine-tuning in Neural Network Pruning. ICLR Alex Renda, Jonathan Frankle, Michael Carbin
2020	J	`jnl`	Comparing Rewinding and Fine-tuning in Neural Network Pruning. CoRR Alex Renda, Jonathan Frankle, Michael Carbin
2020	A*	`conf`	DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates. MICRO Alex Renda, Yishen Chen, Charith Mendis, Michael Carbin
2020	J	`jnl`	DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates. CoRR Alex Renda, Yishen Chen, Charith Mendis, Michael Carbin
2020	A*	`conf`	Linear Mode Connectivity and the Lottery Ticket Hypothesis. ICML Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2020	J	`jnl`	On the Predictability of Pruning Across Scales. CoRR Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit
2020	—	`ch.`	Programming Unreliable Hardware. Foundations of Probabilistic Programming Michael Carbin, Sasa Misailovic
2020	J	`jnl`	Programming and reasoning with partial observability. Proc. ACM Program. Lang. Eric Atkinson, Michael Carbin
2020	J	`jnl`	Pruning Neural Networks at Initialization: Why are We Missing the Mark? CoRR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2020	A*	`conf`	Reactive probabilistic programming. PLDI Guillaume Baudart, Louis Mandel, Eric Atkinson, Benjamin Sherman, Marc Pouzet, Michael Carbin
2020	J	`jnl`	Simplifying Multiple-Statement Reductions with the Polyhedral Model. CoRR Cambridge Yang, Eric Atkinson, Michael Carbin
2020	J	`jnl`	TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning. CoRR Riyadh Baghdadi, Abdelkader Nadir Debbagh, Kamel Abdous, Fatima-Zohra Benhamida, Alex Renda, Jonathan Elliott Frankle, Michael Carbin, Saman P. Amarasinghe
2020	A*	`conf`	The Lottery Ticket Hypothesis for Pre-trained BERT Networks. NeurIPS Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin
2020	J	`jnl`	The Lottery Ticket Hypothesis for Pre-trained BERT Networks. CoRR Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin
2020	J	`jnl`	The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models. CoRR Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang
2020	J	`jnl`	Trace types and denotational semantics for sound programmable inference in probabilistic languages. Proc. ACM Program. Lang. Alexander K. Lew, Marco F. Cusumano-Towner, Benjamin Sherman, Michael Carbin, Vikash K. Mansinghka
2020	J	`jnl`	λ CoRR Benjamin Sherman, Jesse Michel, Michael Carbin
2019	—	`conf`	BHive: A Benchmark Suite and Measurement Framework for Validating x86-64 Basic Block Performance Models. IISWC Yishen Chen, Ajay Brahmakshatriya, Charith Mendis, Alex Renda, Eric Atkinson, Ondrej Sýkora, Saman P. Amarasinghe, Michael Carbin
2019	A*	`conf`	Compiler Auto-Vectorization with Imitation Learning. NeurIPS Charith Mendis, Cambridge Yang, Yewen Pu, Saman P. Amarasinghe, Michael Carbin
2019	A*	`conf`	Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. ICML Charith Mendis, Alex Renda, Saman P. Amarasinghe, Michael Carbin
2019	J	`jnl`	Linear Mode Connectivity and the Lottery Ticket Hypothesis. CoRR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2019	—	`conf`	Overparameterization: A Connection Between Software 1.0 and Software 2.0. SNAPL Michael Carbin
2019	J	`jnl`	Reactive Probabilistic Programming. CoRR Guillaume Baudart, Louis Mandel, Eric Atkinson, Benjamin Sherman, Marc Pouzet, Michael Carbin
2019	J	`jnl`	Sound and robust solid modeling via exact real arithmetic and continuity. Proc. ACM Program. Lang. Benjamin Sherman, Jesse Michel, Michael Carbin
2019	J	`jnl`	The Lottery Ticket Hypothesis at Scale. CoRR Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
2019	—	`conf`	The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. ICLR Jonathan Frankle, Michael Carbin
2018	J	`jnl`	Computable decision making on the reals and other spaces via partiality and nondeterminism. CoRR Benjamin Sherman, Luke Sciarappa, Adam Chlipala, Michael Carbin
2018	A*	`conf`	Computable decision making on the reals and other spaces: via partiality and nondeterminism. LICS Benjamin Sherman, Luke Sciarappa, Adam Chlipala, Michael Carbin
2018	J	`jnl`	Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms. CoRR Phillip Stanley-Marbell, Armin Alaghi, Michael Carbin, Eva Darulova, Lara Dolecek, Andreas Gerstlauer, Ghayoor Gillani, Djordje Jevdjic, Thierry Moreau, Mattia Cacciotti, Alexandros Daglis, Natalie D. Enright Jerger, Babak Falsafi, Sasa Misailovic, Adrian Sampson, Damien Zufferey
2018	J	`jnl`	Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. CoRR Charith Mendis, Saman P. Amarasinghe, Michael Carbin
2018	J	`jnl`	Leto: verifying application-specific hardware fault tolerance with programmable execution models. Proc. ACM Program. Lang. Brett Boston, Zoe Gong, Michael Carbin
2018	J	`jnl`	The Lottery Ticket Hypothesis: Training Pruned Neural Networks. CoRR Jonathan Frankle, Michael Carbin
2018	J	`jnl`	The Three Pillars of Machine-Based Programming. CoRR Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin C. Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson
2018	—	`conf`	The three pillars of machine programming. MAPL@PLDI Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin C. Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson
2018	J	`jnl`	Verifying Handcoded Probabilistic Inference Procedures. CoRR Eric Atkinson, Cambridge Yang, Michael Carbin
2018	J	`jnl`	Verifying Programs Under Custom Application-Specific Execution Models. CoRR Brett Boston, Zoe Gong, Michael Carbin
2017	A*	`conf`	Optimizing CNNs on Multicores for Scalability, Performance and Goodput. ASPLOS Samyam Rajbhandari, Yuxiong He, Olatunji Ruwase, Michael Carbin, Trishul M. Chilimbi
2016	J	`jnl`	Verifying quantitative reliability for programs that execute on unreliable hardware. Commun. ACM Michael Carbin, Sasa Misailovic, Martin C. Rinard
2015	—	`—`	Logical reasoning for approximate and unreliable computation. Michael Carbin
2014	A	`conf`	Chisel: reliability- and accuracy-aware optimization of approximate computational kernels. OOPSLA Sasa Misailovic, Michael Carbin, Sara Achour, Zichao Qi, Martin C. Rinard
2013	C	`conf`	Verified integrity properties for safe approximate program transformations. PEPM Michael Carbin, Deokhwan Kim, Sasa Misailovic, Martin C. Rinard
2013	A	`conf`	Verifying quantitative reliability for programs that execute on unreliable hardware. OOPSLA Michael Carbin, Sasa Misailovic, Martin C. Rinard
2012	A*	`conf`	Automatic input rectification. ICSE Fan Long, Vijay Ganesh, Michael Carbin, Stelios Sidiroglou, Martin C. Rinard
2012	A	`conf`	Bolt: on-demand infinite loop escape in unmodified binaries. OOPSLA Michael Kling, Sasa Misailovic, Michael Carbin, Martin C. Rinard
2012	J	`jnl`	Cryptographic Path Hardening: Hiding Vulnerabilities in Software through Cryptography CoRR Vijay Ganesh, Michael Carbin, Martin C. Rinard
2012	A*	`conf`	Proving acceptability properties of relaxed nondeterministic approximate programs. PLDI Michael Carbin, Deokhwan Kim, Sasa Misailovic, Martin C. Rinard
2011	A	`conf`	Detecting and Escaping Infinite Loops with Jolt. ECOOP Michael Carbin, Sasa Misailovic, Michael Kling, Martin C. Rinard
2011	A*	`conf`	Dynamic knobs for responsive power-aware computing. ASPLOS Henry Hoffmann, Stelios Sidiroglou, Michael Carbin, Sasa Misailovic, Anant Agarwal, Martin C. Rinard
2010	A	`conf`	Automatically identifying critical input regions and code in applications. ISSTA Michael Carbin, Martin C. Rinard
2009	A*	`conf`	Automatically patching errors in deployed software. SOSP Jeff H. Perkins, Sunghun Kim, Samuel Larsen, Saman P. Amarasinghe, Jonathan Bachrach, Michael Carbin, Carlos Pacheco, Frank Sherwood, Stelios Sidiroglou, Gregory T. Sullivan, Weng-Fai Wong, Yoav Zibin, Michael D. Ernst, Martin C. Rinard
2007	B	`conf`	Transactional collection classes. PPoPP Brian D. Carlstrom, Austen McDonald, Michael Carbin, Christos Kozyrakis, Kunle Olukotun
2006	B	`conf`	Reflective program generation with patterns. GPCE Manuel Fähndrich, Michael Carbin, James R. Larus
2005	A*	`conf`	Context-sensitive program analysis as database queries. PODS Monica S. Lam, John Whaley, V. Benjamin Livshits, Michael C. Martin, Dzintars Avots, Michael Carbin, Christopher Unkel
2005	B	`conf`	Using Datalog with Binary Decision Diagrams for Program Analysis. APLAS John Whaley, Dzintars Avots, Michael Carbin, Monica S. Lam