Michael Carbin

ORCID

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 78

Top Venues

CoRR

Proc. ACM Program. Lang.

ICLR

ICML

Massachusetts Institute of Technology, Cambridge, MA, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, MA, USA

PHD

Publications

Charles Yuan, Agnes Villanyi, Michael Carbin
Quantum Control Machine: The Limits of Control Flow in Quantum Programming. Proc. ACM Program. Lang. 8 (OOPSLA1) (2024)
Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite
The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning. ICLR (2024)
Logan Weber, Jesse Michel, Alex Renda, Michael Carbin
Learning to Compile Programs to Neural Networks. CoRR (2024)
Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, Christopher D. Manning
BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text. CoRR (2024)
Jesse Michel, Kevin Mu, Xuanda Yang, Sai Praveen Bangaru, Elias Rojas Collins, Gilbert Bernstein, Jonathan Ragan-Kelley, Michael Carbin, Tzu-Mao Li
Distributions for Compositionally Differentiating Parametric Discontinuities. Proc. ACM Program. Lang. 8 (OOPSLA1) (2024)
Charles Yuan, Michael Carbin
The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation. Proc. ACM Program. Lang. 8 (PLDI) (2024)
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. CoRR (2023)
Charles Yuan, Agnes Villanyi, Michael Carbin
Quantum Control Machine: The Limits of Quantum Programs as Data. CoRR (2023)
Cambridge Yang, Michael L. Littman, Michael Carbin
Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable. AAAI (2023)
Eric Atkinson, Ellie Y. Cheng, Guillaume Baudart, Louis Mandel, Michael Carbin
Verifying Performance Properties of Probabilistic Inference. CoRR (2023)
Alex Renda, Yi Ding, Michael Carbin
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs. Proc. ACM Program. Lang. 7 (OOPSLA2) (2023)
Cambridge Yang, Michael L. Littman, Michael Carbin
Computably Continuous Reinforcement-Learning Objectives are PAC-learnable. CoRR (2023)
Alex Renda, Yi Ding, Michael Carbin
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs. CoRR (2023)
Charles Yuan, Michael Carbin
The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation. CoRR (2023)
Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite
Pruning's Effect on Generalization Through the Lens of Training and Regularization. NeurIPS (2022)
Yi Ding, Aijia Gao, Thibaud Ryden, Kaushik Mitra, Sukumar Kalmanje, Yanai Golany, Michael Carbin, Henry Hoffmann
Acela: Predictable Datacenter-level Maintenance Job Scheduling. CoRR (2022)
Yi Ding, Alex Renda, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann
Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression. CoRR (2022)
Eric Atkinson, Charles Yuan, Guillaume Baudart, Louis Mandel, Michael Carbin
Semi-Symbolic Inference for Efficient Streaming Probabilistic Programming. CoRR (2022)
Charles Yuan, Christopher McNally, Michael Carbin
Twist: Sound Reasoning for Purity and Entanglement in Quantum Programs. CoRR (2022)
Charles Yuan, Christopher McNally, Michael Carbin
Twist: sound reasoning for purity and entanglement in Quantum programs. Proc. ACM Program. Lang. 6 (POPL) (2022)
Charles Yuan, Michael Carbin
Tower: Data Structures in Quantum Superposition. CoRR (2022)
Cambridge Yang, Michael L. Littman, Michael Carbin
On the (In)Tractability of Reinforcement Learning for LTL Objectives. IJCAI (2022)
Charles Yuan, Michael Carbin
Tower: data structures in Quantum superposition. Proc. ACM Program. Lang. 6 (OOPSLA2) (2022)
Hyunji Kim, Ahsan Pervaiz, Henry Hoffmann, Michael Carbin, Yi Ding
SCOPE: Safe Exploration for Dynamic Computer Systems Optimization. CoRR (2022)
Eric Atkinson, Charles Yuan, Guillaume Baudart, Louis Mandel, Michael Carbin
Semi-symbolic inference for efficient streaming probabilistic programming. Proc. ACM Program. Lang. 6 (OOPSLA2) (2022)
Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite
Pruning's Effect on Generalization Through the Lens of Training and Regularization. CoRR (2022)
Rajiv Movva, Jonathan Frankle, Michael Carbin
Studying the Consistency and Composability of Lottery Ticket Pruning Masks. CoRR (2021)
Eric Atkinson, Michael Carbin
Programming and Reasoning with Partial Observability. CoRR (2021)
Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit
On the Predictability of Pruning Across Scales. ICML (2021)
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, Michael Carbin
Pruning Neural Networks at Initialization: Why Are We Missing the Mark? ICLR (2021)
Alex Renda, Yi Ding, Michael Carbin
Programming with Neural Surrogates of Programs. CoRR (2021)
Cambridge Yang, Michael L. Littman, Michael Carbin
Reinforcement Learning for General LTL Objectives Is Intractable. CoRR (2021)
Yi Ding, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann
Generalizable and interpretable learning for configuration extrapolation. ESEC/SIGSOFT FSE (2021)
Alex Renda, Yi Ding, Michael Carbin
Programming with neural surrogates of programs. Onward! (2021)
Yishen Chen, Charith Mendis, Michael Carbin, Saman P. Amarasinghe
VeGen: a vectorizer generator for SIMD and beyond. ASPLOS (2021)
Benjamin Sherman, Jesse Michel, Michael Carbin
𝜆ₛ: computable semantics for differentiable programming with higher-order functions and datatypes. Proc. ACM Program. Lang. 5 (POPL) (2021)
Eric Atkinson, Guillaume Baudart, Louis Mandel, Charles Yuan, Michael Carbin
Statically Bounded-Memory Delayed Sampling for Probabilistic Streams. CoRR (2021)
Eric Atkinson, Guillaume Baudart, Louis Mandel, Charles Yuan, Michael Carbin
Statically bounded-memory delayed sampling for probabilistic streams. Proc. ACM Program. Lang. 5 (OOPSLA) (2021)
Cambridge Yang, Eric Atkinson, Michael Carbin
Simplifying dependent reductions in the polyhedral model. Proc. ACM Program. Lang. 5 (POPL) (2021)
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang
The Lottery Tickets Hypothesis for Supervised and Self-Supervised Pre-Training in Computer Vision Models. CVPR (2021)
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models. CoRR (2020)
Alex Renda, Yishen Chen, Charith Mendis, Michael Carbin
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates. CoRR (2020)
Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit
On the Predictability of Pruning Across Scales. CoRR (2020)
Riyadh Baghdadi, Abdelkader Nadir Debbagh, Kamel Abdous, Fatima-Zohra Benhamida, Alex Renda, Jonathan Elliott Frankle, Michael Carbin, Saman P. Amarasinghe
TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning. CoRR (2020)
Alex Renda, Yishen Chen, Charith Mendis, Michael Carbin
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates. MICRO (2020)
Benjamin Sherman, Jesse Michel, Michael Carbin
: Computable semantics for differentiable programming with higher-order functions and datatypes. CoRR (2020)
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
Linear Mode Connectivity and the Lottery Ticket Hypothesis. ICML (2020)
Alex Renda, Jonathan Frankle, Michael Carbin
Comparing Rewinding and Fine-tuning in Neural Network Pruning. ICLR (2020)
Eric Atkinson, Michael Carbin
Programming and reasoning with partial observability. Proc. ACM Program. Lang. 4 (OOPSLA) (2020)
Alex Renda, Jonathan Frankle, Michael Carbin
Comparing Rewinding and Fine-tuning in Neural Network Pruning. CoRR (2020)
Cambridge Yang, Eric Atkinson, Michael Carbin
Simplifying Multiple-Statement Reductions with the Polyhedral Model. CoRR (2020)
Alexander K. Lew, Marco F. Cusumano-Towner, Benjamin Sherman, Michael Carbin, Vikash K. Mansinghka
Trace types and denotational semantics for sound programmable inference in probabilistic languages. Proc. ACM Program. Lang. 4 (POPL) (2020)
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
Pruning Neural Networks at Initialization: Why are We Missing the Mark? CoRR (2020)
Guillaume Baudart, Louis Mandel, Eric Atkinson, Benjamin Sherman, Marc Pouzet, Michael Carbin
Reactive probabilistic programming. PLDI (2020)
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin
The Lottery Ticket Hypothesis for Pre-trained BERT Networks. NeurIPS (2020)
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
Exploiting Errors for Efficiency: A Survey from Circuits to Applications. ACM Comput. Surv. 53 (3) (2020)
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin
The Lottery Ticket Hypothesis for Pre-trained BERT Networks. CoRR (2020)
Yishen Chen, Ajay Brahmakshatriya, Charith Mendis, Alex Renda, Eric Atkinson, Ondrej Sýkora, Saman P. Amarasinghe, Michael Carbin
BHive: A Benchmark Suite and Measurement Framework for Validating x86-64 Basic Block Performance Models. IISWC (2019)
Charith Mendis, Cambridge Yang, Yewen Pu, Saman P. Amarasinghe, Michael Carbin
Compiler Auto-Vectorization with Imitation Learning. NeurIPS (2019)
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
Linear Mode Connectivity and the Lottery Ticket Hypothesis. CoRR (2019)
Benjamin Sherman, Jesse Michel, Michael Carbin
Sound and robust solid modeling via exact real arithmetic and continuity. Proc. ACM Program. Lang. 3 (ICFP) (2019)
Jonathan Frankle, Michael Carbin
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. ICLR (2019)
Michael Carbin
Overparameterization: A Connection Between Software 1.0 and Software 2.0. SNAPL (2019)
Guillaume Baudart, Louis Mandel, Eric Atkinson, Benjamin Sherman, Marc Pouzet, Michael Carbin
Reactive Probabilistic Programming. CoRR (2019)
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
The Lottery Ticket Hypothesis at Scale. CoRR (2019)
Charith Mendis, Alex Renda, Saman P. Amarasinghe, Michael Carbin
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. ICML (2019)
Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson
The three pillars of machine programming. MAPL@PLDI (2018)
Benjamin Sherman, Luke Sciarappa, Adam Chlipala, Michael Carbin
Computable decision making on the reals and other spaces: via partiality and nondeterminism. LICS (2018)
Charith Mendis, Saman P. Amarasinghe, Michael Carbin
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. CoRR (2018)
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
Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms. CoRR (2018)
Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson
The Three Pillars of Machine-Based Programming. CoRR (2018)
Benjamin Sherman, Luke Sciarappa, Adam Chlipala, Michael Carbin
Computable decision making on the reals and other spaces via partiality and nondeterminism. CoRR (2018)
Brett Boston, Zoe Gong, Michael Carbin
Leto: verifying application-specific hardware fault tolerance with programmable execution models. Proc. ACM Program. Lang. 2 (OOPSLA) (2018)
Brett Boston, Zoe Gong, Michael Carbin
Verifying Programs Under Custom Application-Specific Execution Models. CoRR (2018)
Jonathan Frankle, Michael Carbin
The Lottery Ticket Hypothesis: Training Pruned Neural Networks. CoRR (2018)
Eric Atkinson, Cambridge Yang, Michael Carbin
Verifying Handcoded Probabilistic Inference Procedures. CoRR (2018)
Samyam Rajbhandari, Yuxiong He, Olatunji Ruwase, Michael Carbin, Trishul M. Chilimbi
Optimizing CNNs on Multicores for Scalability, Performance and Goodput. ASPLOS (2017)
Michael Carbin, Sasa Misailovic, Martin C. Rinard
Verifying quantitative reliability for programs that execute on unreliable hardware. Commun. ACM 59 (8) (2016)
Sasa Misailovic, Michael Carbin, Sara Achour, Zichao Qi, Martin C. Rinard
Chisel: reliability- and accuracy-aware optimization of approximate computational kernels. OOPSLA (2014)
Michael Carbin, Sasa Misailovic, Martin C. Rinard
Verifying quantitative reliability for programs that execute on unreliable hardware. OOPSLA (2013)
Michael Carbin, Deokhwan Kim, Sasa Misailovic, Martin C. Rinard
Verified integrity properties for safe approximate program transformations. PEPM (2013)
Michael Kling, Sasa Misailovic, Michael Carbin, Martin C. Rinard
Bolt: on-demand infinite loop escape in unmodified binaries. OOPSLA (2012)
Vijay Ganesh, Michael Carbin, Martin C. Rinard
Cryptographic Path Hardening: Hiding Vulnerabilities in Software through Cryptography CoRR (2012)
Fan Long, Vijay Ganesh, Michael Carbin, Stelios Sidiroglou, Martin C. Rinard
Automatic input rectification. ICSE (2012)
Michael Carbin, Deokhwan Kim, Sasa Misailovic, Martin C. Rinard
Proving acceptability properties of relaxed nondeterministic approximate programs. PLDI (2012)
Henry Hoffmann, Stelios Sidiroglou, Michael Carbin, Sasa Misailovic, Anant Agarwal, Martin C. Rinard
Dynamic knobs for responsive power-aware computing. ASPLOS (2011)
Michael Carbin, Sasa Misailovic, Michael Kling, Martin C. Rinard
Detecting and Escaping Infinite Loops with Jolt. ECOOP (2011)
Michael Carbin, Martin C. Rinard
Automatically identifying critical input regions and code in applications. ISSTA (2010)
Jeff H. Perkins, Sunghun Kim, Samuel Larsen, Saman P. Amarasinghe, Jonathan Bachrach, Michael Carbin, Carlos Pacheco, Frank Sherwood, Stelios Sidiroglou, Greg Sullivan, Weng-Fai Wong, Yoav Zibin, Michael D. Ernst, Martin C. Rinard
Automatically patching errors in deployed software. SOSP (2009)
Brian D. Carlstrom, Austen McDonald, Michael Carbin, Christos Kozyrakis, Kunle Olukotun
Transactional collection classes. PPOPP (2007)
Manuel Fähndrich, Michael Carbin, James R. Larus
Reflective program generation with patterns. GPCE (2006)
Monica S. Lam, John Whaley, V. Benjamin Livshits, Michael C. Martin, Dzintars Avots, Michael Carbin, Christopher Unkel
Context-sensitive program analysis as database queries. PODS (2005)
John Whaley, Dzintars Avots, Michael Carbin, Monica S. Lam
Using Datalog with Binary Decision Diagrams for Program Analysis. APLAS (2005)

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Publication Activity (10 Years)

Top Topics

Top Venues

Michael Carbin