Using Datalog with Binary Decision Diagrams for Program Analysis. - John Whaley; Dzintars Avots; Michael Carbin; Monica S. Lam (APLAS, 2005)

Context-sensitive program analysis as database queries. - Monica S. Lam; John Whaley; V. Benjamin Livshits; Michael C. Martin; Dzintars Avots; Michael Carbin; Christopher Unkel (PODS, 2005)

Reflective program generation with patterns. - Manuel Fähndrich; Michael Carbin; James R. Larus (GPCE, 2006)

Transactional collection classes. - Brian D. Carlstrom; Austen McDonald; Michael Carbin; Christos Kozyrakis; Kunle Olukotun (PPoPP, 2007)

Automatically patching errors in deployed software. - Jeff H. Perkins; Sunghun Kim 001; Samuel Larsen; Saman P. Amarasinghe; Jonathan Bachrach; Michael Carbin; Carlos Pacheco; Frank Sherwood; Stelios Sidiroglou; Gregory T. Sullivan; Weng-Fai Wong; Yoav Zibin; et al. (SOSP, 2009)

Automatically identifying critical input regions and code in applications. - Michael Carbin; Martin C. Rinard (ISSTA, 2010)

Dynamic knobs for responsive power-aware computing. - Henry Hoffmann; Stelios Sidiroglou; Michael Carbin; Sasa Misailovic; Anant Agarwal; Martin C. Rinard (ASPLOS, 2011)

Detecting and Escaping Infinite Loops with Jolt. - Michael Carbin; Sasa Misailovic; Michael Kling; Martin C. Rinard (ECOOP, 2011)

Automatic input rectification. - Fan Long; Vijay Ganesh 001; Michael Carbin; Stelios Sidiroglou; Martin C. Rinard (ICSE, 2012)

Bolt: on-demand infinite loop escape in unmodified binaries. - Michael Kling; Sasa Misailovic; Michael Carbin; Martin C. Rinard (OOPSLA, 2012)

Proving acceptability properties of relaxed nondeterministic approximate programs. - Michael Carbin; Deokhwan Kim; Sasa Misailovic; Martin C. Rinard (PLDI, 2012)

Cryptographic Path Hardening: Hiding Vulnerabilities in Software through Cryptography - Vijay Ganesh 001; Michael Carbin; Martin C. Rinard (CoRR, 2012)

Verified integrity properties for safe approximate program transformations. - Michael Carbin; Deokhwan Kim; Sasa Misailovic; Martin C. Rinard (PEPM, 2013)

Chisel: reliability- and accuracy-aware optimization of approximate computational kernels. - Sasa Misailovic; Michael Carbin; Sara Achour; Zichao Qi; Martin C. Rinard (OOPSLA, 2014)

Logical reasoning for approximate and unreliable computation. - Michael Carbin (2015)

Verifying quantitative reliability for programs that execute on unreliable hardware. - Michael Carbin; Sasa Misailovic; Martin C. Rinard (Commun. ACM, 2016)

Optimizing CNNs on Multicores for Scalability, Performance and Goodput. - Samyam Rajbhandari; Yuxiong He; Olatunji Ruwase; Michael Carbin; Trishul M. Chilimbi (ASPLOS, 2017)

Computable decision making on the reals and other spaces: via partiality and nondeterminism. - Benjamin Sherman; Luke Sciarappa; Adam Chlipala; Michael Carbin (LICS, 2018)

The three pillars of machine programming. - Justin Gottschlich; Armando Solar-Lezama; Nesime Tatbul; Michael Carbin; Martin C. Rinard; Regina Barzilay; Saman P. Amarasinghe; Joshua B. Tenenbaum; Tim Mattson (MAPL@PLDI, 2018)

The Lottery Ticket Hypothesis: Training Pruned Neural Networks. - Jonathan Frankle; Michael Carbin (CoRR, 2018)

The Three Pillars of Machine-Based Programming. - Justin Gottschlich; Armando Solar-Lezama; Nesime Tatbul; Michael Carbin; Martin C. Rinard; Regina Barzilay; Saman P. Amarasinghe; Joshua B. Tenenbaum; Tim Mattson (CoRR, 2018)

Verifying Handcoded Probabilistic Inference Procedures. - Eric Atkinson; Cambridge Yang; Michael Carbin (CoRR, 2018)

Verifying Programs Under Custom Application-Specific Execution Models. - Brett Boston; Zoe Gong; Michael Carbin (CoRR, 2018)

Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. - Charith Mendis; Saman P. Amarasinghe; Michael Carbin (CoRR, 2018)

Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms. - 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; et al. (CoRR, 2018)

Leto: verifying application-specific hardware fault tolerance with programmable execution models. - Brett Boston; Zoe Gong; Michael Carbin (Proc. ACM Program. Lang., 2018)

The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. - Jonathan Frankle; Michael Carbin (ICLR, 2019)

Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. - Charith Mendis; Alex Renda; Saman P. Amarasinghe; Michael Carbin (ICML, 2019)

BHive: A Benchmark Suite and Measurement Framework for Validating x86-64 Basic Block Performance Models. - Yishen Chen; Ajay Brahmakshatriya; Charith Mendis; Alex Renda; Eric Atkinson; Ondrej Sýkora; Saman P. Amarasinghe; Michael Carbin (IISWC, 2019)

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

Overparameterization: A Connection Between Software 1.0 and Software 2.0. - Michael Carbin (SNAPL, 2019)

The Lottery Ticket Hypothesis at Scale. - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (CoRR, 2019)

Sound and robust solid modeling via exact real arithmetic and continuity. - Benjamin Sherman; Jesse Michel; Michael Carbin (Proc. ACM Program. Lang., 2019)

Programming Unreliable Hardware. - Michael Carbin; Sasa Misailovic (Foundations of Probabilistic Programming, 2020)

Comparing Rewinding and Fine-tuning in Neural Network Pruning. - Alex Renda; Jonathan Frankle; Michael Carbin (ICLR, 2020)

Linear Mode Connectivity and the Lottery Ticket Hypothesis. - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (ICML, 2020)

DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates. - Alex Renda; Yishen Chen; Charith Mendis; Michael Carbin (MICRO, 2020)

The Lottery Ticket Hypothesis for Pre-trained BERT Networks. - Tianlong Chen 001; Jonathan Frankle; Shiyu Chang; Sijia Liu 001; Yang Zhang 001; Zhangyang Wang; Michael Carbin (NeurIPS, 2020)

Reactive probabilistic programming. - Guillaume Baudart; Louis Mandel; Eric Atkinson; Benjamin Sherman; Marc Pouzet; Michael Carbin (PLDI, 2020)

TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning. - Riyadh Baghdadi; Abdelkader Nadir Debbagh; Kamel Abdous; Fatima-Zohra Benhamida; Alex Renda; Jonathan Elliott Frankle; Michael Carbin; Saman P. Amarasinghe (CoRR, 2020)

λS: Computable semantics for differentiable programming with higher-order functions and datatypes. - Benjamin Sherman; Jesse Michel; Michael Carbin (CoRR, 2020)

Simplifying Multiple-Statement Reductions with the Polyhedral Model. - Cambridge Yang; Eric Atkinson; Michael Carbin (CoRR, 2020)

Programming and reasoning with partial observability. - Eric Atkinson; Michael Carbin (Proc. ACM Program. Lang., 2020)

Trace types and denotational semantics for sound programmable inference in probabilistic languages. - Alexander K. Lew; Marco F. Cusumano-Towner; Benjamin Sherman; Michael Carbin; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2020)

VeGen: a vectorizer generator for SIMD and beyond. - Yishen Chen; Charith Mendis; Michael Carbin; Saman P. Amarasinghe (ASPLOS, 2021)

The Lottery Tickets Hypothesis for Supervised and Self-Supervised Pre-Training in Computer Vision Models. - Tianlong Chen 001; Jonathan Frankle; Shiyu Chang; Sijia Liu 001; Yang Zhang 001; Michael Carbin; Zhangyang Wang (CVPR, 2021)

Pruning Neural Networks at Initialization: Why Are We Missing the Mark? - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (ICLR, 2021)

On the Predictability of Pruning Across Scales. - Jonathan S. Rosenfeld; Jonathan Frankle; Michael Carbin; Nir Shavit (ICML, 2021)

Programming with neural surrogates of programs. - Alex Renda; Yi Ding 006; Michael Carbin (Onward!, 2021)

Generalizable and interpretable learning for configuration extrapolation. - Yi Ding 006; Ahsan Pervaiz; Michael Carbin; Henry Hoffmann (ESEC/SIGSOFT FSE, 2021)

Studying the Consistency and Composability of Lottery Ticket Pruning Masks. - Rajiv Movva; Jonathan Frankle; Michael Carbin (CoRR, 2021)

Reinforcement Learning for General LTL Objectives Is Intractable. - Cambridge Yang; Michael L. Littman; Michael Carbin (CoRR, 2021)

Exploiting Errors for Efficiency: A Survey from Circuits to Applications. - 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; et al. (ACM Comput. Surv., 2021)

Statically bounded-memory delayed sampling for probabilistic streams. - Eric Atkinson; Guillaume Baudart; Louis Mandel; Charles Yuan; Michael Carbin (Proc. ACM Program. Lang., 2021)

𝜆ₛ: computable semantics for differentiable programming with higher-order functions and datatypes. - Benjamin Sherman; Jesse Michel; Michael Carbin (Proc. ACM Program. Lang., 2021)

Simplifying dependent reductions in the polyhedral model. - Cambridge Yang; Eric Atkinson; Michael Carbin (Proc. ACM Program. Lang., 2021)

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

Pruning's Effect on Generalization Through the Lens of Training and Regularization. - Tian Jin; Michael Carbin; Daniel M. Roy 001; Jonathan Frankle; Gintare Karolina Dziugaite (NeurIPS, 2022)

Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression. - Yi Ding 006; Alex Renda; Ahsan Pervaiz; Michael Carbin; Henry Hoffmann (CoRR, 2022)

SCOPE: Safe Exploration for Dynamic Computer Systems Optimization. - Hyunji Kim; Ahsan Pervaiz; Henry Hoffmann; Michael Carbin; Yi Ding 006 (CoRR, 2022)

Acela: Predictable Datacenter-level Maintenance Job Scheduling. - Yi Ding 006; Aijia Gao; Thibaud Ryden; Kaushik Mitra; Sukumar Kalmanje; Yanai Golany; Michael Carbin; Henry Hoffmann (CoRR, 2022)

Semi-symbolic inference for efficient streaming probabilistic programming. - Eric Atkinson; Charles Yuan; Guillaume Baudart; Louis Mandel; Michael Carbin (Proc. ACM Program. Lang., 2022)

Tower: data structures in Quantum superposition. - Charles Yuan; Michael Carbin (Proc. ACM Program. Lang., 2022)

Twist: sound reasoning for purity and entanglement in Quantum programs. - Charles Yuan; Christopher McNally; Michael Carbin (Proc. ACM Program. Lang., 2022)

Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable. - Cambridge Yang; Michael Littman 002; Michael Carbin (AAAI, 2023)

Quantum Control Machine: The Limits of Quantum Programs as Data. - Charles Yuan; Agnes Villanyi; Michael Carbin (CoRR, 2023)

Verifying Performance Properties of Probabilistic Inference. - Eric Atkinson; Ellie Y. Cheng; Guillaume Baudart; Louis Mandel; Michael Carbin (CoRR, 2023)

The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning. - Tian Jin; Nolan Clement; Xin Dong; Vaishnavh Nagarajan; Michael Carbin; Jonathan Ragan-Kelley; Gintare Karolina Dziugaite (CoRR, 2023)

Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs. - Alex Renda; Yi Ding 006; Michael Carbin (Proc. ACM Program. Lang., 2023)

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 (ICLR, 2024)

Learning to Compile Programs to Neural Networks. - Logan Weber; Jesse Michel; Alex Renda; Michael Carbin (ICML, 2024)

BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text. - Elliot Bolton; Abhinav Venigalla; Michihiro Yasunaga; David Hall 006; Betty Xiong; Tony Lee; Roxana Daneshjou; Jonathan Frankle; Percy Liang; Michael Carbin; Christopher D. Manning (CoRR, 2024)

Long Context RAG Performance of Large Language Models. - Quinn Leng; Jacob P. Portes; Sam Havens; Matei Zaharia; Michael Carbin (CoRR, 2024)

Drowning in Documents: Consequences of Scaling Reranker Inference. - Mathew Jacob; Erik Lindgren; Matei Zaharia; Michael Carbin; Omar Khattab; Andrew Drozdov (CoRR, 2024)

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 (Proc. ACM Program. Lang., 2024)

The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation. - Charles Yuan; Michael Carbin (Proc. ACM Program. Lang., 2024)

Quantum Control Machine: The Limits of Control Flow in Quantum Programming. - Charles Yuan; Agnes Villanyi; Michael Carbin (Proc. ACM Program. Lang., 2024)

Inference Plans for Hybrid Particle Filtering. - Ellie Y. Cheng; Eric Atkinson; Guillaume Baudart; Louis Mandel; Michael Carbin (Proc. ACM Program. Lang., 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 (CoRR, 2025)

FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents. - Nandan Thakur; Jimmy Lin; Sam Havens; Michael Carbin; Omar Khattab; Andrew Drozdov (CoRR, 2025)