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Michael Carbin
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2020 – today
- 2024
- [j16]Charles Yuan
, Agnes Villanyi, Michael Carbin:
Quantum Control Machine: The Limits of Control Flow in Quantum Programming. Proc. ACM Program. Lang. 8(OOPSLA1): 1-28 (2024) - [j15]Jesse Michel
Distributions for Compositionally Differentiating Parametric Discontinuities. Proc. ACM Program. Lang. 8(OOPSLA1): 893-922 (2024) - [j14]Charles Yuan
The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation. Proc. ACM Program. Lang. 8(PLDI): 492-517 (2024) - [c38]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 - [c37]Logan Weber, Jesse Michel, Alex Renda, Michael Carbin:
Learning to Compile Programs to Neural Networks. ICML 2024 - [i41]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 abs/2403.18421 (2024) - [i40]Logan Weber, Jesse Michel, Alex Renda, Michael Carbin:
Learning to Compile Programs to Neural Networks. CoRR abs/2407.15078 (2024) - [i39]Ellie Y. Cheng, Eric Atkinson, Guillaume Baudart, Louis Mandel, Michael Carbin:
Inference Plans for Hybrid Particle Filtering. CoRR abs/2408.11283 (2024) - 2023
- [j13]Alex Renda
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs. Proc. ACM Program. Lang. 7(OOPSLA2): 1648-1676 (2023) - [c36]Cambridge Yang, Michael Littman, Michael Carbin:
Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable. AAAI 2023: 10729-10736 - [e1]Dawn Song, Michael Carbin, Tianqi Chen:
Proceedings of the Sixth Conference on Machine Learning and Systems, MLSys 2023, Miami, FL, USA, June 4-8, 2023. mlsys.org 2023 [contents] - [i38]Cambridge Yang, Michael Littman, Michael Carbin:
Computably Continuous Reinforcement-Learning Objectives are PAC-learnable. CoRR abs/2303.05518 (2023) - [i37]Charles Yuan, Agnes Villanyi, Michael Carbin:
Quantum Control Machine: The Limits of Quantum Programs as Data. CoRR abs/2304.15000 (2023) - [i36]Eric Atkinson, Ellie Y. Cheng, Guillaume Baudart, Louis Mandel, Michael Carbin:
Verifying Performance Properties of Probabilistic Inference. CoRR abs/2307.07355 (2023) - [i35]Alex Renda, Yi Ding, Michael Carbin:
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs. CoRR abs/2309.11726 (2023) - [i34]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 abs/2310.04680 (2023) - [i33]Charles Yuan, Michael Carbin:
The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation. CoRR abs/2311.12772 (2023) - 2022
- [j12]Charles Yuan
Tower: data structures in Quantum superposition. Proc. ACM Program. Lang. 6(OOPSLA2): 259-288 (2022) - [j11]Eric Atkinson
Semi-symbolic inference for efficient streaming probabilistic programming. Proc. ACM Program. Lang. 6(OOPSLA2): 1668-1696 (2022) - [j10]Charles Yuan
Twist: sound reasoning for purity and entanglement in Quantum programs. Proc. ACM Program. Lang. 6(POPL): 1-32 (2022) - [c35]Cambridge Yang, Michael L. Littman, Michael Carbin:
On the (In)Tractability of Reinforcement Learning for LTL Objectives. IJCAI 2022: 3650-3658 - [c34]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 - [i32]Yi Ding, Alex Renda, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann:
Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression. CoRR abs/2204.04831 (2022) - [i31]Hyunji Kim, Ahsan Pervaiz, Henry Hoffmann, Michael Carbin, Yi Ding:
SCOPE: Safe Exploration for Dynamic Computer Systems Optimization. CoRR abs/2204.10451 (2022) - [i30]Charles Yuan, Christopher McNally, Michael Carbin:
Twist: Sound Reasoning for Purity and Entanglement in Quantum Programs. CoRR abs/2205.02287 (2022) - [i29]Charles Yuan, Michael Carbin:
Tower: Data Structures in Quantum Superposition. CoRR abs/2205.10255 (2022) - [i28]Eric Atkinson, Charles Yuan, Guillaume Baudart, Louis Mandel, Michael Carbin:
Semi-Symbolic Inference for Efficient Streaming Probabilistic Programming. CoRR abs/2209.07490 (2022) - [i27]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 abs/2210.13738 (2022) - [i26]Yi Ding, Aijia Gao, Thibaud Ryden, Kaushik Mitra, Sukumar Kalmanje, Yanai Golany, Michael Carbin, Henry Hoffmann:
Acela: Predictable Datacenter-level Maintenance Job Scheduling. CoRR abs/2212.05155 (2022) - 2021
- [j9]Phillip Stanley-Marbell
Exploiting Errors for Efficiency: A Survey from Circuits to Applications. ACM Comput. Surv. 53(3): 51:1-51:39 (2021) - [j8]Eric Atkinson
Statically bounded-memory delayed sampling for probabilistic streams. Proc. ACM Program. Lang. 5(OOPSLA): 1-28 (2021) - [j7]Benjamin Sherman, Jesse Michel, Michael Carbin:
𝜆ₛ: computable semantics for differentiable programming with higher-order functions and datatypes. Proc. ACM Program. Lang. 5(POPL): 1-31 (2021) - [j6]Cambridge Yang, Eric Atkinson, Michael Carbin:
Simplifying dependent reductions in the polyhedral model. Proc. ACM Program. Lang. 5(POPL): 1-33 (2021) - [c33]Yishen Chen, Charith Mendis, Michael Carbin, Saman P. Amarasinghe
VeGen: a vectorizer generator for SIMD and beyond. ASPLOS 2021: 902-914 - [c32]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: 16306-16316 - [c31]Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin:
Pruning Neural Networks at Initialization: Why Are We Missing the Mark? ICLR 2021 - [c30]Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit:
On the Predictability of Pruning Across Scales. ICML 2021: 9075-9083 - [c29]Alex Renda
Programming with neural surrogates of programs. Onward! 2021: 18-38 - [c28]Yi Ding
Generalizable and interpretable learning for configuration extrapolation. ESEC/SIGSOFT FSE 2021: 728-740 - [i25]Eric Atkinson, Michael Carbin:
Programming and Reasoning with Partial Observability. CoRR abs/2101.04742 (2021) - [i24]Rajiv Movva, Jonathan Frankle, Michael Carbin:
Studying the Consistency and Composability of Lottery Ticket Pruning Masks. CoRR abs/2104.14753 (2021) - [i23]Eric Atkinson, Guillaume Baudart, Louis Mandel, Charles Yuan, Michael Carbin:
Statically Bounded-Memory Delayed Sampling for Probabilistic Streams. CoRR abs/2109.12473 (2021) - [i22]Cambridge Yang, Michael L. Littman, Michael Carbin:
Reinforcement Learning for General LTL Objectives Is Intractable. CoRR abs/2111.12679 (2021) - [i21]Alex Renda, Yi Ding, Michael Carbin:
Programming with Neural Surrogates of Programs. CoRR abs/2112.06148 (2021) - 2020
- [j5]Eric Atkinson, Michael Carbin:
Programming and reasoning with partial observability. Proc. ACM Program. Lang. 4(OOPSLA): 200:1-200:28 (2020) - [j4]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): 19:1-19:32 (2020) - [c27]Alex Renda, Jonathan Frankle, Michael Carbin:
Comparing Rewinding and Fine-tuning in Neural Network Pruning. ICLR 2020 - [c26]Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin:
Linear Mode Connectivity and the Lottery Ticket Hypothesis. ICML 2020: 3259-3269 - [c25]Alex Renda, Yishen Chen, Charith Mendis, Michael Carbin:
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates. MICRO 2020: 442-455 - [c24]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 - [c23]Guillaume Baudart, Louis Mandel, Eric Atkinson, Benjamin Sherman, Marc Pouzet, Michael Carbin:
Reactive probabilistic programming. PLDI 2020: 898-912 - [i20]Alex Renda, Jonathan Frankle, Michael Carbin:
Comparing Rewinding and Fine-tuning in Neural Network Pruning. CoRR abs/2003.02389 (2020) - [i19]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 abs/2005.04091 (2020) - [i18]Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit:
On the Predictability of Pruning Across Scales. CoRR abs/2006.10621 (2020) - [i17]Benjamin Sherman, Jesse Michel, Michael Carbin:
λS: Computable semantics for differentiable programming with higher-order functions and datatypes. CoRR abs/2007.08017 (2020) - [i16]Cambridge Yang, Eric Atkinson, Michael Carbin:
Simplifying Multiple-Statement Reductions with the Polyhedral Model. CoRR abs/2007.11203 (2020) - [i15]Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin:
The Lottery Ticket Hypothesis for Pre-trained BERT Networks. CoRR abs/2007.12223 (2020) - [i14]Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin:
Pruning Neural Networks at Initialization: Why are We Missing the Mark? CoRR abs/2009.08576 (2020) - [i13]Alex Renda, Yishen Chen, Charith Mendis, Michael Carbin:
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates. CoRR abs/2010.04017 (2020) - [i12]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 abs/2012.06908 (2020)
2010 – 2019
- 2019
- [j3]Benjamin Sherman, Jesse Michel, Michael Carbin:
Sound and robust solid modeling via exact real arithmetic and continuity. Proc. ACM Program. Lang. 3(ICFP): 99:1-99:29 (2019) - [c22]Jonathan Frankle, Michael Carbin:
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks. ICLR 2019 - [c21]Charith Mendis, Alex Renda, Saman P. Amarasinghe, Michael Carbin:
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. ICML 2019: 4505-4515 - [c20]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: 167-177 - [c19]Charith Mendis, Cambridge Yang, Yewen Pu, Saman P. Amarasinghe, Michael Carbin:
Compiler Auto-Vectorization with Imitation Learning. NeurIPS 2019: 14598-14609 - [c18]Michael Carbin:
Overparameterization: A Connection Between Software 1.0 and Software 2.0. SNAPL 2019: 1:1-1:13 - [i11]Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin:
The Lottery Ticket Hypothesis at Scale. CoRR abs/1903.01611 (2019) - [i10]Guillaume Baudart, Louis Mandel, Eric Atkinson, Benjamin Sherman, Marc Pouzet, Michael Carbin:
Reactive Probabilistic Programming. CoRR abs/1908.07563 (2019) - [i9]Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin:
Linear Mode Connectivity and the Lottery Ticket Hypothesis. CoRR abs/1912.05671 (2019) - 2018
- [j2]Brett Boston, Zoe Gong, Michael Carbin:
Leto: verifying application-specific hardware fault tolerance with programmable execution models. Proc. ACM Program. Lang. 2(OOPSLA): 163:1-163:30 (2018) - [c17]Benjamin Sherman, Luke Sciarappa, Adam Chlipala, Michael Carbin:
Computable decision making on the reals and other spaces: via partiality and nondeterminism. LICS 2018: 859-868 - [c16]Justin Gottschlich, Armando Solar-Lezama
The three pillars of machine programming. MAPL@PLDI 2018: 69-80 - [i8]Jonathan Frankle, Michael Carbin:
The Lottery Ticket Hypothesis: Training Pruned Neural Networks. CoRR abs/1803.03635 (2018) - [i7]Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin C. Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson:
The Three Pillars of Machine-Based Programming. CoRR abs/1803.07244 (2018) - [i6]Benjamin Sherman, Luke Sciarappa, Adam Chlipala, Michael Carbin:
Computable decision making on the reals and other spaces via partiality and nondeterminism. CoRR abs/1805.00468 (2018) - [i5]Eric Atkinson, Cambridge Yang, Michael Carbin:
Verifying Handcoded Probabilistic Inference Procedures. CoRR abs/1805.01863 (2018) - [i4]Brett Boston, Zoe Gong, Michael Carbin:
Verifying Programs Under Custom Application-Specific Execution Models. CoRR abs/1805.06090 (2018) - [i3]Charith Mendis, Saman P. Amarasinghe, Michael Carbin:
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. CoRR abs/1808.07412 (2018) - [i2]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 abs/1809.05859 (2018) - 2017
- [c15]Samyam Rajbhandari, Yuxiong He, Olatunji Ruwase, Michael Carbin, Trishul M. Chilimbi:
Optimizing CNNs on Multicores for Scalability, Performance and Goodput. ASPLOS 2017: 267-280 - 2016
- [j1]Michael Carbin, Sasa Misailovic, Martin C. Rinard:
Verifying quantitative reliability for programs that execute on unreliable hardware. Commun. ACM 59(8): 83-91 (2016) - 2015
- [b1]Michael Carbin:
Logical reasoning for approximate and unreliable computation. Massachusetts Institute of Technology, Cambridge, MA, USA, 2015 - 2014
- [c14]Sasa Misailovic, Michael Carbin, Sara Achour
Chisel: reliability- and accuracy-aware optimization of approximate computational kernels. OOPSLA 2014: 309-328 - 2013
- [c13]Michael Carbin, Sasa Misailovic, Martin C. Rinard:
Verifying quantitative reliability for programs that execute on unreliable hardware. OOPSLA 2013: 33-52 - [c12]Michael Carbin, Deokhwan Kim, Sasa Misailovic, Martin C. Rinard:
Verified integrity properties for safe approximate program transformations. PEPM 2013: 63-66 - 2012
- [c11]Fan Long, Vijay Ganesh
Automatic input rectification. ICSE 2012: 80-90 - [c10]Michael Kling, Sasa Misailovic, Michael Carbin, Martin C. Rinard:
Bolt: on-demand infinite loop escape in unmodified binaries. OOPSLA 2012: 431-450 - [c9]Michael Carbin, Deokhwan Kim, Sasa Misailovic, Martin C. Rinard:
Proving acceptability properties of relaxed nondeterministic approximate programs. PLDI 2012: 169-180 - [i1]Vijay Ganesh, Michael Carbin, Martin C. Rinard:
Cryptographic Path Hardening: Hiding Vulnerabilities in Software through Cryptography. CoRR abs/1202.0359 (2012) - 2011
- [c8]Henry Hoffmann, Stelios Sidiroglou, Michael Carbin, Sasa Misailovic, Anant Agarwal, Martin C. Rinard:
Dynamic knobs for responsive power-aware computing. ASPLOS 2011: 199-212 - [c7]Michael Carbin, Sasa Misailovic, Michael Kling, Martin C. Rinard:
Detecting and Escaping Infinite Loops with Jolt. ECOOP 2011: 609-633 - 2010
- [c6]Michael Carbin, Martin C. Rinard:
Automatically identifying critical input regions and code in applications. ISSTA 2010: 37-48
2000 – 2009
- 2009
- [c5]Jeff H. Perkins, Sunghun Kim, Samuel Larsen, Saman P. Amarasinghe
Automatically patching errors in deployed software. SOSP 2009: 87-102 - 2007
- [c4]Brian D. Carlstrom, Austen McDonald, Michael Carbin, Christos Kozyrakis, Kunle Olukotun:
Transactional collection classes. PPoPP 2007: 56-67 - 2006
- [c3]Manuel Fähndrich, Michael Carbin, James R. Larus:
Reflective program generation with patterns. GPCE 2006: 275-284 - 2005
- [c2]John Whaley, Dzintars Avots, Michael Carbin, Monica S. Lam:
Using Datalog with Binary Decision Diagrams for Program Analysis. APLAS 2005: 97-118 - [c1]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: 1-12
Coauthor Index
Jonathan Frankle
aka: Jonathan Elliott Frankle
aka: Jonathan Elliott Frankle
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last updated on 2024-10-07 21:14 CEST by the dblp team
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