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2020 – today
- 2024
[j28]Nathanael L. Ackerman
, Cameron E. Freer, Younesse Kaddar, Jacek Karwowski, Sean K. Moss, Daniel M. Roy, Sam Staton, Hongseok Yang:
Probabilistic Programming Interfaces for Random Graphs: Markov Categories, Graphons, and Nominal Sets. Proc. ACM Program. Lang. 8(POPL): 1819-1849 (2024)- [c87]Hyunsu Kim, Yegon Kim, Hongseok Yang, Juho Lee:
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts. ICML 2024 - [c86]Taeyoung Kim, Hongseok Yang:
An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network. ICML 2024 - [i36]Hyunsu Kim, Yegon Kim, Hongseok Yang, Juho Lee:
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts. CoRR abs/2407.04271 (2024) - 2023
- [j27]Hoil Lee, Fadhel Ayed, Paul Jung, Juho Lee, Hongseok Yang, Francois Caron:
Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility. J. Mach. Learn. Res. 24: 289:1-289:78 (2023) - [j26]Wonyeol Lee, Xavier Rival, Hongseok Yang:
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference. Proc. ACM Program. Lang. 7(POPL): 335-366 (2023) - [c85]Hyunsu Kim, Hyungi Lee, Hongseok Yang, Juho Lee:
Regularizing Towards Soft Equivariance Under Mixed Symmetries. ICML 2023: 16712-16727 - [c84]Seokin Seo, HyeongJoo Hwang, Hongseok Yang, Kee-Eung Kim:
Regularized Behavior Cloning for Blocking the Leakage of Past Action Information. NeurIPS 2023 - [i35]Francois Caron, Fadhel Ayed, Paul Jung, Hoil Lee, Juho Lee, Hongseok Yang:
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning. CoRR abs/2302.01002 (2023) - [i34]Hyunsu Kim, Hyungi Lee, Hongseok Yang, Juho Lee:
Regularizing Towards Soft Equivariance Under Mixed Symmetries. CoRR abs/2306.00356 (2023) - [i33]Tien Dat Nguyen, Jinwoo Kim, Hongseok Yang, Seunghoon Hong:
Learning Symmetrization for Equivariance with Orbit Distance Minimization. CoRR abs/2311.07143 (2023) - [i32]Taeyoung Kim, Hongseok Yang:
An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network. CoRR abs/2312.03386 (2023) - [i31]Nathanael L. Ackerman, Cameron E. Freer, Younesse Kaddar, Jacek Karwowski, Sean K. Moss, Daniel M. Roy, Sam Staton, Hongseok Yang:
Probabilistic programming interfaces for random graphs: Markov categories, graphons, and nominal sets. CoRR abs/2312.17127 (2023) - 2022
- [c83]Geon-Hyeong Kim, Seokin Seo, Jongmin Lee, Wonseok Jeon, HyeongJoo Hwang, Hongseok Yang, Kee-Eung Kim:
DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations. ICLR 2022 - [c82]Hyungi Lee, Eunggu Yun, Hongseok Yang, Juho Lee:
Scale Mixtures of Neural Network Gaussian Processes. ICLR 2022 - [c81]Geon-Hyeong Kim, Jongmin Lee, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim:
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation. NeurIPS 2022 - [c80]Sangho Lim, Eun-Gyeol Oh, Hongseok Yang:
Learning Symmetric Rules with SATNet. NeurIPS 2022 - [i30]Geon-Hyeong Kim, Jongmin Lee, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim:
LobsDICE: Offline Imitation Learning from Observation via Stationary Distribution Correction Estimation. CoRR abs/2202.13536 (2022) - [i29]Hoil Lee, Fadhel Ayed, Paul Jung, Juho Lee, Hongseok Yang, François Caron:
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility. CoRR abs/2205.08187 (2022) - [i28]Sangho Lim, Eun-Gyeol Oh, Hongseok Yang:
Learning Symmetric Rules with SATNet. CoRR abs/2206.13998 (2022) - [i27]Wonyeol Lee, Xavier Rival, Hongseok Yang:
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference. CoRR abs/2208.10530 (2022) - 2021
- [j25]Hagit Attiya, Sebastian Burckhardt, Alexey Gotsman, Adam Morrison, Hongseok Yang, Marek Zawirski:
Specification and space complexity of collaborative text editing. Theor. Comput. Sci. 855: 141-160 (2021) - [c79]David Tolpin, Yuan Zhou, Tom Rainforth, Hongseok Yang:
Probabilistic Programs with Stochastic Conditioning. ICML 2021: 10312-10323 - [i26]Gwonsoo Che, Hongseok Yang:
Meta-Learning an Inference Algorithm for Probabilistic Programs. CoRR abs/2103.00737 (2021) - [i25]Paul Jung, Hoil Lee, Jiho Lee, Hongseok Yang:
α-Stable convergence of heavy-tailed infinitely-wide neural networks. CoRR abs/2106.11064 (2021) - [i24]Hyungi Lee, Eunggu Yun, Hongseok Yang, Juho Lee:
Scale Mixtures of Neural Network Gaussian Processes. CoRR abs/2107.01408 (2021) - 2020
- [j24]Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang:
Towards verified stochastic variational inference for probabilistic programs. Proc. ACM Program. Lang. 4(POPL): 16:1-16:33 (2020) - [c78]Hyoungjin Lim, Gwonsoo Che, Wonyeol Lee, Hongseok Yang:
Differentiable Algorithm for Marginalising Changepoints. AAAI 2020: 4828-4835 - [c77]Geon-Hyeong Kim, Youngsoo Jang, Hongseok Yang, Kee-Eung Kim:
Variational Inference for Sequential Data with Future Likelihood Estimates. ICML 2020: 5296-5305 - [c76]Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth:
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. ICML 2020: 11534-11545 - [c75]Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang:
On Correctness of Automatic Differentiation for Non-Differentiable Functions. NeurIPS 2020 - [i23]David Tolpin, Yuan Zhou, Hongseok Yang:
Stochastically Differentiable Probabilistic Programs. CoRR abs/2003.00704 (2020) - [i22]Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang:
On Correctness of Automatic Differentiation for Non-Differentiable Functions. CoRR abs/2006.06903 (2020) - [i21]David Tolpin, Yuan Zhou, Hongseok Yang:
Probabilistic Programs with Stochastic Conditioning. CoRR abs/2010.00282 (2020) - [i20]David Tolpin, Yuan Zhou, Hongseok Yang:
Bayesian Policy Search for Stochastic Domains. CoRR abs/2010.00284 (2020)
2010 – 2019
- 2019
- [c74]Geon-hyeong Kim, Youngsoo Jang, Jongmin Lee, Wonseok Jeon, Hongseok Yang, Kee-Eung Kim:
Trust Region Sequential Variational Inference. ACML 2019: 1033-1048 - [c73]Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood:
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. AISTATS 2019: 148-157 - [c72]Kihong Heo, Hakjoo Oh, Hongseok Yang:
Resource-aware program analysis via online abstraction coarsening. ICSE 2019: 94-104 - [c71]Hongseok Yang:
Some Semantic Issues in Probabilistic Programming Languages (Invited Talk). FSCD 2019: 4:1-4:6 - [i19]Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn, Tom Rainforth, Hongseok Yang, Frank Wood:
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models. CoRR abs/1903.02482 (2019) - [i18]Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang:
Towards Verified Stochastic Variational Inference for Probabilistic Programs. CoRR abs/1907.08827 (2019) - [i17]Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth:
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support. CoRR abs/1910.13324 (2019) - [i16]Hyoungjin Lim, Gwonsoo Che, Wonyeol Lee, Hongseok Yang:
Differentiable Algorithm for Marginalising Changepoints. CoRR abs/1911.09839 (2019) - 2018
- [j23]Kihong Heo, Hakjoo Oh, Hongseok Yang:
Learning analysis strategies for octagon and context sensitivity from labeled data generated by static analyses. Formal Methods Syst. Des. 53(2): 189-220 (2018) - [j22]Adam Scibior, Ohad Kammar, Matthijs Vákár, Sam Staton, Hongseok Yang, Yufei Cai, Klaus Ostermann, Sean K. Moss, Chris Heunen, Zoubin Ghahramani:
Denotational validation of higher-order Bayesian inference. Proc. ACM Program. Lang. 2(POPL): 60:1-60:29 (2018) - [j21]Kihong Heo, Hakjoo Oh, Hongseok Yang, Kwangkeun Yi:
Adaptive Static Analysis via Learning with Bayesian Optimization. ACM Trans. Program. Lang. Syst. 40(4): 14:1-14:37 (2018) - [c70]Sam Staton, Dario Stein, Hongseok Yang, Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy:
The Beta-Bernoulli process and algebraic effects. ICALP 2018: 141:1-141:15 - [c69]Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington:
On Nesting Monte Carlo Estimators. ICML 2018: 4264-4273 - [c68]Wonyeol Lee, Hangyeol Yu, Hongseok Yang:
Reparameterization Gradient for Non-differentiable Models. NeurIPS 2018: 5558-5568 - [i15]Sam Staton, Dario Stein, Hongseok Yang, Nathanael L. Ackerman, Cameron E. Freer, Daniel M. Roy:
The Beta-Bernoulli process and algebraic effects. CoRR abs/1802.09598 (2018) - [i14]Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn, Hongseok Yang, Frank D. Wood:
Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs. CoRR abs/1804.03523 (2018) - [i13]Wonyeol Lee, Hangyeol Yu, Hongseok Yang:
Reparameterization Gradient for Non-differentiable Models. CoRR abs/1806.00176 (2018) - [i12]Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood:
An Introduction to Probabilistic Programming. CoRR abs/1809.10756 (2018) - 2017
- [j20]Kwonsoo Chae, Hakjoo Oh, Kihong Heo, Hongseok Yang:
Automatically generating features for learning program analysis heuristics for C-like languages. Proc. ACM Program. Lang. 1(OOPSLA): 101:1-101:25 (2017) - [c67]Hongseok Yang:
Probabilistic Programming (Invited Talk). CONCUR 2017: 3:1-3:1 - [c66]Andrea Cerone, Alexey Gotsman, Hongseok Yang:
Algebraic Laws for Weak Consistency. CONCUR 2017: 26:1-26:18 - [c65]Chris Heunen, Ohad Kammar, Sam Staton, Hongseok Yang:
A convenient category for higher-order probability theory. LICS 2017: 1-12 - [e2]Hongseok Yang:
Programming Languages and Systems - 26th European Symposium on Programming, ESOP 2017, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2017, Uppsala, Sweden, April 22-29, 2017, Proceedings. Lecture Notes in Computer Science 10201, Springer 2017, ISBN 978-3-662-54433-4 [contents] - [i11]Chris Heunen, Ohad Kammar, Sam Staton, Hongseok Yang:
A Convenient Category for Higher-Order Probability Theory. CoRR abs/1701.02547 (2017) - [i10]Andrea Cerone, Alexey Gotsman, Hongseok Yang:
Algebraic Laws for Weak Consistency. CoRR abs/1702.06028 (2017) - [i9]Adam Scibior, Ohad Kammar, Matthijs Vákár, Sam Staton, Hongseok Yang, Yufei Cai, Klaus Ostermann, Sean K. Moss, Chris Heunen, Zoubin Ghahramani:
Denotational validation of higher-order Bayesian inference. CoRR abs/1711.03219 (2017) - 2016
- [j19]Hila Peleg, Sharon Shoham, Eran Yahav, Hongseok Yang:
Symbolic automata for representing big code. Acta Informatica 53(4): 327-356 (2016) - [j18]Hakjoo Oh, Wonchan Lee, Kihong Heo, Hongseok Yang, Kwangkeun Yi:
Selective X-Sensitive Analysis Guided by Impact Pre-Analysis. ACM Trans. Program. Lang. Syst. 38(2): 6:1-6:45 (2016) - [c64]Mahsa Najafzadeh, Alexey Gotsman, Hongseok Yang, Carla Ferreira, Marc Shapiro:
The CISE tool: proving weakly-consistent applications correct. PaPoC@EuroSys 2016: 2:1-2:3 - [c63]David Tolpin, Jan-Willem van de Meent, Hongseok Yang, Frank D. Wood:
Design and Implementation of Probabilistic Programming Language Anglican. IFL 2016: 6:1-6:12 - [c62]Sam Staton, Hongseok Yang, Frank D. Wood, Chris Heunen, Ohad Kammar:
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints. LICS 2016: 525-534 - [c61]Hagit Attiya, Sebastian Burckhardt, Alexey Gotsman, Adam Morrison, Hongseok Yang, Marek Zawirski:
Specification and Complexity of Collaborative Text Editing. PODC 2016: 259-268 - [c60]Alexey Gotsman, Hongseok Yang, Carla Ferreira, Mahsa Najafzadeh, Marc Shapiro:
'Cause I'm strong enough: reasoning about consistency choices in distributed systems. POPL 2016: 371-384 - [c59]Radu Grigore, Hongseok Yang:
Abstraction refinement guided by a learnt probabilistic model. POPL 2016: 485-498 - [c58]Kihong Heo, Hakjoo Oh, Hongseok Yang:
Learning a Variable-Clustering Strategy for Octagon from Labeled Data Generated by a Static Analysis. SAS 2016: 237-256 - [i8]Sam Staton, Hongseok Yang, Chris Heunen, Ohad Kammar, Frank D. Wood:
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints. CoRR abs/1601.04943 (2016) - [i7]Mike Wu, Yura N. Perov, Frank D. Wood, Hongseok Yang:
Spreadsheet Probabilistic Programming. CoRR abs/1606.04216 (2016) - [i6]David Tolpin, Jan-Willem van de Meent, Hongseok Yang, Frank D. Wood:
Design and Implementation of Probabilistic Programming Language Anglican. CoRR abs/1608.05263 (2016) - [i5]Kwonsoo Chae, Hakjoo Oh, Kihong Heo, Hongseok Yang:
Automatically generating features for learning program analysis heuristics. CoRR abs/1612.09394 (2016) - 2015
- [c57]Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood:
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. AISTATS 2015 - [c56]Alexey Gotsman, Hongseok Yang:
Composite Replicated Data Types. ESOP 2015: 585-609 - [c55]Hakjoo Oh, Hongseok Yang, Kwangkeun Yi:
Learning a strategy for adapting a program analysis via bayesian optimisation. OOPSLA 2015: 572-588 - [c54]Ghila Castelnuovo, Mayur Naik, Noam Rinetzky, Mooly Sagiv, Hongseok Yang:
Modularity in Lattices: A Case Study on the Correspondence Between Top-Down and Bottom-Up Analysis. SAS 2015: 252-274 - [c53]Andrea Cerone, Alexey Gotsman, Hongseok Yang:
Transaction Chopping for Parallel Snapshot Isolation. DISC 2015: 388-404 - [i4]Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood:
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. CoRR abs/1501.06769 (2015) - [i3]Radu Grigore, Hongseok Yang:
Abstraction Refinement Guided by a Learnt Probabilistic Model. CoRR abs/1511.01874 (2015) - 2014
- [c52]Ravi Mangal, Mayur Naik, Hongseok Yang:
A Correspondence between Two Approaches to Interprocedural Analysis in the Presence of Join. ESOP 2014: 513-533 - [c51]Andrea Cerone, Alexey Gotsman, Hongseok Yang:
Parameterised Linearisability. ICALP (2) 2014: 98-109 - [c50]Xin Zhang, Ravi Mangal, Radu Grigore, Mayur Naik, Hongseok Yang:
On abstraction refinement for program analyses in Datalog. PLDI 2014: 239-248 - [c49]Xin Zhang, Ravi Mangal, Mayur Naik, Hongseok Yang:
Hybrid top-down and bottom-up interprocedural analysis. PLDI 2014: 249-258 - [c48]Hakjoo Oh, Wonchan Lee, Kihong Heo, Hongseok Yang, Kwangkeun Yi:
Selective context-sensitivity guided by impact pre-analysis. PLDI 2014: 475-484 - [c47]Sebastian Burckhardt, Alexey Gotsman, Hongseok Yang, Marek Zawirski:
Replicated data types: specification, verification, optimality. POPL 2014: 271-284 - 2013
- [j17]Alexey Gotsman, Hongseok Yang:
Linearizability with Ownership Transfer. Log. Methods Comput. Sci. 9(3) (2013) - [j16]Alexey Gotsman, Hongseok Yang:
Modular verification of preemptive OS kernels. J. Funct. Program. 23(4): 452-514 (2013) - [j15]Jan Schwinghammer, Lars Birkedal, François Pottier, Bernhard Reus, Kristian Støvring, Hongseok Yang:
A step-indexed Kripke model of hidden state. Math. Struct. Comput. Sci. 23(1): 1-54 (2013) - [c46]Alexey Gotsman, Noam Rinetzky, Hongseok Yang:
Verifying Concurrent Memory Reclamation Algorithms with Grace. ESOP 2013: 249-269 - [c45]Xin Zhang, Mayur Naik, Hongseok Yang:
Finding optimum abstractions in parametric dataflow analysis. PLDI 2013: 365-376 - [c44]Thomas Dinsdale-Young, Lars Birkedal, Philippa Gardner, Matthew J. Parkinson, Hongseok Yang:
Views: compositional reasoning for concurrent programs. POPL 2013: 287-300 - [c43]Hila Peleg, Sharon Shoham, Eran Yahav, Hongseok Yang:
Symbolic Automata for Static Specification Mining. SAS 2013: 63-83 - 2012
- [j14]Jacob Thamsborg, Lars Birkedal, Hongseok Yang:
Two for the Price of One: Lifting Separation Logic Assertions. Log. Methods Comput. Sci. 8(3) (2012) - [j13]Oukseh Lee, Hongseok Yang, Rasmus Petersen:
A divide-and-conquer approach for analysing overlaid data structures. Formal Methods Syst. Des. 41(1): 4-24 (2012) - [c42]Alexey Gotsman, Hongseok Yang:
Linearizability with Ownership Transfer. CONCUR 2012: 256-271 - [c41]Sebastian Burckhardt, Alexey Gotsman, Madanlal Musuvathi, Hongseok Yang:
Concurrent Library Correctness on the TSO Memory Model. ESOP 2012: 87-107 - [c40]Mayur Naik, Hongseok Yang, Ghila Castelnuovo, Mooly Sagiv:
Abstractions from tests. POPL 2012: 373-386 - [c39]Saswat Anand, Mayur Naik, Mary Jean Harrold, Hongseok Yang:
Automated concolic testing of smartphone apps. SIGSOFT FSE 2012: 59 - [c38]Alexey Gotsman, Madanlal Musuvathi, Hongseok Yang:
Show No Weakness: Sequentially Consistent Specifications of TSO Libraries. DISC 2012: 31-45 - 2011
- [j12]Jan Schwinghammer, Lars Birkedal, Bernhard Reus, Hongseok Yang:
Nested Hoare Triples and Frame Rules for Higher-order Store. Log. Methods Comput. Sci. 7(3) (2011) - [j11]Cristiano Calcagno, Dino Distefano, Peter W. O'Hearn, Hongseok Yang:
Compositional Shape Analysis by Means of Bi-Abduction. J. ACM 58(6): 26:1-26:66 (2011) - [c37]Oukseh Lee, Hongseok Yang, Rasmus Petersen:
Program Analysis for Overlaid Data Structures. CAV 2011: 592-608 - [c36]Alexey Gotsman, Hongseok Yang:
Liveness-Preserving Atomicity Abstraction. ICALP (2) 2011: 453-465 - [c35]Alexey Gotsman, Hongseok Yang:
Modular verification of preemptive OS kernels. ICFP 2011: 404-417 - [c34]Lars Birkedal, Bernhard Reus, Jan Schwinghammer, Kristian Støvring, Jacob Thamsborg, Hongseok Yang:
Step-indexed kripke models over recursive worlds. POPL 2011: 119-132 - [e1]Hongseok Yang:
Programming Languages and Systems - 9th Asian Symposium, APLAS 2011, Kenting, Taiwan, December 5-7, 2011. Proceedings. Lecture Notes in Computer Science 7078, Springer 2011, ISBN 978-3-642-25317-1 [contents] - 2010
- [j10]Ivana Filipovic, Peter W. O'Hearn, Noah Torp-Smith, Hongseok Yang:
Blaming the client: on data refinement in the presence of pointers. Formal Aspects Comput. 22(5): 547-583 (2010) - [j9]Ivana Filipovic, Peter W. O'Hearn, Noam Rinetzky, Hongseok Yang:
Abstraction for concurrent objects. Theor. Comput. Sci. 411(51-52): 4379-4398 (2010) - [c33]Aziem Chawdhary, Hongseok Yang:
Metric Spaces and Termination Analyses. APLAS 2010: 156-171 - [c32]Jan Schwinghammer, Hongseok Yang, Lars Birkedal, François Pottier, Bernhard Reus:
A Semantic Foundation for Hidden State. FoSSaCS 2010: 2-17
2000 – 2009
- 2009
- [j8]Peter W. O'Hearn, Hongseok Yang, John C. Reynolds:
Separation and information hiding. ACM Trans. Program. Lang. Syst. 31(3): 11:1-11:50 (2009) - [c31]Jan Schwinghammer, Lars Birkedal, Bernhard Reus, Hongseok Yang:
Nested Hoare Triples and Frame Rules for Higher-Order Store. CSL 2009: 440-454 - [c30]Hongseok Yang:
Automatic Verification of Heap-Manipulating Programs Using Separation Logic. CSR 2009: 25 - [c29]Ivana Filipovic, Peter W. O'Hearn, Noam Rinetzky, Hongseok Yang:
Abstraction for Concurrent Objects. ESOP 2009: 252-266 - [c28]Cristiano Calcagno, Dino Distefano, Peter W. O'Hearn, Hongseok Yang:
Compositional shape analysis by means of bi-abduction. POPL 2009: 289-300 - 2008
- [j7]Lars Birkedal, Hongseok Yang:
Relational Parametricity and Separation Logic. Log. Methods Comput. Sci. 4(2) (2008) - [c27]Hongseok Yang, Oukseh Lee, Josh Berdine, Cristiano Calcagno, Byron Cook, Dino Distefano, Peter W. O'Hear