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ORCIDMartin T. Vechev
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- affiliation: ETH Zürich, Department of Computer Science, Switzerland
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2020 – today
- 2023
[j18]Martin T. Vechev:
Technical Perspective: Beautiful Symbolic Abstractions for Safe and Secure Machine Learning. Commun. ACM 66(2): 104 (2023)- [j17]Mark Niklas Müller
, Marc Fischer, Robin Staab, Martin T. Vechev:
Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks. Proc. ACM Program. Lang. 7(PLDI): 786-810 (2023) - [j16]Luca Beurer-Kellner, Marc Fischer, Martin T. Vechev:
Prompting Is Programming: A Query Language for Large Language Models. Proc. ACM Program. Lang. 7(PLDI): 1946-1969 (2023) - [c142]Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev:
Human-Guided Fair Classification for Natural Language Processing. ICLR 2023 - [c141]Mark Niklas Müller, Franziska Eckert, Marc Fischer, Martin T. Vechev:
Certified Training: Small Boxes are All You Need. ICLR 2023 - [c140]Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Efficient Certified Training and Robustness Verification of Neural ODEs. ICLR 2023 - [c139]Nikola Jovanovic, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev:
FARE: Provably Fair Representation Learning with Practical Certificates. ICML 2023: 15401-15420 - [c138]Mark Vero, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev:
TabLeak: Tabular Data Leakage in Federated Learning. ICML 2023: 35051-35083 - [i54]Jingxuan He, Martin T. Vechev:
Controlling Large Language Models to Generate Secure and Vulnerable Code. CoRR abs/2302.05319 (2023) - [i53]Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Efficient Certified Training and Robustness Verification of Neural ODEs. CoRR abs/2303.05246 (2023) - [i52]Benjamin Bichsel, Maximilian Baader, Anouk Paradis, Martin T. Vechev:
Abstraqt: Analysis of Quantum Circuits via Abstract Stabilizer Simulation. CoRR abs/2304.00921 (2023) - [i51]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
TAPS: Connecting Certified and Adversarial Training. CoRR abs/2305.04574 (2023) - [i50]Niels Mündler, Jingxuan He, Slobodan Jenko, Martin T. Vechev:
Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation. CoRR abs/2305.15852 (2023) - [i49]Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin T. Vechev:
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. CoRR abs/2305.16272 (2023) - [i48]Kostadin Garov, Dimitar I. Dimitrov, Nikola Jovanovic, Martin T. Vechev:
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning. CoRR abs/2306.03013 (2023) - [i47]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Understanding Certified Training with Interval Bound Propagation. CoRR abs/2306.10426 (2023) - [i46]Mark Vero, Mislav Balunovic, Martin T. Vechev:
Programmable Synthetic Tabular Data Generation. CoRR abs/2307.03577 (2023) - 2022
- [j15]Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
PRIMA: general and precise neural network certification via scalable convex hull approximations. Proc. ACM Program. Lang. 6(POPL): 1-33 (2022) - [j14]Dimitar Iliev Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev:
Data Leakage in Federated Averaging. Trans. Mach. Learn. Res. 2022 (2022) - [j13]Nikola Jovanovic, Mislav Balunovic, Maximilian Baader, Martin T. Vechev:
On the Paradox of Certified Training. Trans. Mach. Learn. Res. 2022 (2022) - [j12]Matthew Mirman, Maximilian Baader, Martin T. Vechev:
The Fundamental Limits of Neural Networks for Interval Certified Robustness. Trans. Mach. Learn. Res. 2022 (2022) - [c137]Marc Fischer, Christian Sprecher, Dimitar I. Dimitrov, Gagandeep Singh, Martin T. Vechev:
Shared Certificates for Neural Network Verification. CAV (1) 2022: 127-148 - [c136]Nikola Jovanovic, Marc Fischer, Samuel Steffen, Martin T. Vechev:
Private and Reliable Neural Network Inference. CCS 2022: 1663-1677 - [c135]Samuel Steffen, Benjamin Bichsel, Martin T. Vechev:
Zapper: Smart Contracts with Data and Identity Privacy. CCS 2022: 2735-2749 - [c134]Momchil Peychev, Anian Ruoss, Mislav Balunovic, Maximilian Baader, Martin T. Vechev:
Latent Space Smoothing for Individually Fair Representations. ECCV (13) 2022: 535-554 - [c133]Mislav Balunovic, Dimitar Iliev Dimitrov, Robin Staab, Martin T. Vechev:
Bayesian Framework for Gradient Leakage. ICLR 2022 - [c132]Mislav Balunovic, Anian Ruoss, Martin T. Vechev:
Fair Normalizing Flows. ICLR 2022 - [c131]Dimitar Iliev Dimitrov, Gagandeep Singh, Timon Gehr, Martin T. Vechev:
Provably Robust Adversarial Examples. ICLR 2022 - [c130]Claudio Ferrari, Mark Niklas Müller, Nikola Jovanovic, Martin T. Vechev:
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound. ICLR 2022 - [c129]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Boosting Randomized Smoothing with Variance Reduced Classifiers. ICLR 2022 - [c128]Jingxuan He, Luca Beurer-Kellner, Martin T. Vechev:
On Distribution Shift in Learning-based Bug Detectors. ICML 2022: 8559-8580 - [c127]Mislav Balunovic, Dimitar I. Dimitrov, Nikola Jovanovic, Martin T. Vechev:
LAMP: Extracting Text from Gradients with Language Model Priors. NeurIPS 2022 - [c126]Luca Beurer-Kellner, Martin T. Vechev, Laurent Vanbever, Petar Velickovic:
Learning to Configure Computer Networks with Neural Algorithmic Reasoning. NeurIPS 2022 - [c125]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
(De-)Randomized Smoothing for Decision Stump Ensembles. NeurIPS 2022 - [c124]Pesho Ivanov, Benjamin Bichsel, Martin T. Vechev:
Fast and Optimal Sequence-to-Graph Alignment Guided by Seeds. RECOMB 2022: 306-325 - [c123]Samuel Steffen, Benjamin Bichsel, Roger Baumgartner, Martin T. Vechev:
ZeeStar: Private Smart Contracts by Homomorphic Encryption and Zero-knowledge Proofs. SP 2022: 179-197 - [i45]Dimitar I. Dimitrov, Mislav Balunovic, Nikola Jovanovic, Martin T. Vechev:
LAMP: Extracting Text from Gradients with Language Model Priors. CoRR abs/2202.08827 (2022) - [i44]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Robust and Accurate - Compositional Architectures for Randomized Smoothing. CoRR abs/2204.00487 (2022) - [i43]Jingxuan He, Luca Beurer-Kellner, Martin T. Vechev:
On Distribution Shift in Learning-based Bug Detectors. CoRR abs/2204.10049 (2022) - [i42]Claudio Ferrari, Mark Niklas Müller, Nikola Jovanovic, Martin T. Vechev:
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound. CoRR abs/2205.00263 (2022) - [i41]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
(De-)Randomized Smoothing for Decision Stump Ensembles. CoRR abs/2205.13909 (2022) - [i40]Dimitar I. Dimitrov, Mislav Balunovic, Nikola Konstantinov, Martin T. Vechev:
Data Leakage in Federated Averaging. CoRR abs/2206.12395 (2022) - [i39]Mark Vero, Mislav Balunovic, Dimitar I. Dimitrov, Martin T. Vechev:
Data Leakage in Tabular Federated Learning. CoRR abs/2210.01785 (2022) - [i38]Mark Niklas Müller, Franziska Eckert, Marc Fischer, Martin T. Vechev:
Certified Training: Small Boxes are All You Need. CoRR abs/2210.04871 (2022) - [i37]Nikola Jovanovic, Mislav Balunovic, Dimitar I. Dimitrov, Martin T. Vechev:
FARE: Provably Fair Representation Learning. CoRR abs/2210.07213 (2022) - [i36]Nikola Jovanovic, Marc Fischer, Samuel Steffen, Martin T. Vechev:
Private and Reliable Neural Network Inference. CoRR abs/2210.15614 (2022) - [i35]Luca Beurer-Kellner, Martin T. Vechev, Laurent Vanbever, Petar Velickovic:
Learning to Configure Computer Networks with Neural Algorithmic Reasoning. CoRR abs/2211.01980 (2022) - [i34]Luca Beurer-Kellner, Marc Fischer, Martin T. Vechev:
Prompting Is Programming: A Query Language For Large Language Models. CoRR abs/2212.06094 (2022) - [i33]Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev:
Human-Guided Fair Classification for Natural Language Processing. CoRR abs/2212.10154 (2022) - 2021
- [c122]Anian Ruoss, Maximilian Baader, Mislav Balunovic, Martin T. Vechev:
Efficient Certification of Spatial Robustness. AAAI 2021: 2504-2513 - [c121]Wonryong Ryou, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Marian Dan, Martin T. Vechev:
Scalable Polyhedral Verification of Recurrent Neural Networks. CAV (1) 2021: 225-248 - [c120]Jingxuan He, Gishor Sivanrupan, Petar Tsankov, Martin T. Vechev:
Learning to Explore Paths for Symbolic Execution. CCS 2021: 2526-2540 - [c119]Tobias Lorenz, Anian Ruoss, Mislav Balunovic, Gagandeep Singh, Martin T. Vechev:
Robustness Certification for Point Cloud Models. ICCV 2021: 7588-7598 - [c118]Mark Niklas Müller, Mislav Balunovic, Martin T. Vechev:
Certify or Predict: Boosting Certified Robustness with Compositional Architectures. ICLR 2021 - [c117]Berkay Berabi, Jingxuan He, Veselin Raychev, Martin T. Vechev:
TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer. ICML 2021: 780-791 - [c116]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Scalable Certified Segmentation via Randomized Smoothing. ICML 2021: 3340-3351 - [c115]Miguel Zamora, Momchil Peychev, Sehoon Ha, Martin T. Vechev, Stelian Coros:
PODS: Policy Optimization via Differentiable Simulation. ICML 2021: 7805-7817 - [c114]Christoph Müller, François Serre, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Scaling Polyhedral Neural Network Verification on GPUs. MLSys 2021 - [c113]Chengyuan Yao, Pavol Bielik, Petar Tsankov, Martin T. Vechev:
Automated Discovery of Adaptive Attacks on Adversarial Defenses. NeurIPS 2021: 26858-26870 - [c112]Rüdiger Birkner, Tobias Brodmann, Petar Tsankov, Laurent Vanbever, Martin T. Vechev:
Metha: Network Verifiers Need To Be Correct Too! NSDI 2021: 99-113 - [c111]Anouk Paradis, Benjamin Bichsel, Samuel Steffen, Martin T. Vechev:
Unqomp: synthesizing uncomputation in Quantum circuits. PLDI 2021: 222-236 - [c110]Jingxuan He, Cheng-Chun Lee, Veselin Raychev, Martin T. Vechev:
Learning to find naming issues with big code and small supervision. PLDI 2021: 296-311 - [c109]Gregory Bonaert, Dimitar I. Dimitrov, Maximilian Baader, Martin T. Vechev:
Fast and precise certification of transformers. PLDI 2021: 466-481 - [c108]Matthew Mirman, Alexander Hägele, Pavol Bielik, Timon Gehr, Martin T. Vechev:
Robustness certification with generative models. PLDI 2021: 1141-1154 - [c107]Benjamin Bichsel, Samuel Steffen, Ilija Bogunovic, Martin T. Vechev:
DP-Sniper: Black-Box Discovery of Differential Privacy Violations using Classifiers. SP 2021: 391-409 - [i32]Nikola Jovanovic, Mislav Balunovic, Maximilian Baader, Martin T. Vechev:
Certified Defenses: Why Tighter Relaxations May Hurt Training? CoRR abs/2102.06700 (2021) - [i31]Chengyuan Yao, Pavol Bielik, Petar Tsankov, Martin T. Vechev:
Automated Discovery of Adaptive Attacks on Adversarial Defenses. CoRR abs/2102.11860 (2021) - [i30]Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Precise Multi-Neuron Abstractions for Neural Network Certification. CoRR abs/2103.03638 (2021) - [i29]Tobias Lorenz, Anian Ruoss, Mislav Balunovic, Gagandeep Singh, Martin T. Vechev:
Robustness Certification for Point Cloud Models. CoRR abs/2103.16652 (2021) - [i28]Mislav Balunovic, Anian Ruoss, Martin T. Vechev:
Fair Normalizing Flows. CoRR abs/2106.05937 (2021) - [i27]Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Boosting Randomized Smoothing with Variance Reduced Classifiers. CoRR abs/2106.06946 (2021) - [i26]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Scalable Certified Segmentation via Randomized Smoothing. CoRR abs/2107.00228 (2021) - [i25]Christian Sprecher, Marc Fischer, Dimitar I. Dimitrov, Gagandeep Singh, Martin T. Vechev:
Shared Certificates for Neural Network Verification. CoRR abs/2109.00542 (2021) - [i24]Mark Niklas Müller, Robin Staab, Marc Fischer, Martin T. Vechev:
Effective Certification of Monotone Deep Equilibrium Models. CoRR abs/2110.08260 (2021) - [i23]Mislav Balunovic, Dimitar I. Dimitrov, Robin Staab, Martin T. Vechev:
Bayesian Framework for Gradient Leakage. CoRR abs/2111.04706 (2021) - [i22]Momchil Peychev, Anian Ruoss, Mislav Balunovic, Maximilian Baader, Martin T. Vechev:
Latent Space Smoothing for Individually Fair Representations. CoRR abs/2111.13650 (2021) - [i21]Matthew Mirman, Maximilian Baader, Martin T. Vechev:
The Fundamental Limits of Interval Arithmetic for Neural Networks. CoRR abs/2112.05235 (2021) - 2020
- [c106]Maximilian Baader, Matthew Mirman, Martin T. Vechev:
Universal Approximation with Certified Networks. ICLR 2020 - [c105]Mislav Balunovic, Martin T. Vechev:
Adversarial Training and Provable Defenses: Bridging the Gap. ICLR 2020 - [c104]Larissa Laich, Pavol Bielik, Martin T. Vechev:
Guiding Program Synthesis by Learning to Generate Examples. ICLR 2020 - [c103]Pavol Bielik, Martin T. Vechev:
Adversarial Robustness for Code. ICML 2020: 896-907 - [c102]Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin T. Vechev:
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. ICML 2020: 2356-2365 - [c101]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Certified Defense to Image Transformations via Randomized Smoothing. NeurIPS 2020 - [c100]Anian Ruoss, Mislav Balunovic, Marc Fischer, Martin T. Vechev:
Learning Certified Individually Fair Representations. NeurIPS 2020 - [c99]Rüdiger Birkner, Dana Drachsler-Cohen, Laurent Vanbever, Martin T. Vechev:
Config2Spec: Mining Network Specifications from Network Configurations. NSDI 2020: 969-984 - [c98]Benjamin Bichsel, Maximilian Baader, Timon Gehr, Martin T. Vechev:
Silq: a high-level quantum language with safe uncomputation and intuitive semantics. PLDI 2020: 286-300 - [c97]Timon Gehr, Samuel Steffen, Martin T. Vechev:
λPSI: exact inference for higher-order probabilistic programs. PLDI 2020: 883-897 - [c96]Jingxuan He, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Learning fast and precise numerical analysis. PLDI 2020: 1112-1127 - [c95]Pesho Ivanov, Benjamin Bichsel, Harun Mustafa, André Kahles, Gunnar Rätsch, Martin T. Vechev:
AStarix: Fast and Optimal Sequence-to-Graph Alignment. RECOMB 2020: 104-119 - [c94]Samuel Steffen, Timon Gehr, Petar Tsankov, Laurent Vanbever, Martin T. Vechev:
Probabilistic Verification of Network Configurations. SIGCOMM 2020: 750-764 - [c93]Anton Permenev, Dimitar K. Dimitrov, Petar Tsankov, Dana Drachsler-Cohen, Martin T. Vechev:
VerX: Safety Verification of Smart Contracts. SP 2020: 1661-1677 - [i20]Pavol Bielik, Martin T. Vechev:
Adversarial Robustness for Code. CoRR abs/2002.04694 (2020) - [i19]Anian Ruoss, Mislav Balunovic, Marc Fischer, Martin T. Vechev:
Learning Certified Individually Fair Representations. CoRR abs/2002.10312 (2020) - [i18]Marc Fischer, Maximilian Baader, Martin T. Vechev:
Certification of Semantic Perturbations via Randomized Smoothing. CoRR abs/2002.12463 (2020) - [i17]Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin T. Vechev:
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. CoRR abs/2003.03778 (2020) - [i16]Matthew Mirman, Timon Gehr, Martin T. Vechev:
Robustness Certification of Generative Models. CoRR abs/2004.14756 (2020) - [i15]Wonryong Ryou, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Marian Dan, Martin T. Vechev:
Fast and Effective Robustness Certification for Recurrent Neural Networks. CoRR abs/2005.13300 (2020) - [i14]Christoph Müller, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Neural Network Robustness Verification on GPUs. CoRR abs/2007.10868 (2020) - [i13]Dimitar I. Dimitrov, Gagandeep Singh, Timon Gehr, Martin T. Vechev:
Scalable Inference of Symbolic Adversarial Examples. CoRR abs/2007.12133 (2020) - [i12]Nick Baumann, Samuel Steffen, Benjamin Bichsel, Petar Tsankov, Martin T. Vechev:
zkay v0.2: Practical Data Privacy for Smart Contracts. CoRR abs/2009.01020 (2020) - [i11]Anian Ruoss, Maximilian Baader, Mislav Balunovic, Martin T. Vechev:
Efficient Certification of Spatial Robustness. CoRR abs/2009.09318 (2020)
2010 – 2019
- 2019
- [j11]Veselin Raychev, Martin T. Vechev, Andreas Krause:
Predicting program properties from 'big code'. Commun. ACM 62(3): 99-107 (2019) - [j10]Gagandeep Singh, Timon Gehr, Markus Püschel, Martin T. Vechev:
An abstract domain for certifying neural networks. Proc. ACM Program. Lang. 3(POPL): 41:1-41:30 (2019) - [c92]Jingxuan He, Mislav Balunovic, Nodar Ambroladze, Petar Tsankov, Martin T. Vechev:
Learning to Fuzz from Symbolic Execution with Application to Smart Contracts. CCS 2019: 531-548 - [c91]Samuel Steffen, Benjamin Bichsel, Mario Gersbach, Noa Melchior, Petar Tsankov, Martin T. Vechev:
zkay: Specifying and Enforcing Data Privacy in Smart Contracts. CCS 2019: 1759-1776 - [c90]Gagandeep Singh, Timon Gehr, Markus Püschel, Martin T. Vechev:
Boosting Robustness Certification of Neural Networks. ICLR (Poster) 2019 - [c89]Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin T. Vechev:
DL2: Training and Querying Neural Networks with Logic. ICML 2019: 1931-1941 - [c88]Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin T. Vechev:
Beyond the Single Neuron Convex Barrier for Neural Network Certification. NeurIPS 2019: 15072-15083 - [c87]Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin T. Vechev:
Certifying Geometric Robustness of Neural Networks. NeurIPS 2019: 15287-15297 - [c86]Jan Eberhardt, Samuel Steffen, Veselin Raychev, Martin T. Vechev:
Unsupervised learning of API aliasing specifications. PLDI 2019: 745-759 - [c85]Victor Chibotaru, Benjamin Bichsel, Veselin Raychev, Martin T. Vechev:
Scalable taint specification inference with big code. PLDI 2019: 760-774 - [i10]Matthew Mirman, Gagandeep Singh, Martin T. Vechev:
A Provable Defense for Deep Residual Networks. CoRR abs/1903.12519 (2019) - [i9]Maximilian Baader, Matthew Mirman, Martin T. Vechev:
Universal Approximation with Certified Networks. CoRR abs/1909.13846 (2019) - [i8]Marc Fischer, Matthew Mirman, Steven Stalder, Martin T. Vechev:
Online Robustness Training for Deep Reinforcement Learning. CoRR abs/1911.00887 (2019) - [i7]Philippe Schlattner, Pavol Bielik, Martin T. Vechev:
Learning to Infer User Interface Attributes from Images. CoRR abs/1912.13243 (2019) - 2018
- [j9]Pavol Bielik, Marc Fischer, Martin T. Vechev:
Robust relational layout synthesis from examples for Android. Proc. ACM Program. Lang. 2(OOPSLA): 156:1-156:29 (2018) - [j8]Gagandeep Singh, Markus Püschel, Martin T. Vechev:
A practical construction for decomposing numerical abstract domains. Proc. ACM Program. Lang. 2(POPL): 55:1-55:28 (2018) - [j7]Dimitar K. Dimitrov, Martin T. Vechev, Vivek Sarkar:
Race Detection in Two Dimensions. ACM Trans. Parallel Comput. 4(4): 19:1-19:22 (2018) - [c84]Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Fast Numerical Program Analysis with Reinforcement Learning. CAV (1) 2018: 211-229 - [c83]Petar Tsankov, Andrei Marian Dan, Dana Drachsler-Cohen, Arthur Gervais, Florian Bünzli, Martin T. Vechev:
Securify: Practical Security Analysis of Smart Contracts. CCS 2018: 67-82 - [c82]Benjamin Bichsel, Timon Gehr, Dana Drachsler-Cohen, Petar Tsankov, Martin T. Vechev:
DP-Finder: Finding Differential Privacy Violations by Sampling and Optimization. CCS 2018: 508-524 - [c81]Jingxuan He, Pesho Ivanov, Petar Tsankov, Veselin Raychev, Martin T. Vechev:
Debin: Predicting Debug Information in Stripped Binaries. CCS 2018: 1667-1680 - [c80]Benjamin Bichsel, Timon Gehr, Martin T. Vechev:
Fine-Grained Semantics for Probabilistic Programs. ESOP 2018: 145-185 - [c79]