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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
- 2024
[j22]Anouk Paradis, Jasper Dekoninck
, Benjamin Bichsel, Martin T. Vechev:
Synthetiq: Fast and Versatile Quantum Circuit Synthesis. Proc. ACM Program. Lang. 8(OOPSLA1): 55-82 (2024)- [j21]Hristo Venev, Timon Gehr, Dimitar Dimitrov, Martin T. Vechev:
Modular Synthesis of Efficient Quantum Uncomputation. Proc. ACM Program. Lang. 8(OOPSLA2): 2097-2124 (2024) - [j20]Anouk Paradis, Benjamin Bichsel, Martin T. Vechev:
Reqomp: Space-constrained Uncomputation for Quantum Circuits. Quantum 8: 1258 (2024) - [c160]Anton Alexandrov, Veselin Raychev, Mark Mueller, Ce Zhang, Martin T. Vechev, Kristina Toutanova:
Mitigating Catastrophic Forgetting in Language Transfer via Model Merging. EMNLP (Findings) 2024: 17167-17186 - [c159]Maximilian Baader, Mark Niklas Müller, Yuhao Mao, Martin T. Vechev:
Expressivity of ReLU-Networks under Convex Relaxations. ICLR 2024 - [c158]Jasper Dekoninck, Marc Fischer, Luca Beurer-Kellner, Martin T. Vechev:
Controlled Text Generation via Language Model Arithmetic. ICLR 2024 - [c157]Kostadin Garov, Dimitar Iliev Dimitrov, Nikola Jovanovic, Martin T. Vechev:
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning. ICLR 2024 - [c156]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Understanding Certified Training with Interval Bound Propagation. ICLR 2024 - [c155]Niels Mündler, Jingxuan He, Slobodan Jenko, Martin T. Vechev:
Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation. ICLR 2024 - [c154]Robin Staab, Mark Vero, Mislav Balunovic, Martin T. Vechev:
Beyond Memorization: Violating Privacy via Inference with Large Language Models. ICLR 2024 - [c153]Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Watermark Stealing in Large Language Models. ICML 2024 - [c152]Luca Beurer-Kellner, Marc Fischer, Martin T. Vechev:
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation. ICML 2024 - [c151]Luca Beurer-Kellner, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Prompt Sketching for Large Language Models. ICML 2024 - [c150]Jingxuan He, Mark Vero, Gabriela Krasnopolska, Martin T. Vechev:
Instruction Tuning for Secure Code Generation. ICML 2024 - [c149]Mark Vero, Mislav Balunovic, Martin T. Vechev:
CuTS: Customizable Tabular Synthetic Data Generation. ICML 2024 - [c148]Robin Staab, Nikola Jovanovic, Mislav Balunovic, Martin T. Vechev:
From Principle to Practice: Vertical Data Minimization for Machine Learning. SP 2024: 4733-4752 - [i88]Momchil Peychev, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Automated Classification of Model Errors on ImageNet. CoRR abs/2401.02430 (2024) - [i87]Jasper Dekoninck, Mark Niklas Müller, Maximilian Baader, Marc Fischer, Martin T. Vechev:
Evading Data Contamination Detection for Language Models is (too) Easy. CoRR abs/2402.02823 (2024) - [i86]Jingxuan He, Mark Vero, Gabriela Krasnopolska, Martin T. Vechev:
Instruction Tuning for Secure Code Generation. CoRR abs/2402.09497 (2024) - [i85]Berkay Berabi, Alexey Gronskiy, Veselin Raychev, Gishor Sivanrupan, Victor Chibotaru, Martin T. Vechev:
DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models. CoRR abs/2402.13291 (2024) - [i84]Robin Staab, Mark Vero, Mislav Balunovic, Martin T. Vechev:
Large Language Models are Advanced Anonymizers. CoRR abs/2402.13846 (2024) - [i83]Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Watermark Stealing in Large Language Models. CoRR abs/2402.19361 (2024) - [i82]Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev:
SPEAR: Exact Gradient Inversion of Batches in Federated Learning. CoRR abs/2403.03945 (2024) - [i81]Luca Beurer-Kellner, Marc Fischer, Martin T. Vechev:
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation. CoRR abs/2403.06988 (2024) - [i80]Stefan Balauca, Mark Niklas Müller, Yuhao Mao, Maximilian Baader, Marc Fischer, Martin T. Vechev:
Overcoming the Paradox of Certified Training with Gaussian Smoothing. CoRR abs/2403.07095 (2024) - [i79]Batuhan Tömekçe, Mark Vero, Robin Staab, Martin T. Vechev:
Private Attribute Inference from Images with Vision-Language Models. CoRR abs/2404.10618 (2024) - [i78]Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev:
DAGER: Exact Gradient Inversion for Large Language Models. CoRR abs/2405.15586 (2024) - [i77]Jasper Dekoninck, Mark Niklas Müller, Martin T. Vechev:
ConStat: Performance-Based Contamination Detection in Large Language Models. CoRR abs/2405.16281 (2024) - [i76]Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He, Martin T. Vechev:
Exploiting LLM Quantization. CoRR abs/2405.18137 (2024) - [i75]Angéline Pouget, Nikola Jovanovic, Mark Vero, Robin Staab, Martin T. Vechev:
Back to the Drawing Board for Fair Representation Learning. CoRR abs/2405.18161 (2024) - [i74]Thibaud Gloaguen, Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Black-Box Detection of Language Model Watermarks. CoRR abs/2405.20777 (2024) - [i73]Yuhao Mao, Stefan Balauca, Martin T. Vechev:
CTBENCH: A Library and Benchmark for Certified Training. CoRR abs/2406.04848 (2024) - [i72]Hanna Yukhymenko, Robin Staab, Mark Vero, Martin T. Vechev:
A Synthetic Dataset for Personal Attribute Inference. CoRR abs/2406.07217 (2024) - [i71]Niels Mündler, Mark Niklas Müller, Jingxuan He, Martin T. Vechev:
Code Agents are State of the Art Software Testers. CoRR abs/2406.12952 (2024) - [i70]Hristo Venev, Timon Gehr, Dimitar Dimitrov, Martin T. Vechev:
Modular Synthesis of Efficient Quantum Uncomputation. CoRR abs/2406.14227 (2024) - [i69]Anton Alexandrov, Veselin Raychev, Mark Niklas Müller, Ce Zhang, Martin T. Vechev, Kristina Toutanova:
Mitigating Catastrophic Forgetting in Language Transfer via Model Merging. CoRR abs/2407.08699 (2024) - [i68]Slobodan Jenko, Jingxuan He, Niels Mündler, Mark Vero, Martin T. Vechev:
Practical Attacks against Black-box Code Completion Engines. CoRR abs/2408.02509 (2024) - [i67]Jasper Dekoninck, Maximilian Baader, Martin T. Vechev:
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation. CoRR abs/2409.00696 (2024) - [i66]Mert Ünsal, Timon Gehr, Martin T. Vechev:
AlphaIntegrator: Transformer Action Search for Symbolic Integration Proofs. CoRR abs/2410.02666 (2024) - [i65]Thibaud Gloaguen, Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Discovering Clues of Spoofed LM Watermarks. CoRR abs/2410.02693 (2024) - [i64]Nikola Jovanovic, Robin Staab, Maximilian Baader, Martin T. Vechev:
Ward: Provable RAG Dataset Inference via LLM Watermarks. CoRR abs/2410.03537 (2024) - [i63]Yuhao Mao, Yani Zhang, Martin T. Vechev:
Multi-Neuron Unleashes Expressivity of ReLU Networks Under Convex Relaxation. CoRR abs/2410.06816 (2024) - [i62]Chenhao Sun, Yuhao Mao, Mark Niklas Müller, Martin T. Vechev:
Average Certified Radius is a Poor Metric for Randomized Smoothing. CoRR abs/2410.06895 (2024) - [i61]Philipp Guldimann, Alexander Spiridonov, Robin Staab, Nikola Jovanovic, Mark Vero, Velko Vechev, Anna Gueorguieva, Mislav Balunovic, Nikola Konstantinov, Pavol Bielik, Petar Tsankov, Martin T. Vechev:
COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act. CoRR abs/2410.07959 (2024) - [i60]Jasper Dekoninck, Maximilian Baader, Martin T. Vechev:
A Unified Approach to Routing and Cascading for LLMs. CoRR abs/2410.10347 (2024) - 2023
- [j19]Martin T. Vechev:
Technical Perspective: Beautiful Symbolic Abstractions for Safe and Secure Machine Learning. Commun. ACM 66(2): 104 (2023) - [j18]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) - [j17]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) - [j16]Benjamin Bichsel, Anouk Paradis, Maximilian Baader, Martin T. Vechev:
Abstraqt: Analysis of Quantum Circuits via Abstract Stabilizer Simulation. Quantum 7: 1185 (2023) - [c147]Jingxuan He, Martin T. Vechev:
Large Language Models for Code: Security Hardening and Adversarial Testing. CCS 2023: 1865-1879 - [c146]Johan Lokna, Anouk Paradis, Dimitar I. Dimitrov, Martin T. Vechev:
Group and Attack: Auditing Differential Privacy. CCS 2023: 1905-1918 - [c145]Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev:
Human-Guided Fair Classification for Natural Language Processing. ICLR 2023 - [c144]Mark Niklas Müller, Franziska Eckert, Marc Fischer, Martin T. Vechev:
Certified Training: Small Boxes are All You Need. ICLR 2023 - [c143]Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Efficient Certified Training and Robustness Verification of Neural ODEs. ICLR 2023 - [c142]Nikola Jovanovic, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev:
FARE: Provably Fair Representation Learning with Practical Certificates. ICML 2023: 15401-15420 - [c141]Mark Vero, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev:
TabLeak: Tabular Data Leakage in Federated Learning. ICML 2023: 35051-35083 - [c140]Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin T. Vechev:
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. NeurIPS 2023 - [c139]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Connecting Certified and Adversarial Training. NeurIPS 2023 - [c138]Momchil Peychev, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Automated Classification of Model Errors on ImageNet. NeurIPS 2023 - [d1]Mark Niklas Müller, Marc Fischer, Robin Staab, Martin T. Vechev:
Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks - Artifact. Zenodo, 2023
- [i59]Jingxuan He, Martin T. Vechev:
Controlling Large Language Models to Generate Secure and Vulnerable Code. CoRR abs/2302.05319 (2023) - [i58]Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Efficient Certified Training and Robustness Verification of Neural ODEs. CoRR abs/2303.05246 (2023) - [i57]Benjamin Bichsel, Maximilian Baader, Anouk Paradis, Martin T. Vechev:
Abstraqt: Analysis of Quantum Circuits via Abstract Stabilizer Simulation. CoRR abs/2304.00921 (2023) - [i56]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
TAPS: Connecting Certified and Adversarial Training. CoRR abs/2305.04574 (2023) - [i55]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) - [i54]Florian E. Dorner, Nikola Konstantinov, Georgi Pashaliev, Martin T. Vechev:
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. CoRR abs/2305.16272 (2023) - [i53]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) - [i52]Yuhao Mao, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Understanding Certified Training with Interval Bound Propagation. CoRR abs/2306.10426 (2023) - [i51]Mark Vero, Mislav Balunovic, Martin T. Vechev:
Programmable Synthetic Tabular Data Generation. CoRR abs/2307.03577 (2023) - [i50]Robin Staab, Mark Vero, Mislav Balunovic, Martin T. Vechev:
Beyond Memorization: Violating Privacy Via Inference with Large Language Models. CoRR abs/2310.07298 (2023) - [i49]Maximilian Baader, Mark Niklas Müller, Yuhao Mao, Martin T. Vechev:
Expressivity of ReLU-Networks under Convex Relaxations. CoRR abs/2311.04015 (2023) - [i48]Luca Beurer-Kellner, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Prompt Sketching for Large Language Models. CoRR abs/2311.04954 (2023) - [i47]Robin Staab, Nikola Jovanovic, Mislav Balunovic, Martin T. Vechev:
From Principle to Practice: Vertical Data Minimization for Machine Learning. CoRR abs/2311.10500 (2023) - [i46]Jasper Dekoninck, Marc Fischer, Luca Beurer-Kellner, Martin T. Vechev:
Controlled Text Generation via Language Model Arithmetic. CoRR abs/2311.14479 (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]Matthew Mirman, Dimitar K. Dimitrov, Pavle Djordjevic, Timon Gehr, Martin T. Vechev:
Training Neural Machines with Trace-Based Supervision. ICML 2018: 3566-3574 - [c78]Matthew Mirman, Timon Gehr, Martin T. Vechev:
Differentiable Abstract Interpretation for Provably Robust Neural Networks. ICML 2018: 3575-3583 - [c77]Mislav Balunovic, Pavol Bielik, Martin T. Vechev:
Learning to Solve SMT Formulas. NeurIPS 2018: 10338-10349 - [c76]Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin T. Vechev:
Fast and Effective Robustness Certification. NeurIPS 2018: 10825-10836 - [c75]Ahmed El-Hassany, Petar Tsankov, Laurent Vanbever, Martin T. Vechev:
NetComplete: Practical Network-Wide Configuration Synthesis with Autocompletion. NSDI 2018: 579-594 - [c74]Rüdiger Birkner, Dana Drachsler-Cohen, Laurent Vanbever, Martin T. Vechev:
Net2Text: Query-Guided Summarization of Network Forwarding Behaviors. NSDI 2018: 609-623 - [c73]Lucas Brutschy, Dimitar K. Dimitrov, Peter Müller, Martin T. Vechev:
Static serializability analysis for causal consistency. PLDI 2018: 90-104 - [c72]Rumen Paletov, Petar Tsankov, Veselin Raychev, Martin T. Vechev:
Inferring crypto API rules from code changes. PLDI 2018: 450-464 - [c71]Marco F. Cusumano-Towner, Benjamin Bichsel, Timon Gehr, Martin T. Vechev, Vikash K. Mansinghka:
Incremental inference for probabilistic programs. PLDI 2018: 571-585 - [c70]Timon Gehr, Sasa Misailovic, Petar Tsankov, Laurent Vanbever, Pascal Wiesmann, Martin T. Vechev:
Bayonet: probabilistic inference for networks. PLDI 2018: 586-602 - [c69]Dana Drachsler-Cohen, Martin T. Vechev, Eran Yahav:
Practical concurrent traversals in search trees. PPoPP 2018: 207-218 - [c68]Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, Martin T. Vechev:
AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation. IEEE Symposium on Security and Privacy 2018: 3-18 - [c67]Roland Meier, Petar Tsankov, Vincent Lenders, Laurent Vanbever, Martin T. Vechev:
NetHide: Secure and Practical Network Topology Obfuscation. USENIX Security Symposium 2018: 693-709 - [c66]Cedric Baumann, Andrei Marian Dan, Yuri Meshman, Torsten Hoefler, Martin T. Vechev:
Automatic Verification of RMA Programs via Abstraction Extrapolation. VMCAI 2018: 47-70 - [i6]Petar Tsankov, Andrei Marian Dan, Dana Drachsler-Cohen, Arthur Gervais, Florian Buenzli, Martin T. Vechev:
Securify: Practical Security Analysis of Smart Contracts. CoRR abs/1806.01143 (2018) - 2017
- [j6]Andrei Marian Dan, Yuri Meshman, Martin T. Vechev, Eran Yahav:
Effective abstractions for verification under relaxed memory models. Comput. Lang. Syst. Struct. 47: 62-76 (2017) - [c65]Pavol Bielik, Veselin Raychev, Martin T. Vechev:
Learning a Static Analyzer from Data. CAV (1) 2017: 233-253 - [c64]Ahmed El-Hassany, Petar Tsankov, Laurent Vanbever, Martin T. Vechev:
Network-Wide Configuration Synthesis. CAV (2) 2017: 261-281 - [c63]Andrei Marian Dan, Manu Sridharan, Satish Chandra, Jean-Baptiste Jeannin, Martin T. Vechev:
Finding Fix Locations for CFL-Reachability Analyses via Minimum Cuts. CAV (2) 2017: 521-541 - [c62]Martin Kucera, Petar Tsankov, Timon Gehr, Marco Guarnieri, Martin T. Vechev:
Synthesis of Probabilistic Privacy Enforcement. CCS 2017: 391-408 - [c61]Nader H. Bshouty, Dana Drachsler-Cohen, Martin T. Vechev, Eran Yahav:
Learning Disjunctions of Predicates. COLT 2017: 346-369 - [c60]Pavol Bielik, Veselin Raychev, Martin T. Vechev:
Program Synthesis for Character Level Language Modeling. ICLR (Poster) 2017 - [c59]Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Fast polyhedra abstract domain. POPL 2017: 46-59 - [c58]Lucas Brutschy, Dimitar K. Dimitrov, Peter Müller, Martin T. Vechev:
Serializability for eventual consistency: criterion, analysis, and applications. POPL 2017: 458-472 - [c57]Roman May, Ahmed El-Hassany, Laurent Vanbever, Martin T. Vechev:
BigBug: Practical Concurrency Analysis for SDN. SOSR 2017: 88-94 - [e2]Albert Cohen, Martin T. Vechev:
Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2017, Barcelona, Spain, June 18-23, 2017. ACM 2017, ISBN 978-1-4503-4988-8 [contents] - [i5]Nader H. Bshouty, Dana Drachsler-Cohen, Martin T. Vechev, Eran Yahav:
Learning Disjunctions of Predicates. CoRR abs/1706.05070 (2017) - 2016
- [j5]Martin T. Vechev, Eran Yahav:
Programming with "Big Code". Found. Trends Program. Lang. 3(4): 231-284 (2016) - [c56]Petar Tsankov, Marco Pistoia, Omer Tripp, Martin T. Vechev, Pietro Ferrara:
FASE: functionality-aware security enforcement. ACSAC 2016: 471-483 - [c55]Timon Gehr, Sasa Misailovic, Martin T. Vechev:
PSI: Exact Symbolic Inference for Probabilistic Programs. CAV (1) 2016: 62-83 - [c54]Benjamin Bichsel, Veselin Raychev, Petar Tsankov, Martin T. Vechev:
Statistical Deobfuscation of Android Applications. CCS 2016: 343-355 - [c53]Pavol Bielik, Veselin Raychev, Martin T. Vechev:
PHOG: Probabilistic Model for Code. ICML 2016: 2933-2942 - [c52]Andrei Marian Dan, Patrick Lam, Torsten Hoefler, Martin T. Vechev:
Modeling and analysis of remote memory access programming. OOPSLA 2016: 129-144 - [c51]Veselin Raychev, Pavol Bielik, Martin T. Vechev:
Probabilistic model for code with decision trees. OOPSLA 2016: 731-747 - [c50]Ahmed El-Hassany, Jeremie Miserez, Pavol Bielik, Laurent Vanbever, Martin T. Vechev:
SDNRacer: concurrency analysis for software-defined networks. PLDI 2016: 402-415 - [c49]Veselin Raychev, Pavol Bielik, Martin T. Vechev, Andreas Krause:
Learning programs from noisy data. POPL 2016: 761-774 - [i4]Dana Drachsler-Cohen, Martin T. Vechev, Eran Yahav:
Optimal Learning of Specifications from Examples. CoRR abs/1608.00089 (2016) - [i3]Pavol Bielik, Veselin Raychev, Martin T. Vechev:
Learning a Static Analyzer from Data. CoRR abs/1611.01752 (2016) - [i2]Ahmed El-Hassany, Petar Tsankov, Laurent Vanbever, Martin T. Vechev:
Network-wide Configuration Synthesis. CoRR abs/1611.02537 (2016) - 2015
- [c48]Timon Gehr, Dimitar K. Dimitrov, Martin T. Vechev:
Learning Commutativity Specifications. CAV (1) 2015: 307-323 - [c47]Jibin Ou, Martin T. Vechev, Otmar Hilliges:
An Interactive System for Data Structure Development. CHI 2015: 3053-3062 - [c46]Casper Svenning Jensen, Anders Møller, Veselin Raychev, Dimitar K. Dimitrov, Martin T. Vechev:
Stateless model checking of event-driven applications. OOPSLA 2015: 57-73 - [c45]Pavol Bielik, Veselin Raychev, Martin T. Vechev:
Scalable race detection for Android applications. OOPSLA 2015: 332-348 - [c44]Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Making numerical program analysis fast. PLDI 2015: 303-313 - [c43]Veselin Raychev, Martin T. Vechev, Andreas Krause:
Predicting Program Properties from "Big Code". POPL 2015: 111-124 - [c42]Pavol Bielik, Veselin Raychev, Martin T. Vechev:
Programming with "Big Code": Lessons, Techniques and Applications. SNAPL 2015: 41-50 - [c41]Jeremie Miserez, Pavol Bielik, Ahmed El-Hassany, Laurent Vanbever, Martin T. Vechev:
SDNRacer: detecting concurrency violations in software-defined networks. SOSR 2015: 22:1-22:7 - [c40]Dimitar K. Dimitrov, Martin T. Vechev, Vivek Sarkar:
Race Detection in Two Dimensions. SPAA 2015: 101-110 - [c39]Andrei Marian Dan, Yuri Meshman, Martin T. Vechev, Eran Yahav:
Effective Abstractions for Verification under Relaxed Memory Models. VMCAI 2015: 449-466 - [i1]William W. Cohen, Charles Sutton, Martin T. Vechev:
Programming with "Big Code" (Dagstuhl Seminar 15472). Dagstuhl Reports 5(11): 90-102 (2015) - 2014
- [c38]Ohad Shacham, Eran Yahav, Guy Golan-Gueta, Alex Aiken, Nathan Grasso Bronson, Mooly Sagiv, Martin T. Vechev:
Verifying atomicity via data independence. ISSTA 2014: 26-36 - [c37]Svetoslav Karaivanov, Veselin Raychev, Martin T. Vechev:
Phrase-Based Statistical Translation of Programming Languages. Onward! 2014: 173-184 - [c36]Dimitar K. Dimitrov, Veselin Raychev, Martin T. Vechev, Eric Koskinen:
Commutativity race detection. PLDI 2014: 305-315 - [c35]Veselin Raychev, Martin T. Vechev, Eran Yahav:
Code completion with statistical language models. PLDI 2014: 419-428 - [c34]Dana Drachsler, Martin T. Vechev, Eran Yahav:
Practical concurrent binary search trees via logical ordering. PPoPP 2014: 343-356 - [c33]Yuri Meshman, Andrei Marian Dan, Martin T. Vechev, Eran Yahav:
Synthesis of Memory Fences via Refinement Propagation. SAS 2014: 237-252 - 2013
- [j4]Martin T. Vechev, Eran Yahav, Greta Yorsh:
Abstraction-guided synthesis of synchronization. Int. J. Softw. Tools Technol. Transf. 15(5-6): 413-431 (2013) - [c32]Veselin Raychev, Martin T. Vechev, Manu Sridharan:
Effective race detection for event-driven programs. OOPSLA 2013: 151-166 - [c31]Veselin Raychev, Max Schäfer, Manu Sridharan, Martin T. Vechev:
Refactoring with synthesis. OOPSLA 2013: 339-354 - [c30]Andrei Marian Dan, Yuri Meshman, Martin T. Vechev, Eran Yahav:
Predicate Abstraction for Relaxed Memory Models. SAS 2013: 84-104 - [c29]Veselin Raychev, Martin T. Vechev, Eran Yahav:
Automatic Synthesis of Deterministic Concurrency. SAS 2013: 283-303 - 2012
- [j3]Raghavan Raman, Jisheng Zhao, Vivek Sarkar, Martin T. Vechev, Eran Yahav:
Efficient data race detection for async-finish parallelism. Formal Methods Syst. Des. 41(3): 321-347 (2012) - [j2]Michael Kuperstein, Martin T. Vechev, Eran Yahav:
Automatic inference of memory fences. SIGACT News 43(2): 108-123 (2012) - [c28]Boris Petrov, Martin T. Vechev, Manu Sridharan, Julian Dolby:
Race detection for web applications. PLDI 2012: 251-262 - [c27]Feng Liu, Nayden Nedev, Nedyalko Prisadnikov, Martin T. Vechev, Eran Yahav:
Dynamic synthesis for relaxed memory models. PLDI 2012: 429-440 - [c26]Raghavan Raman, Jisheng Zhao, Vivek Sarkar, Martin T. Vechev, Eran Yahav:
Scalable and precise dynamic datarace detection for structured parallelism. PLDI 2012: 531-542 - [e1]Martin T. Vechev, Kathryn S. McKinley:
International Symposium on Memory Management, ISMM '12, Beijing, China, June 15-16, 2012. ACM 2012, ISBN 978-1-4503-1350-6 [contents] - 2011
- [j1]Matthew Arnold, Martin T. Vechev, Eran Yahav:
QVM: An Efficient Runtime for Detecting Defects in Deployed Systems. ACM Trans. Softw. Eng. Methodol. 21(1): 2:1-2:35 (2011) - [c25]Ohad Shacham, Nathan Grasso Bronson, Alex Aiken, Mooly Sagiv, Martin T. Vechev, Eran Yahav:
Testing atomicity of composed concurrent operations. OOPSLA 2011: 51-64 - [c24]Arun Raman, Greta Yorsh, Martin T. Vechev, Eran Yahav:
Sprint: speculative prefetching of remote data. OOPSLA 2011: 259-274 - [c23]Edward Aftandilian, Samuel Z. Guyer, Martin T. Vechev, Eran Yahav:
Asynchronous assertions. OOPSLA 2011: 275-288 - [c22]Michael Kuperstein, Martin T. Vechev, Eran Yahav:
Partial-coherence abstractions for relaxed memory models. PLDI 2011: 187-198 - [c21]Hagit Attiya, Rachid Guerraoui, Danny Hendler, Petr Kuznetsov, Maged M. Michael, Martin T. Vechev:
Laws of order: expensive synchronization in concurrent algorithms cannot be eliminated. POPL 2011: 487-498 - 2010
- [c20]Martin T. Vechev:
Computer-aided construction of concurrent systems. CompSysTech 2010: 19-24 - [c19]Michael Kuperstein, Martin T. Vechev, Eran Yahav:
Automatic inference of memory fences. FMCAD 2010: 111-119 - [c18]Martin T. Vechev, Eran Yahav, Greta Yorsh:
PHALANX: parallel checking of expressive heap assertions. ISMM 2010: 41-50 - [c17]Peter W. O'Hearn, Noam Rinetzky, Martin T. Vechev, Eran Yahav, Greta Yorsh:
Verifying linearizability with hindsight. PODC 2010: 85-94 - [c16]Martin T. Vechev, Eran Yahav, Greta Yorsh:
Abstraction-guided synthesis of synchronization. POPL 2010: 327-338 - [c15]Raghavan Raman, Jisheng Zhao, Vivek Sarkar, Martin T. Vechev, Eran Yahav:
Efficient Data Race Detection for Async-Finish Parallelism. RV 2010: 368-383 - [c14]Martin T. Vechev, Eran Yahav, Raghavan Raman, Vivek Sarkar:
Automatic Verification of Determinism for Structured Parallel Programs. SAS 2010: 455-471
2000 – 2009
- 2009
- [c13]Ohad Shacham, Martin T. Vechev, Eran Yahav:
Chameleon: adaptive selection of collections. PLDI 2009: 408-418 - [c12]Maged M. Michael, Martin T. Vechev, Vijay A. Saraswat:
Idempotent work stealing. PPoPP 2009: 45-54 - [c11]Martin T. Vechev, Eran Yahav, Greta Yorsh:
Experience with Model Checking Linearizability. SPIN 2009: 261-278 - [c10]Martin T. Vechev, Eran Yahav, Greta Yorsh:
Inferring Synchronization under Limited Observability. TACAS 2009: 139-154 - 2008
- [c9]Matthew Arnold, Martin T. Vechev, Eran Yahav:
QVM: an efficient runtime for detecting defects in deployed systems. OOPSLA 2008: 143-162 - [c8]Martin T. Vechev, Eran Yahav:
Deriving linearizable fine-grained concurrent objects. PLDI 2008: 125-135 - 2007
- [b1]Martin T. Vechev:
Derivation and evaluation of concurrent collectors. University of Cambridge, UK, 2007 - [c7]Martin T. Vechev, Eran Yahav, David F. Bacon, Noam Rinetzky:
CGCExplorer: a semi-automated search procedure for provably correct concurrent collectors. PLDI 2007: 456-467 - 2006
- [c6]Martin T. Vechev, Eran Yahav, David F. Bacon:
Correctness-preserving derivation of concurrent garbage collection algorithms. PLDI 2006: 341-353 - 2005
- [c5]Martin T. Vechev, David F. Bacon, Perry Cheng, David Grove:
Derivation and Evaluation of Concurrent Collectors. ECOOP 2005: 577-601 - [c4]David F. Bacon, Perry Cheng, David Grove, Michael Hind, V. T. Rajan, Eran Yahav, Matthias Hauswirth, Christoph M. Kirsch, Daniel Spoonhower, Martin T. Vechev:
High-level real-time programming in Java. EMSOFT 2005: 68-78 - [c3]David F. Bacon, Perry Cheng, David Grove, Martin T. Vechev:
Syncopation: generational real-time garbage collection in the metronome. LCTES 2005: 183-192 - 2004
- [c2]Martin T. Vechev, David F. Bacon:
Write barrier elision for concurrent garbage collectors. ISMM 2004: 13-24 - 2003
- [c1]Martin T. Vechev, Peter D. Petrov:
Class Unloading with a Concurrent Garbage Collector in an Embedded Java VM. Embedded Systems and Applications 2003: 99-108
Coauthor Index
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