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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
[j21]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)- [j20]Anouk Paradis, Benjamin Bichsel, Martin T. Vechev:
Reqomp: Space-constrained Uncomputation for Quantum Circuits. Quantum 8: 1258 (2024) - [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 - [i81]Momchil Peychev, Mark Niklas Müller, Marc Fischer, Martin T. Vechev:
Automated Classification of Model Errors on ImageNet. CoRR abs/2401.02430 (2024) - [i80]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) - [i79]Jingxuan He, Mark Vero, Gabriela Krasnopolska, Martin T. Vechev:
Instruction Tuning for Secure Code Generation. CoRR abs/2402.09497 (2024) - [i78]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) - [i77]Robin Staab, Mark Vero, Mislav Balunovic, Martin T. Vechev:
Large Language Models are Advanced Anonymizers. CoRR abs/2402.13846 (2024) - [i76]Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Watermark Stealing in Large Language Models. CoRR abs/2402.19361 (2024) - [i75]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) - [i74]Luca Beurer-Kellner, Marc Fischer, Martin T. Vechev:
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation. CoRR abs/2403.06988 (2024) - [i73]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) - [i72]Batuhan Tömekçe, Mark Vero, Robin Staab, Martin T. Vechev:
Private Attribute Inference from Images with Vision-Language Models. CoRR abs/2404.10618 (2024) - [i71]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) - [i70]Jasper Dekoninck, Mark Niklas Müller, Martin T. Vechev:
ConStat: Performance-Based Contamination Detection in Large Language Models. CoRR abs/2405.16281 (2024) - [i69]Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He, Martin T. Vechev:
Exploiting LLM Quantization. CoRR abs/2405.18137 (2024) - [i68]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) - [i67]Thibaud Gloaguen, Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Black-Box Detection of Language Model Watermarks. CoRR abs/2405.20777 (2024) - [i66]Yuhao Mao, Stefan Balauca, Martin T. Vechev:
CTBENCH: A Library and Benchmark for Certified Training. CoRR abs/2406.04848 (2024) - [i65]Hanna Yukhymenko, Robin Staab, Mark Vero, Martin T. Vechev:
A Synthetic Dataset for Personal Attribute Inference. CoRR abs/2406.07217 (2024) - [i64]Niels Mündler, Mark Niklas Müller, Jingxuan He, Martin T. Vechev:
Code Agents are State of the Art Software Testers. CoRR abs/2406.12952 (2024) - [i63]Hristo Venev, Timon Gehr, Dimitar Dimitrov, Martin T. Vechev:
Modular Synthesis of Efficient Quantum Uncomputation. CoRR abs/2406.14227 (2024) - [i62]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) - [i61]Slobodan Jenko, Jingxuan He, Niels Mündler, Mark Vero, Martin T. Vechev:
Practical Attacks against Black-box Code Completion Engines. CoRR abs/2408.02509 (2024) - [i60]Jasper Dekoninck, Maximilian Baader, Martin T. Vechev:
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation. CoRR abs/2409.00696 (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 - [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]