default search action
Martin T. Vechev
Person information
- affiliation: ETH Zürich, Department of Computer Science, Switzerland
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
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]