2024
Code-Aware Prompting: A Study of Coverage-Guided Test Generation in Regression Setting using LLM.
Proc. ACM Softw. Eng., 2024
Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for Code Generation with Lookahead Planning.
CoRR, 2024
Training LLMs to Better Self-Debug and Explain Code.
CoRR, 2024
Token Alignment via Character Matching for Subword Completion.
CoRR, 2024
LeDex: Training LLMs to Better Self-Debug and Explain Code.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
ReTA: Recursively Thinking Ahead to Improve the Strategic Reasoning of Large Language Models.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024
Reasoning and Planning with Large Language Models in Code Development.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
CodeFort: Robust Training for Code Generation Models.
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Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024
Token Alignment via Character Matching for Subword Completion.
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Proceedings of the Findings of the Association for Computational Linguistics, 2024
2023
Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models.
CoRR, 2023
Greener yet Powerful: Taming Large Code Generation Models with Quantization.
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CoRR, 2023
Towards Greener Yet Powerful Code Generation via Quantization: An Empirical Study.
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Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2023
Are Diffusion Models Vulnerable to Membership Inference Attacks?
Proceedings of the International Conference on Machine Learning, 2023
Multi-lingual Evaluation of Code Generation Models.
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Proceedings of the Eleventh International Conference on Learning Representations, 2023
ReCode: Robustness Evaluation of Code Generation Models.
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Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023
2022
Efficient Neural Network Verification Using Branch and Bound
PhD thesis, 2022
Multi-lingual Evaluation of Code Generation Models.
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CoRR, 2022
General Cutting Planes for Bound-Propagation-Based Neural Network Verification.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks.
Proceedings of the International Conference on Machine Learning, 2022
2021
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification.
CoRR, 2021
Cost-Aware Robust Tree Ensembles for Security Applications.
Proceedings of the 30th USENIX Security Symposium, 2021
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers.
Proceedings of the 9th International Conference on Learning Representations, 2021
Learning Security Classifiers with Verified Global Robustness Properties.
Proceedings of the CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, November 15, 2021
Adaptive Verifiable Training Using Pairwise Class Similarity.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
2020
Towards Understanding Fast Adversarial Training.
CoRR, 2020
Towards Practical Lottery Ticket Hypothesis for Adversarial Training.
CoRR, 2020
On Pruning Adversarially Robust Neural Networks.
CoRR, 2020
On Training Robust PDF Malware Classifiers.
Proceedings of the 29th USENIX Security Symposium, 2020
HYDRA: Pruning Adversarially Robust Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
2019
Bringing Engineering Rigor to Deep Learning.
ACM SIGOPS Oper. Syst. Rev., 2019
Training Robust Tree Ensembles for Security.
CoRR, 2019
Towards Compact and Robust Deep Neural Networks.
CoRR, 2019
Enhancing Gradient-based Attacks with Symbolic Intervals.
CoRR, 2019
2018
Prediction of a hotspot pattern in keyword search results.
Comput. Speech Lang., 2018
MixTrain: Scalable Training of Formally Robust Neural Networks.
CoRR, 2018
Formal Security Analysis of Neural Networks using Symbolic Intervals.
Proceedings of the 27th USENIX Security Symposium, 2018
Efficient Formal Safety Analysis of Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
2017
ContexloT: Towards Providing Contextual Integrity to Appified IoT Platforms.
Proceedings of the 24th Annual Network and Distributed System Security Symposium, 2017