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ORCIDGagandeep Singh 0001
Person information
- affiliation: University of Illinois Urbana-Champaign, IL, USA
- affiliation (PhD 2020): ETH Zurich, Switzerland
Other persons with the same name
- Gagandeep Singh — disambiguation page
- Gagandeep Singh 0002
![[0000-0002-3502-7401]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-3502-7401")
— IBM Research - Zurich, Switzerland (and 1 more) - Gagandeep Singh 0003 — Khalsa College, Amritsar, India (and 1 more)
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2020 – today
- 2024
[j11]Debangshu Banerjee, Changming Xu, Gagandeep Singh:
Input-Relational Verification of Deep Neural Networks. Proc. ACM Program. Lang. 8(PLDI): 1-27 (2024)- [c26]Debangshu Banerjee, Avaljot Singh, Gagandeep Singh:
Interpreting Robustness Proofs of Deep Neural Networks. ICLR 2024 - [c25]Harman Singh Farwah, Gagandeep Singh, Cheng Tan:
Exploiting Time Channel Vulnerability of Learned Bloom Filters. Tiny Papers @ ICLR 2024 - [c24]Tarun Suresh, Shubham Ugare, Gagandeep Singh, Sasa Misailovic:
Is Watermarking LLM-Generated Code Robust? Tiny Papers @ ICLR 2024 - [c23]Shubham Ugare, Tarun Suresh, Debangshu Banerjee, Gagandeep Singh, Sasa Misailovic:
Incremental Randomized Smoothing Certification. ICLR 2024 - [c22]Jason Vega, Isha Chaudhary, Changming Xu, Gagandeep Singh:
Bypassing the Safety Training of Open-Source LLMs with Priming Attacks. Tiny Papers @ ICLR 2024 - [c21]Debangshu Banerjee, Gagandeep Singh:
Relational DNN Verification With Cross Executional Bound Refinement. ICML 2024 - [c20]Changming Xu, Gagandeep Singh:
Robust Universal Adversarial Perturbations. ICML 2024 - [c19]Isha Chaudhary, Alex Renda, Charith Mendis, Gagandeep Singh:
COMET: Neural Cost Model Explanation Framework. MLSys 2024 - [i29]Yinglun Xu, Gagandeep Singh:
Efficient Two-Phase Offline Deep Reinforcement Learning from Preference Feedback. CoRR abs/2401.00330 (2024) - [i28]Yinglun Xu, Rohan Gumaste, Gagandeep Singh:
Reward Poisoning Attack Against Offline Reinforcement Learning. CoRR abs/2402.09695 (2024) - [i27]Isha Chaudhary, Vedaant V. Jain, Gagandeep Singh:
QuaCer-C: Quantitative Certification of Knowledge Comprehension in LLMs. CoRR abs/2402.15929 (2024) - [i26]Shubham Ugare, Tarun Suresh, Hangoo Kang, Sasa Misailovic, Gagandeep Singh:
Improving LLM Code Generation with Grammar Augmentation. CoRR abs/2403.01632 (2024) - [i25]Tarun Suresh, Shubham Ugare, Gagandeep Singh, Sasa Misailovic:
Is Watermarking LLM-Generated Code Robust? CoRR abs/2403.17983 (2024) - [i24]Avaljot Singh, Yasmin Sarita, Charith Mendis, Gagandeep Singh:
ConstraintFlow: A DSL for Specification and Verification of Neural Network Analyses. CoRR abs/2403.18729 (2024) - [i23]Yasmin Sarita, Avaljot Singh, Shaurya Gomber, Gagandeep Singh, Mahesh Vishwanathan:
Syndicate: Synergistic Synthesis of Ranking Function and Invariants for Termination Analysis. CoRR abs/2404.05951 (2024) - [i22]Changming Xu, Gagandeep Singh:
Cross-Input Certified Training for Universal Perturbations. CoRR abs/2405.09176 (2024) - [i21]Debangshu Banerjee, Gagandeep Singh:
Relational DNN Verification With Cross Executional Bound Refinement. CoRR abs/2405.10143 (2024) - [i20]Isha Chaudhary, Qian Hu, Manoj Kumar, Morteza Ziyadi, Rahul Gupta, Gagandeep Singh:
Quantitative Certification of Bias in Large Language Models. CoRR abs/2405.18780 (2024) - [i19]Yinglun Xu, David Zhu, Rohan Gumaste, Gagandeep Singh:
Optimal Reward Labeling: Bridging Offline Preference and Reward-Based Reinforcement Learning. CoRR abs/2406.10445 (2024) - 2023
- [j10]Jacob Laurel, Siyuan Brant Qian, Gagandeep Singh, Sasa Misailovic:
Synthesizing Precise Static Analyzers for Automatic Differentiation. Proc. ACM Program. Lang. 7(OOPSLA2): 1964-1992 (2023) - [j9]Shubham Ugare, Debangshu Banerjee, Sasa Misailovic, Gagandeep Singh:
Incremental Verification of Neural Networks. Proc. ACM Program. Lang. 7(PLDI): 1920-1945 (2023) - [j8]Yinglun Xu, Qi Zeng, Gagandeep Singh:
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c18]Rem Yang, Jacob Laurel, Sasa Misailovic, Gagandeep Singh:
Provable Defense Against Geometric Transformations. ICLR 2023 - [c17]Zikun Liu, Changming Xu, Emerson Sie, Gagandeep Singh, Deepak Vasisht:
Exploring Practical Vulnerabilities of Machine Learning-based Wireless Systems. NSDI 2023: 1801-1817 - [c16]Gagandeep Singh:
Building Trust and Safety in Artificial Intelligence with Abstract Interpretation. SAS 2023: 28-38 - [i18]Debangshu Banerjee, Avaljot Singh, Gagandeep Singh:
Interpreting Robustness Proofs of Deep Neural Networks. CoRR abs/2301.13845 (2023) - [i17]Isha Chaudhary, Alex Renda, Charith Mendis, Gagandeep Singh:
CoMEt: x86 Cost Model Explanation Framework. CoRR abs/2302.06836 (2023) - [i16]Shubham Ugare, Debangshu Banerjee, Sasa Misailovic, Gagandeep Singh:
Incremental Verification of Neural Networks. CoRR abs/2304.01874 (2023) - [i15]Yinglun Xu, Gagandeep Singh:
Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning. CoRR abs/2305.10681 (2023) - [i14]Shubham Ugare, Tarun Suresh, Debangshu Banerjee, Gagandeep Singh, Sasa Misailovic:
Incremental Randomized Smoothing Certification. CoRR abs/2305.19521 (2023) - [i13]Jason Vega, Isha Chaudhary, Changming Xu, Gagandeep Singh:
Bypassing the Safety Training of Open-Source LLMs with Priming Attacks. CoRR abs/2312.12321 (2023) - 2022
- [j7]Shubham Ugare, Gagandeep Singh, Sasa Misailovic:
Proof transfer for fast certification of multiple approximate neural networks. Proc. ACM Program. Lang. 6(OOPSLA1): 1-29 (2022) - [j6]Jacob Laurel, Rem Yang, Shubham Ugare, Robert Nagel, Gagandeep Singh, Sasa Misailovic:
A general construction for abstract interpretation of higher-order automatic differentiation. Proc. ACM Program. Lang. 6(OOPSLA2): 1007-1035 (2022) - [j5]Haoze Wu, Clark W. Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh:
Scalable verification of GNN-based job schedulers. Proc. ACM Program. Lang. 6(OOPSLA2): 1036-1065 (2022) - [j4]Jacob Laurel, Rem Yang, Gagandeep Singh, Sasa Misailovic:
A dual number abstraction for static analysis of Clarke Jacobians. Proc. ACM Program. Lang. 6(POPL): 1-30 (2022) - [j3]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) - [c15]Marc Fischer, Christian Sprecher, Dimitar I. Dimitrov, Gagandeep Singh, Martin T. Vechev:
Shared Certificates for Neural Network Verification. CAV (1) 2022: 127-148 - [c14]Dimitar Iliev Dimitrov, Gagandeep Singh, Timon Gehr, Martin T. Vechev:
Provably Robust Adversarial Examples. ICLR 2022 - [e1]Gagandeep Singh, Caterina Urban:
Static Analysis - 29th International Symposium, SAS 2022, Auckland, New Zealand, December 5-7, 2022, Proceedings. Lecture Notes in Computer Science 13790, Springer 2022, ISBN 978-3-031-22307-5 [contents] - [i12]Haoze Wu, Clark W. Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh:
Scalable Verification of GNN-based Job Schedulers. CoRR abs/2203.03153 (2022) - [i11]Yinglun Xu, Qi Zeng, Gagandeep Singh:
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning. CoRR abs/2205.14842 (2022) - [i10]Changming Xu, Gagandeep Singh:
Robust Universal Adversarial Perturbations. CoRR abs/2206.10858 (2022) - [i9]Rem Yang, Jacob Laurel, Sasa Misailovic, Gagandeep Singh:
Training Certifiably Robust Neural Networks Against Semantic Perturbations. CoRR abs/2207.11177 (2022) - 2021
- [c13]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 - [c12]Tobias Lorenz, Anian Ruoss, Mislav Balunovic, Gagandeep Singh, Martin T. Vechev:
Robustness Certification for Point Cloud Models. ICCV 2021: 7588-7598 - [c11]Christoph Müller, François Serre, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Scaling Polyhedral Neural Network Verification on GPUs. MLSys 2021 - [c10]Zikun Liu, Gagandeep Singh, Chenren Xu, Deepak Vasisht:
FIRE: enabling reciprocity for FDD MIMO systems. MobiCom 2021: 628-641 - [i8]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) - [i7]Tobias Lorenz, Anian Ruoss, Mislav Balunovic, Gagandeep Singh, Martin T. Vechev:
Robustness Certification for Point Cloud Models. CoRR abs/2103.16652 (2021) - [i6]Christian Sprecher, Marc Fischer, Dimitar I. Dimitrov, Gagandeep Singh, Martin T. Vechev:
Shared Certificates for Neural Network Verification. CoRR abs/2109.00542 (2021) - 2020
- [b1]Gagandeep Singh:
Scalable Automated Reasoning for Programs and Deep Learning. ETH Zurich, Zürich, Switzerland, 2020 - [c9]Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin T. Vechev:
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. ICML 2020: 2356-2365 - [c8]Jingxuan He, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Learning fast and precise numerical analysis. PLDI 2020: 1112-1127 - [i5]Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin T. Vechev:
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. CoRR abs/2003.03778 (2020) - [i4]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) - [i3]Christoph Müller, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Neural Network Robustness Verification on GPUs. CoRR abs/2007.10868 (2020) - [i2]Dimitar I. Dimitrov, Gagandeep Singh, Timon Gehr, Martin T. Vechev:
Scalable Inference of Symbolic Adversarial Examples. CoRR abs/2007.12133 (2020)
2010 – 2019
- 2019
- [j2]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) - [c7]Gagandeep Singh, Timon Gehr, Markus Püschel, Martin T. Vechev:
Boosting Robustness Certification of Neural Networks. ICLR (Poster) 2019 - [c6]Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin T. Vechev:
Beyond the Single Neuron Convex Barrier for Neural Network Certification. NeurIPS 2019: 15072-15083 - [c5]Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin T. Vechev:
Certifying Geometric Robustness of Neural Networks. NeurIPS 2019: 15287-15297 - [i1]Matthew Mirman, Gagandeep Singh, Martin T. Vechev:
A Provable Defense for Deep Residual Networks. CoRR abs/1903.12519 (2019) - 2018
- [j1]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) - [c4]Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Fast Numerical Program Analysis with Reinforcement Learning. CAV (1) 2018: 211-229 - [c3]Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin T. Vechev:
Fast and Effective Robustness Certification. NeurIPS 2018: 10825-10836 - 2017
- [c2]Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Fast polyhedra abstract domain. POPL 2017: 46-59 - 2015
- [c1]Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Making numerical program analysis fast. PLDI 2015: 303-313
Coauthor Index
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