Gagandeep Singh

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 56

Top Topics

Code Generation

Reinforcement Learning

Top Venues

Proc. ACM Program. Lang.

Publications

Yinglun Xu, Gagandeep Singh
Efficient Two-Phase Offline Deep Reinforcement Learning from Preference Feedback. CoRR (2024)
Shubham Ugare, Tarun Suresh, Debangshu Banerjee, Gagandeep Singh, Sasa Misailovic
Incremental Randomized Smoothing Certification. ICLR (2024)
Debangshu Banerjee, Avaljot Singh, Gagandeep Singh
Interpreting Robustness Proofs of Deep Neural Networks. ICLR (2024)
Yinglun Xu, Rohan Gumaste, Gagandeep Singh
Reward Poisoning Attack Against Offline Reinforcement Learning. CoRR (2024)
Yinglun Xu, David Zhu, Rohan Gumaste, Gagandeep Singh
Optimal Reward Labeling: Bridging Offline Preference and Reward-Based Reinforcement Learning. CoRR (2024)
Tarun Suresh, Shubham Ugare, Gagandeep Singh, Sasa Misailovic
Is Watermarking LLM-Generated Code Robust? CoRR (2024)
Yasmin Sarita, Avaljot Singh, Shaurya Gomber, Gagandeep Singh, Mahesh Vishwanathan
Syndicate: Synergistic Synthesis of Ranking Function and Invariants for Termination Analysis. CoRR (2024)
Debangshu Banerjee, Changming Xu, Gagandeep Singh
Input-Relational Verification of Deep Neural Networks. Proc. ACM Program. Lang. 8 (PLDI) (2024)
Isha Chaudhary, Qian Hu, Manoj Kumar, Morteza Ziyadi, Rahul Gupta, Gagandeep Singh
Quantitative Certification of Bias in Large Language Models. CoRR (2024)
Shubham Ugare, Tarun Suresh, Hangoo Kang, Sasa Misailovic, Gagandeep Singh
Improving LLM Code Generation with Grammar Augmentation. CoRR (2024)
Jason Vega, Isha Chaudhary, Changming Xu, Gagandeep Singh
Bypassing the Safety Training of Open-Source LLMs with Priming Attacks. Tiny Papers @ ICLR (2024)
Avaljot Singh, Yasmin Sarita, Charith Mendis, Gagandeep Singh
ConstraintFlow: A DSL for Specification and Verification of Neural Network Analyses. CoRR (2024)
Changming Xu, Gagandeep Singh
Cross-Input Certified Training for Universal Perturbations. CoRR (2024)
Harman Singh Farwah, Gagandeep Singh, Cheng Tan
Exploiting Time Channel Vulnerability of Learned Bloom Filters. Tiny Papers @ ICLR (2024)
Isha Chaudhary, Vedaant V. Jain, Gagandeep Singh
QuaCer-C: Quantitative Certification of Knowledge Comprehension in LLMs. CoRR (2024)
Isha Chaudhary, Alex Renda, Charith Mendis, Gagandeep Singh
COMET: Neural Cost Model Explanation Framework. MLSys (2024)
Tarun Suresh, Shubham Ugare, Gagandeep Singh, Sasa Misailovic
Is Watermarking LLM-Generated Code Robust? Tiny Papers @ ICLR (2024)
Debangshu Banerjee, Gagandeep Singh
Relational DNN Verification With Cross Executional Bound Refinement. CoRR (2024)
Shubham Ugare, Debangshu Banerjee, Sasa Misailovic, Gagandeep Singh
Incremental Verification of Neural Networks. Proc. ACM Program. Lang. 7 (PLDI) (2023)
Yinglun Xu, Qi Zeng, Gagandeep Singh
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)
Rem Yang, Jacob Laurel, Sasa Misailovic, Gagandeep Singh
Provable Defense Against Geometric Transformations. ICLR (2023)
Zikun Liu, Changming Xu, Emerson Sie, Gagandeep Singh, Deepak Vasisht
Exploring Practical Vulnerabilities of Machine Learning-based Wireless Systems. NSDI (2023)
Jacob Laurel, Siyuan Brant Qian, Gagandeep Singh, Sasa Misailovic
Synthesizing Precise Static Analyzers for Automatic Differentiation. Proc. ACM Program. Lang. 7 (OOPSLA2) (2023)
Shubham Ugare, Tarun Suresh, Debangshu Banerjee, Gagandeep Singh, Sasa Misailovic
Incremental Randomized Smoothing Certification. CoRR (2023)
Shubham Ugare, Debangshu Banerjee, Sasa Misailovic, Gagandeep Singh
Incremental Verification of Neural Networks. CoRR (2023)
Gagandeep Singh
Building Trust and Safety in Artificial Intelligence with Abstract Interpretation. SAS (2023)
Yinglun Xu, Gagandeep Singh
Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning. CoRR (2023)
Shubham Ugare, Gagandeep Singh, Sasa Misailovic
Proof transfer for fast certification of multiple approximate neural networks. Proc. ACM Program. Lang. 6 (OOPSLA) (2022)
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) (2022)
Jacob Laurel, Rem Yang, Gagandeep Singh, Sasa Misailovic
A dual number abstraction for static analysis of Clarke Jacobians. Proc. ACM Program. Lang. 6 (POPL) (2022)
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) (2022)
Marc Fischer, Christian Sprecher, Dimitar I. Dimitrov, Gagandeep Singh, Martin T. Vechev
Shared Certificates for Neural Network Verification. CAV (1) (2022)
Dimitar Iliev Dimitrov, Gagandeep Singh, Timon Gehr, Martin T. Vechev
Provably Robust Adversarial Examples. ICLR (2022)
Rem Yang, Jacob Laurel, Sasa Misailovic, Gagandeep Singh
Training Certifiably Robust Neural Networks Against Semantic Perturbations. CoRR (2022)
Haoze Wu, Clark W. Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh
Scalable verification of GNN-based job schedulers. Proc. ACM Program. Lang. 6 (OOPSLA2) (2022)
Christian Sprecher, Marc Fischer, Dimitar I. Dimitrov, Gagandeep Singh, Martin T. Vechev
Shared Certificates for Neural Network Verification. CoRR (2021)
Tobias Lorenz, Anian Ruoss, Mislav Balunovic, Gagandeep Singh, Martin T. Vechev
Robustness Certification for Point Cloud Models. CoRR (2021)
Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, Martin T. Vechev
Precise Multi-Neuron Abstractions for Neural Network Certification. CoRR (2021)
Tobias Lorenz, Anian Ruoss, Mislav Balunovic, Gagandeep Singh, Martin T. Vechev
Robustness Certification for Point Cloud Models. ICCV (2021)
Wonryong Ryou, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Marian Dan, Martin T. Vechev
Scalable Polyhedral Verification of Recurrent Neural Networks. CAV (1) (2021)
Christoph Müller, François Serre, Gagandeep Singh, Markus Püschel, Martin T. Vechev
Scaling Polyhedral Neural Network Verification on GPUs. MLSys (2021)
Dimitar I. Dimitrov, Gagandeep Singh, Timon Gehr, Martin T. Vechev
Scalable Inference of Symbolic Adversarial Examples. CoRR (2020)
Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin T. Vechev
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. ICML (2020)
Wonryong Ryou, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Marian Dan, Martin T. Vechev
Fast and Effective Robustness Certification for Recurrent Neural Networks. CoRR (2020)
Jingxuan He, Gagandeep Singh, Markus Püschel, Martin T. Vechev
Learning fast and precise numerical analysis. PLDI (2020)
Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin T. Vechev
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. CoRR (2020)
Christoph Müller, Gagandeep Singh, Markus Püschel, Martin T. Vechev
Neural Network Robustness Verification on GPUs. CoRR (2020)
Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin T. Vechev
Beyond the Single Neuron Convex Barrier for Neural Network Certification. NeurIPS (2019)
Gagandeep Singh, Timon Gehr, Markus Püschel, Martin T. Vechev
An abstract domain for certifying neural networks. Proc. ACM Program. Lang. 3 (POPL) (2019)
Matthew Mirman, Gagandeep Singh, Martin T. Vechev
A Provable Defense for Deep Residual Networks. CoRR (2019)
Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin T. Vechev
Certifying Geometric Robustness of Neural Networks. NeurIPS (2019)
Gagandeep Singh, Timon Gehr, Markus Püschel, Martin T. Vechev
Boosting Robustness Certification of Neural Networks. ICLR (Poster) (2019)
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin T. Vechev
Fast and Effective Robustness Certification. NeurIPS (2018)
Gagandeep Singh, Markus Püschel, Martin T. Vechev
A practical construction for decomposing numerical abstract domains. Proc. ACM Program. Lang. 2 (POPL) (2018)
Gagandeep Singh, Markus Püschel, Martin T. Vechev
Fast Numerical Program Analysis with Reinforcement Learning. CAV (1) (2018)
Gagandeep Singh, Markus Püschel, Martin T. Vechev
Fast polyhedra abstract domain. POPL (2017)
Gagandeep Singh, Markus Püschel, Martin T. Vechev
Making numerical program analysis fast. PLDI (2015)