Making Numerical Program Analysis Fast
Gagandeep Singh, Markus Püschel, Martin Vechev, PLDI 2015.
Posts by Collection
publications
A Practical Construction for Decomposing Numerical Abstract Domains
Gagandeep Singh, Markus Püschel, Martin Vechev, POPL 2018.
Fast Numerical Program Analysis with Reinforcement Learning
Gagandeep Singh, Markus Püschel, Martin Vechev, CAV 2018.
Fast and Effective Robustness Certification
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev, NeurIPS 2018.
An Abstract Domain for Certifying Neural Networks
Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev, POPL 2019.
Boosting Robustness Certification of Neural Networks
Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev, ICLR 2019.
Certifying Geometric Robustness of Neural Networks
Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin Vechev, NeurIPS 2019.
Beyond the Single Neuron Convex Barrier for Neural Network Certification
Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin Vechev, NeurIPS 2019.
Learning Fast and Precise Numerical Analysis
Jingxuan He, Gagandeep Singh, Markus Püschel, Martin Vechev, PLDI 2020.
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin Vechev, ICML 2020.
Scaling Polyhedral Neural Network Verification on GPUs
Christoph Müller, Francois Serre, Gagandeep Singh, Markus Püschel, Martin Vechev, MLSys 2021.
Scalable Polyhedral Verification of Recurrent Neural Networks
Wonryong Ryou, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Dan, Martin Vechev, CAV 2021.
Robustness Certification for Point Cloud Models
Tobias Lorenz, Anian Ruoss, Mislav Balunović, Gagandeep Singh, Martin Vechev, ICCV 2021.
FIRE: Enabling Reciprocity for FDD MIMO Systems
Zikun Liu, Gagandeep Singh, Chenren Xu, Deepak Vasisht, MobiCom 2021.
A Dual Number Abstraction for Static Analysis of Clarke Jacobians
Jacob Laurel, Rem Yang, Gagandeep Singh, Sasa Misailovic, POPL 2022.
PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations
Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, Martin Vechev, POPL 2022.
Provably Robust Adversarial Examples
Dimitar I. Dimitrov, Gagandeep Singh, Timon Gehr, Martin Vechev, ICLR 2022.
Shared Certificates for Neural Network Verification
Marc Fischer, Christian Sprecher, Dimitar I. Dimitrov, Gagandeep Singh, Martin Vechev, CAV 2022.
A General Construction for Abstract Interpretation of Higher-Order Automatic Differentiation
Jacob Laurel, Rem yang, Shubham Ugare, Robert Nagel, Gagandeep Singh, Sasa Misailovic, OOPSLA 2022.
Proof Transfer for Fast Certification of Multiple Approximate Neural Networks
Shubham Ugare, Gagandeep Singh, Sasa Misailovic, OOPSLA 2022.
Scalable Verification of GNN-Based Job Schedulers
Haoze Wu, Clark Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh, OOPSLA 2022.
Exploring Practical Vulnerabilities of Machine Learning-based Wireless Systems
Zikun Liu, Changming Xu, Gagandeep Singh, and Deepak Vasisht, NSDI 2023.
Provable Defense Against Geometric Transformations
Rem Yang, Jacob Laurel, Sasa Misailovic, Gagandeep Singh, ICLR 2023 (Spotlight).
Incremental Verification of Neural Networks
Shubham Ugare, Debangshu Banerjee, Sasa Misailovic, and Gagandeep Singh, PLDI 2023.
Synthesizing Precise Static Analyzers for Automatic Differentiation
Jacob Laurel, Siyuan Brant Qian, Gagandeep Singh, Sasa Misailovic, OOPSLA 2023.
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning
Yinglun Xu, Qi Zeng, Gagandeep Singh, TMLR 2023 (Featured Certification).
Building Trust and Safety in Artificial Intelligence with Abstract Interpretation
Gagandeep Singh, SAS (Invited Abstract) 2023.
Incremental Randomized Smoothing Certification
Shubham Ugare, Tarun Suresh, Debangshu Banerjee, Gagandeep Singh, and Sasa Misailovic, ICLR 2024.
Interpreting Robustness Proofs of Deep Neural Networks
Debangshu Banerjee, Avaljot Singh, Gagandeep Singh, ICLR 2024, also at WFVML@ICML 2023 (Outstanding paper).
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler
Zilinghan Li, Pranshu Chaturvedi, Shilan He, Han Chen, Gagandeep Singh, Volodymyr Kindratenko, Eliu A Huerta, Kibaek Kim, Ravi Madduri, ICLR 2024.
COMET: Neural Cost Model Explanation Framework
Isha Chaudhary, Alex Renda, Charith Mendis, Gagandeep Singh, MLSys 2024 , also at XAIA@NeurIPS 2023..
talks
teaching
Logic and Artificial Intelligence
Advanced Graduate course, 2021