RAMP: Boosting Adversarial Robustness Against Multiple Perturbations for Universal Robustness
Enyi Jiang, Gagandeep Singh, NeurIPS 2024.
Publications
Preprints
Decoding Intelligence: A Framework for Certifying Knowledge Comprehension in LLMs
Isha Chaudhary, Vedaant V. Jain, Gagandeep Singh, Arxiv 2024.
Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety Alignment
Jason Vega, Junsheng Huang, Gaokai Zhang, Hangoo Kang, Minjia Zhang, Gagandeep Singh, Arxiv 2024.
ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs
Yuchen Yang, Shubham Ugare, Yifan Zhao, Gagandeep Singh, Sasa Misailovic, Arxiv 2024.
Robust Thompson Sampling Algorithms Against Reward Poisoning Attacks
Yinglun Xu, Zhiwei Wang, Gagandeep Singh, Arxiv 2024.
Towards Universal Certified Robustness with Multi-Norm Training
Enyi Jiang, Gagandeep Singh, Arxiv 2024.
IterGen: Iterative Structured LLM Generation
Shubham Ugare, Rohan Gumaste, Tarun Suresh, Gagandeep Singh, Sasa Misailovic, Arxiv 2024.
Optimal Reward Labeling: Bridging Offline Preference and Reward-Based Reinforcement Learning
Yinglun Xu, David Zhu, Rohan Gumaste, Gagandeep Singh, Arxiv 2024.
Quantitative Certification of Bias in Large Language Models
Isha Chaudhary, Qian Hu, Manoj Kumar, Morteza Ziyadi, Rahul Gupta, Gagandeep Singh, Arxiv 2024.
Syndicate: Synergistic Synthesis of Ranking Function and Invariants for Termination Analysis
Yasmin Sarita, Avaljot Singh, Shaurya Gomber, Gagandeep Singh, Mahesh Vishwanathan, Arxiv 2024.
SynCode: LLM Generation with Grammar Augmentation
Shubham Ugare, Tarun Suresh, Hangoo Kang, Sasa Misailovic, Gagandeep Singh, Arxiv 2024.
Reward Poisoning Attack Against Offline Reinforcement Learning
Yinglun Xu, Rohan Gumaste, Gagandeep Singh, Arxiv 2024.
Efficient Two-Phase Offline Deep Reinforcement Learning from Preference Feedback
Yinglun Xu, Gagandeep Singh, Arxiv 2024.
Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning
Yinglun Xu, Gagandeep Singh, Arxiv 2023.
Workshops and Short Papers
Scalable Relational Verification and Training for Deep Neural Networks
Debangshu Banerjee, Changming Xu, Gagandeep Singh, SAIV@CAV 2024.
QuaCer-C: Quantitative Certification of Knowledge Comprehension in LLMs
Isha Chaudhary, Vedaant Jain, Gagandeep Singh, SeT LLM@ICLR 2024.
Bypassing the Safety Training of Open-Source LLMs with Priming Attacks
Jason Vega, Isha Chaudhary, Changming Xu, Gagandeep Singh, Tiny Papers@ICLR 2024.
Exploiting Time Channel Vulnerability of Learned Bloom Filters
Harman Singh Farwah, Gagandeep Singh, Cheng Tan, Tiny Papers@ICLR 2024.
Is Watermarking LLM-Generated Code Robust?
Tarun Suresh, Shubham Ugare, Gagandeep Singh, Sasa Misailovic, Tiny Papers@ICLR 2024.
Abstract Interpretation for Automatic Differentiation
Jacob Laurel, Siyuan Brant Qian, Gagandeep Singh, Sasa Misailovic, LAFI@POPL 2024.
Toward Continuous Verification of DNNs
Shubham Ugare, Debangshu Banerjee, Tarun Suresh, Sasa Misailovic, Gagandeep Singh, WFVML@ICML 2023.
Property-Driven Evaluation of RL-Controllers in Self-Driving Datacenters
Arnav Chakravarthy, Nina Narodytska, Asmitha Rathis, Marius Vilcu, Mahmood Sharif, Gagandeep Singh, DMML@NeurIPS 2022.
Physically-Constrained Adversarial Attacks on Brain-Machine Interfaces
Xiaying Wang, Rodolfo Octavio Siller Quintanilla, Michael Hersche, Luca Benini, Gagandeep Singh, TSRML@NeurIPS 2022.
Conferences and Journals
Relational Verification Leaps Forward with RABBit
Tarun Suresh, Debangshu Banerjee, Gagandeep Singh, NeurIPS 2024.
NN4SysBench: Characterizing Neural Network Verification for Computer Systems
Shuyi Lin, Haoyu He, Tianhao Wei, Kaidi Xu, Huan Zhang, Gagandeep Singh, Changliu Liu, Cheng Tan, NeurIPS 2024 Datasets and Benchmarks Track.
ConstraintFlow: A DSL for Specification and Verification of Neural Network Analyses
Avaljot Singh, Yasmin Sarita, Charith Mendis, Gagandeep Singh, SAS 2024 (NEAT Category).
Cross-Input Certified Training for Universal Perturbations
Changming Xu, Gagandeep Singh, ECCV 2024.
Relational DNN Verification With Cross Executional Bound Refinement
Debangshu Banerjee, Gagandeep Singh, ICML 2024.
Input Relational Verification of Deep Neural Networks
Debangshu Banerjee, Changming Xu, and Gagandeep Singh, PLDI 2024.
COMET: Neural Cost Model Explanation Framework
Isha Chaudhary, Alex Renda, Charith Mendis, Gagandeep Singh, MLSys 2024 , also at XAIA@NeurIPS 2023..
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.
Interpreting Robustness Proofs of Deep Neural Networks
Debangshu Banerjee, Avaljot Singh, Gagandeep Singh, ICLR 2024, also at WFVML@ICML 2023 (Outstanding paper).
Incremental Randomized Smoothing Certification
Shubham Ugare, Tarun Suresh, Debangshu Banerjee, Gagandeep Singh, and Sasa Misailovic, ICLR 2024.
Building Trust and Safety in Artificial Intelligence with Abstract Interpretation
Gagandeep Singh, SAS (Invited Abstract) 2023.
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning
Yinglun Xu, Qi Zeng, Gagandeep Singh, TMLR 2023 (Featured Certification).
Synthesizing Precise Static Analyzers for Automatic Differentiation
Jacob Laurel, Siyuan Brant Qian, Gagandeep Singh, Sasa Misailovic, OOPSLA 2023.
Incremental Verification of Neural Networks
Shubham Ugare, Debangshu Banerjee, Sasa Misailovic, and Gagandeep Singh, PLDI 2023.
Provable Defense Against Geometric Transformations
Rem Yang, Jacob Laurel, Sasa Misailovic, Gagandeep Singh, ICLR 2023 (Spotlight).
Exploring Practical Vulnerabilities of Machine Learning-based Wireless Systems
Zikun Liu, Changming Xu, Gagandeep Singh, and Deepak Vasisht, NSDI 2023.
Scalable Verification of GNN-Based Job Schedulers
Haoze Wu, Clark Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh, OOPSLA 2022.
Proof Transfer for Fast Certification of Multiple Approximate Neural Networks
Shubham Ugare, Gagandeep Singh, Sasa Misailovic, OOPSLA 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.
Shared Certificates for Neural Network Verification
Marc Fischer, Christian Sprecher, Dimitar I. Dimitrov, Gagandeep Singh, Martin Vechev, CAV 2022.
Provably Robust Adversarial Examples
Dimitar I. Dimitrov, Gagandeep Singh, Timon Gehr, Martin Vechev, ICLR 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.
A Dual Number Abstraction for Static Analysis of Clarke Jacobians
Jacob Laurel, Rem Yang, Gagandeep Singh, Sasa Misailovic, POPL 2022.
FIRE: Enabling Reciprocity for FDD MIMO Systems
Zikun Liu, Gagandeep Singh, Chenren Xu, Deepak Vasisht, MobiCom 2021.
Robustness Certification for Point Cloud Models
Tobias Lorenz, Anian Ruoss, Mislav Balunović, Gagandeep Singh, Martin Vechev, ICCV 2021.
Scalable Polyhedral Verification of Recurrent Neural Networks
Wonryong Ryou, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Dan, Martin Vechev, CAV 2021.
Scaling Polyhedral Neural Network Verification on GPUs
Christoph Müller, Francois Serre, Gagandeep Singh, Markus Püschel, Martin Vechev, MLSys 2021.
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin Vechev, ICML 2020.
Learning Fast and Precise Numerical Analysis
Jingxuan He, Gagandeep Singh, Markus Püschel, Martin Vechev, PLDI 2020.
Beyond the Single Neuron Convex Barrier for Neural Network Certification
Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin Vechev, NeurIPS 2019.
Certifying Geometric Robustness of Neural Networks
Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin Vechev, NeurIPS 2019.
Boosting Robustness Certification of Neural Networks
Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev, ICLR 2019.
An Abstract Domain for Certifying Neural Networks
Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev, POPL 2019.
Fast and Effective Robustness Certification
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev, NeurIPS 2018.
Fast Numerical Program Analysis with Reinforcement Learning
Gagandeep Singh, Markus Püschel, Martin Vechev, CAV 2018.