Publications

Preprints

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.

Cross-Input Certified Training for Universal Perturbations

Changming Xu, 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.

ConstraintFlow: A DSL for Specification and Verification of Neural Network Analyses

Avaljot Singh, Yasmin Sarita, Charith Mendis, Gagandeep Singh, Arxiv 2024.

Improving LLM Code 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.

RAMP: Boosting Adversarial Robustness Against Multiple Lp Perturbations

Enyi Jiang, 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

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

Robust Universal Adversarial Perturbations

Changming Xu, Gagandeep Singh, ICML 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.

A Practical Construction for Decomposing Numerical Abstract Domains

Gagandeep Singh, Markus Püschel, Martin Vechev, POPL 2018.

Fast Polyhedra Abstract Domain

Gagandeep Singh, Markus Püschel, Martin Vechev, POPL 2017.

Making Numerical Program Analysis Fast

Gagandeep Singh, Markus Püschel, Martin Vechev, PLDI 2015.