![](https://dblp.dagstuhl.de/img/logo.ua.320x120.png)
![](https://dblp.dagstuhl.de/img/dropdown.dark.16x16.png)
![](https://dblp.dagstuhl.de/img/peace.dark.16x16.png)
Остановите войну!
for scientists:
![search dblp search dblp](https://dblp.dagstuhl.de/img/search.dark.16x16.png)
![search dblp](https://dblp.dagstuhl.de/img/search.dark.16x16.png)
default search action
Gagandeep 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
— IBM Research - Zurich, Switzerland (and 1 more)
- Gagandeep Singh 0003
— Khalsa College, Amritsar, India (and 1 more)
Refine list
![note](https://dblp.dagstuhl.de/img/note-mark.dark.12x12.png)
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
- [i21]Yinglun Xu, Gagandeep Singh:
Efficient Two-Phase Offline Deep Reinforcement Learning from Preference Feedback. CoRR abs/2401.00330 (2024) - [i20]Yinglun Xu, Rohan Gumaste, Gagandeep Singh:
Reward Poisoning Attack Against Offline Reinforcement Learning. CoRR abs/2402.09695 (2024) - [i19]Shubham Ugare, Tarun Suresh, Hangoo Kang, Sasa Misailovic, Gagandeep Singh:
Improving LLM Code Generation with Grammar Augmentation. CoRR abs/2403.01632 (2024) - [i18]Tarun Suresh, Shubham Ugare, Gagandeep Singh, Sasa Misailovic:
Is Watermarking LLM-Generated Code Robust? CoRR abs/2403.17983 (2024) - [i17]Debangshu Banerjee, Gagandeep Singh:
Relational DNN Verification With Cross Executional Bound Refinement. CoRR abs/2405.10143 (2024) - 2023
- [j9]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) - [j8]Shubham Ugare
, Debangshu Banerjee
, Sasa Misailovic
, Gagandeep Singh
:
Incremental Verification of Neural Networks. Proc. ACM Program. Lang. 7(PLDI): 1920-1945 (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 - [i16]Debangshu Banerjee, Avaljot Singh, Gagandeep Singh:
Interpreting Robustness Proofs of Deep Neural Networks. CoRR abs/2301.13845 (2023) - [i15]Shubham Ugare, Debangshu Banerjee, Sasa Misailovic, Gagandeep Singh:
Incremental Verification of Neural Networks. CoRR abs/2304.01874 (2023) - [i14]Yinglun Xu, Gagandeep Singh:
Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning. CoRR abs/2305.10681 (2023) - [i13]Shubham Ugare, Tarun Suresh, Debangshu Banerjee, Gagandeep Singh, Sasa Misailovic:
Incremental Randomized Smoothing Certification. CoRR abs/2305.19521 (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
![](https://dblp.dagstuhl.de/img/cog.dark.24x24.png)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-07-06 23:44 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint