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2020 – today
- 2024
[b1]Rachid Guerraoui, Nirupam Gupta, Rafael Pinot:
Robust Machine Learning - Distributed Methods for Safe AI. Springer 2024, ISBN 978-981-97-0687-7, pp. 1-154- [j7]Rachid Guerraoui
, Nirupam Gupta, Rafael Pinot:
Byzantine Machine Learning: A Primer. ACM Comput. Surv. 56(7): 169:1-169:39 (2024) - [c24]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych:
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates. ICML 2024 - [c23]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot:
Brief Announcement: A Case for Byzantine Machine Learning. PODC 2024: 131-134 - [e1]Armando Castañeda, Constantin Enea, Nirupam Gupta:
Networked Systems - 12th International Conference, NETYS 2024, Rabat, Morocco, May 29-31, 2024, Proceedings. Lecture Notes in Computer Science 14783, Springer 2024, ISBN 978-3-031-67320-7 [contents] - [i33]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych:
Tackling Byzantine Clients in Federated Learning. CoRR abs/2402.12780 (2024) - [i32]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot:
On the Relevance of Byzantine Robust Optimization Against Data Poisoning. CoRR abs/2405.00491 (2024) - [i31]Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Ahmed Jellouli, Geovani Rizk, John Stephan:
Boosting Robustness by Clipping Gradients in Distributed Learning. CoRR abs/2405.14432 (2024) - [i30]Youssef Allouah, Abdellah El Mrini, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot:
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients. CoRR abs/2409.20329 (2024) - [i29]Youssef Allouah, Akash Dhasade, Rachid Guerraoui, Nirupam Gupta, Anne-Marie Kermarrec, Rafael Pinot, Rafael Pires, Rishi Sharma:
Revisiting Ensembling in One-Shot Federated Learning. CoRR abs/2411.07182 (2024) - 2023
- [j6]Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya:
Byzantine Fault-Tolerance in Federated Local SGD Under $2f$-Redundancy. IEEE Trans. Control. Netw. Syst. 10(4): 1669-1681 (2023) - [c22]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. AISTATS 2023: 1232-1300 - [c21]Shuo Liu, Nirupam Gupta, Nitin H. Vaidya:
Impact of Redundancy on Resilience in Distributed Optimization and Learning. ICDCN 2023: 80-89 - [c20]Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
On the Privacy-Robustness-Utility Trilemma in Distributed Learning. ICML 2023: 569-626 - [c19]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan:
Robust Collaborative Learning with Linear Gradient Overhead. ICML 2023: 9761-9813 - [c18]Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk:
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. NeurIPS 2023 - [i28]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. CoRR abs/2302.01772 (2023) - [i27]Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Distributed Learning with Curious and Adversarial Machines. CoRR abs/2302.04787 (2023) - [i26]Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Geovani Rizk:
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. CoRR abs/2309.13591 (2023) - 2022
- [j5]Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem. Autom. 137: 110095 (2022) - [j4]Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
On Preconditioning of Decentralized Gradient-Descent When Solving a System of Linear Equations. IEEE Trans. Control. Netw. Syst. 9(2): 811-822 (2022) - [c17]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums. ICML 2022: 6246-6283 - [c16]Karim Boubouh, Amine Boussetta, Nirupam Gupta, Alexandre Maurer, Rafaël Pinot:
Democratizing Machine Learning: Resilient Distributed Learning with Heterogeneous Participants. SRDS 2022: 94-120 - [i25]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums. CoRR abs/2205.12173 (2022) - [i24]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê Nguyên Hoang, Rafael Pinot, John Stephan:
Making Byzantine Decentralized Learning Efficient. CoRR abs/2209.10931 (2022) - [i23]El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan:
On the Impossible Safety of Large AI Models. CoRR abs/2209.15259 (2022) - [i22]Shuo Liu, Nirupam Gupta, Nitin H. Vaidya:
Impact of Redundancy on Resilience in Distributed Optimization and Learning. CoRR abs/2211.08622 (2022) - 2021
- [j3]Nirupam Gupta, Shripad Gade, Nikhil Chopra, Nitin H. Vaidya:
Preserving Statistical Privacy in Distributed Optimization. IEEE Control. Syst. Lett. 5(3): 779-784 (2021) - [j2]Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem. IEEE Control. Syst. Lett. 5(6): 2180-2185 (2021) - [c15]Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem. ACC 2021: 2248-2253 - [c14]Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya:
Byzantine Fault-Tolerance in Decentralized Optimization under 2f-Redundancy. ACC 2021: 3632-3637 - [c13]Nirupam Gupta, Shuo Liu, Nitin H. Vaidya:
Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination. DSN Workshops 2021: 175-181 - [c12]Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning. L4DC 2021: 447-458 - [c11]Shuo Liu, Nirupam Gupta, Nitin H. Vaidya:
Approximate Byzantine Fault-Tolerance in Distributed Optimization. PODC 2021: 379-389 - [c10]Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Sébastien Rouault, John Stephan:
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? PODC 2021: 391-401 - [c9]Shuo Liu, Nirupam Gupta, Nitin H. Vaidya:
Redundancy in cost functions for Byzantine fault-tolerant federated learning. ResilientFL 2021: 4-6 - [i21]Shuo Liu, Nirupam Gupta, Nitin H. Vaidya:
Approximate Byzantine Fault-Tolerance in Distributed Optimization. CoRR abs/2101.09337 (2021) - [i20]Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem. CoRR abs/2101.10967 (2021) - [i19]Nirupam Gupta, Nitin H. Vaidya:
Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent. CoRR abs/2101.12316 (2021) - [i18]Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Sébastien Rouault, John Stephan:
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? CoRR abs/2102.08166 (2021) - [i17]Shuo Liu, Nirupam Gupta, Nitin H. Vaidya:
Asynchronous Distributed Optimization with Redundancy in Cost Functions. CoRR abs/2106.03998 (2021) - [i16]Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
On Accelerating Distributed Convex Optimizations. CoRR abs/2108.08670 (2021) - [i15]Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya:
Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancy. CoRR abs/2108.11769 (2021) - [i14]Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Sébastien Rouault, John Stephan:
Combining Differential Privacy and Byzantine Resilience in Distributed SGD. CoRR abs/2110.03991 (2021) - [i13]Shuo Liu, Nirupam Gupta, Nitin H. Vaidya:
Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning. CoRR abs/2110.10858 (2021) - 2020
- [j1]Yimeng Dong, Nirupam Gupta, Nikhil Chopra:
False Data Injection Attacks in Bilateral Teleoperation Systems. IEEE Trans. Control. Syst. Technol. 28(3): 1168-1176 (2020) - [c8]Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method. ACC 2020: 3977-3982 - [c7]Nirupam Gupta, Nitin H. Vaidya:
Fault-Tolerance in Distributed Optimization: The Case of Redundancy. PODC 2020: 365-374 - [i12]Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method. CoRR abs/2003.07180 (2020) - [i11]Nirupam Gupta, Nitin H. Vaidya:
Resilience in Collaborative Optimization: Redundant and Independent Cost Functions. CoRR abs/2003.09675 (2020) - [i10]Nirupam Gupta, Shripad Gade, Nikhil Chopra, Nitin H. Vaidya:
Preserving Statistical Privacy in Distributed Optimization. CoRR abs/2004.01312 (2020) - [i9]Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem. CoRR abs/2008.02856 (2020) - [i8]Nirupam Gupta, Shuo Liu, Nitin H. Vaidya:
Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE). CoRR abs/2008.04699 (2020) - [i7]Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya:
Byzantine Fault-Tolerance in Decentralized Optimization under Minimal Redundancy. CoRR abs/2009.14763 (2020) - [i6]Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra:
Accelerating Distributed SGD for Linear Linear Regression using Iterative Pre-Conditioning. CoRR abs/2011.07595 (2020)
2010 – 2019
- 2019
- [c6]Nirupam Gupta, Nitin H. Vaidya:
Byzantine Fault-Tolerant Parallelized Stochastic Gradient Descent for Linear Regression. Allerton 2019: 415-420 - [c5]Nirupam Gupta, Jonathan Katz, Nikhil Chopra:
Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs. ACC 2019: 1836-1841 - [i5]Nirupam Gupta, Nitin H. Vaidya:
Byzantine Fault Tolerant Distributed Linear Regression. CoRR abs/1903.08752 (2019) - [i4]Nirupam Gupta, Jonathan Katz, Nikhil Chopra:
Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs. CoRR abs/1903.09315 (2019) - [i3]Nirupam Gupta, Nikhil Chopra:
Privacy of Agents' Costs in Peer-to-Peer Distributed Optimization. CoRR abs/1905.00733 (2019) - [i2]Nirupam Gupta, Nitin H. Vaidya:
Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning. CoRR abs/1912.09528 (2019) - 2018
- [c4]Nirupam Gupta, Nikhil Chopra:
Model-Based Encryption: Privacy of States in Networked Control Systems. Allerton 2018: 242-248 - [i1]Nirupam Gupta, Jonathan Katz, Nikhil Chopra:
Information-Theoretic Privacy in Distributed Average Consensus. CoRR abs/1809.01794 (2018) - 2017
- [c3]Nirupam Gupta, Yimeng Dong, Nikhil Chopra:
Robustness of distributive double-integrator consensus to loss of graph connectivity. ACC 2017: 4516-4521 - 2016
- [c2]Yimeng Dong, Nirupam Gupta, Nikhil Chopra:
On content modification attacks in bilateral teleoperation systems. ACC 2016: 316-321 - [c1]Nirupam Gupta, Nikhil Chopra:
Confidentiality in distributed average information consensus. CDC 2016: 6709-6714
Coauthor Index
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