Nirupam Gupta

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 61

Top Topics

Alternating Least Squares

Top Venues

Publications

Rachid Guerraoui, Nirupam Gupta, Rafael Pinot
Byzantine Machine Learning: A Primer. ACM Comput. Surv. 56 (7) (2024)
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot
Brief Announcement: A Case for Byzantine Machine Learning. PODC (2024)
Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych
Tackling Byzantine Clients in Federated Learning. CoRR (2024)
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Ahmed Jellouli, Geovani Rizk, John Stephan
Boosting Robustness by Clipping Gradients in Distributed Learning. CoRR (2024)
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot
On the Relevance of Byzantine Robust Optimization Against Data Poisoning. CoRR (2024)
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Geovani Rizk
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. CoRR (2023)
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)
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
Impact of Redundancy on Resilience in Distributed Optimization and Learning. ICDCN (2023)
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) (2023)
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. NeurIPS (2023)
Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. CoRR (2023)
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan
On the Privacy-Robustness-Utility Trilemma in Distributed Learning. ICML (2023)
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan
Robust Collaborative Learning with Linear Gradient Overhead. ICML (2023)
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan
Distributed Learning with Curious and Adversarial Machines. CoRR (2023)
El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan
SoK: On the Impossible Security of Very Large Foundation Models. CoRR (2022)
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
Impact of Redundancy on Resilience in Distributed Optimization and Learning. CoRR (2022)
Karim Boubouh, Amine Boussetta, Nirupam Gupta, Alexandre Maurer, Rafaël Pinot
Democratizing Machine Learning: Resilient Distributed Learning with Heterogeneous Participants. SRDS (2022)
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê Nguyên Hoang, Rafael Pinot, John Stephan
Making Byzantine Decentralized Learning Efficient. CoRR (2022)
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan
Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums. ICML (2022)
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 (2022)
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums. CoRR (2022)
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) (2022)
Nirupam Gupta, Shuo Liu, Nitin H. Vaidya
Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination. DSN Workshops (2021)
Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Sébastien Rouault, John Stephan
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? PODC (2021)
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
Redundancy in cost functions for Byzantine fault-tolerant federated learning. ResilientFL (2021)
Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot, Sébastien Rouault, John Stephan
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up? CoRR (2021)
Nirupam Gupta, Shripad Gade, Nikhil Chopra, Nitin H. Vaidya
Preserving Statistical Privacy in Distributed Optimization. IEEE Control. Syst. Lett. 5 (3) (2021)
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) (2021)
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem. ACC (2021)
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning. CoRR (2021)
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
Approximate Byzantine Fault-Tolerance in Distributed Optimization. PODC (2021)
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
Asynchronous Distributed Optimization with Redundancy in Cost Functions. CoRR (2021)
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem. CoRR (2021)
Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya
Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancy. CoRR (2021)
Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya
Byzantine Fault-Tolerance in Decentralized Optimization under 2f-Redundancy. ACC (2021)
Nirupam Gupta, Nitin H. Vaidya
Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent. CoRR (2021)
Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Sebastien Rouault, John Stephan
Combining Differential Privacy and Byzantine Resilience in Distributed SGD. CoRR (2021)
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
On Accelerating Distributed Convex Optimizations. CoRR (2021)
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning. L4DC (2021)
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
Approximate Byzantine Fault-Tolerance in Distributed Optimization. CoRR (2021)
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 (2020)
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method. ACC (2020)
Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya
Byzantine Fault-Tolerance in Decentralized Optimization under Minimal Redundancy. CoRR (2020)
Yimeng Dong, Nirupam Gupta, Nikhil Chopra
False Data Injection Attacks in Bilateral Teleoperation Systems. IEEE Trans. Control. Syst. Technol. 28 (3) (2020)
Nirupam Gupta, Nitin H. Vaidya
Resilience in Collaborative Optimization: Redundant and Independent Cost Functions. CoRR (2020)
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem. CoRR (2020)
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method. CoRR (2020)
Nirupam Gupta, Nitin H. Vaidya
Fault-Tolerance in Distributed Optimization: The Case of Redundancy. PODC (2020)
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
Accelerating Distributed SGD for Linear Linear Regression using Iterative Pre-Conditioning. CoRR (2020)
Nirupam Gupta, Shripad Gade, Nikhil Chopra, Nitin H. Vaidya
Preserving Statistical Privacy in Distributed Optimization. CoRR (2020)
Nirupam Gupta, Nitin H. Vaidya
Byzantine Fault-Tolerant Parallelized Stochastic Gradient Descent for Linear Regression. Allerton (2019)
Nirupam Gupta, Nitin H. Vaidya
Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning. CoRR (2019)
Nirupam Gupta, Jonathan Katz, Nikhil Chopra
Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs. CoRR (2019)
Nirupam Gupta, Nitin H. Vaidya
Byzantine Fault Tolerant Distributed Linear Regression. CoRR (2019)
Nirupam Gupta, Jonathan Katz, Nikhil Chopra
Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs. ACC (2019)
Nirupam Gupta, Nikhil Chopra
Privacy of Agents' Costs in Peer-to-Peer Distributed Optimization. CoRR (2019)
Nirupam Gupta, Nikhil Chopra
Model-Based Encryption: Privacy of States in Networked Control Systems. Allerton (2018)
Nirupam Gupta, Jonathan Katz, Nikhil Chopra
Information-Theoretic Privacy in Distributed Average Consensus. CoRR (2018)
Nirupam Gupta, Yimeng Dong, Nikhil Chopra
Robustness of distributive double-integrator consensus to loss of graph connectivity. ACC (2017)
Nirupam Gupta, Nikhil Chopra
Confidentiality in distributed average information consensus. CDC (2016)
Yimeng Dong, Nirupam Gupta, Nikhil Chopra
On content modification attacks in bilateral teleoperation systems. ACC (2016)