Deepesh Data - RankMe

64 papers A* 6A 2B 13Journal 36Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Decentralized Optimization Resilient Against Local Data Poisoning Attacks. IEEE Trans. Autom. Control. Yanwen Mao, Deepesh Data, Suhas N. Diggavi, Paulo Tabuada
2024	B	`conf`	Utilitarian Privacy and Private Sampling. ISIT Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran
2023	A*	`conf`	A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. ICLR Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
2023	J	`jnl`	Byzantine-Resilient High-Dimensional Federated Learning. IEEE Trans. Inf. Theory Deepesh Data, Suhas N. Diggavi
2023	J	`jnl`	Must the Communication Graph of MPC Protocols be an Expander? CoRR Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek
2023	J	`jnl`	Must the Communication Graph of MPC Protocols be an Expander? J. Cryptol. Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek
2023	J	`jnl`	SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Optimization. IEEE Trans. Autom. Control. Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
2022	J	`jnl`	A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy. CoRR Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
2022	—	`conf`	Decentralized Learning Robust to Data Poisoning Attacks. CDC Yanwen Mao, Deepesh Data, Suhas N. Diggavi, Paulo Tabuada
2022	B	`conf`	Distributed User-Level Private Mean Estimation. ISIT Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
2022	A	`conf`	Flexible Accuracy for Differential Privacy. AISTATS Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
2021	A*	`conf`	Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. ICML Deepesh Data, Suhas N. Diggavi
2021	B	`conf`	Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data. ISIT Deepesh Data, Suhas N. Diggavi
2021	J	`jnl`	Data Encoding for Byzantine-Resilient Distributed Optimization. IEEE Trans. Inf. Theory Deepesh Data, Linqi Song, Suhas N. Diggavi
2021	B	`conf`	Differentially Private Federated Learning with Shuffling and Client Self-Sampling. ISIT Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
2021	J	`jnl`	Flexible Accuracy for Differential Privacy. CoRR Aman Bansal, Rahul Chunduru, Deepesh Data, Manoj Prabhakaran
2021	J	`jnl`	On the Renyi Differential Privacy of the Shuffle Model. CoRR Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz
2021	A*	`conf`	On the Rényi Differential Privacy of the Shuffle Model. CCS Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Ananda Theertha Suresh, Peter Kairouz
2021	A*	`conf`	QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. NeurIPS Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi
2021	J	`jnl`	QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. CoRR Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi
2021	J	`jnl`	QuPeL: Quantized Personalization with Applications to Federated Learning. CoRR Kaan Ozkara, Navjot Singh, Deepesh Data, Suhas N. Diggavi
2021	A*	`conf`	Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning. NeurIPS Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
2021	J	`jnl`	Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning. CoRR Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
2021	B	`conf`	SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization. ISIT Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
2021	J	`jnl`	SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization. IEEE J. Sel. Areas Inf. Theory Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
2021	A	`conf`	Shuffled Model of Differential Privacy in Federated Learning. AISTATS Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
2021	J	`jnl`	Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs. IEEE J. Sel. Areas Inf. Theory Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
2020	J	`jnl`	Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. CoRR Deepesh Data, Suhas N. Diggavi
2020	J	`jnl`	Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data. CoRR Deepesh Data, Suhas N. Diggavi
2020	B	`conf`	Hiding Identities: Estimation Under Local Differential Privacy. ISIT Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi
2020	J	`jnl`	Interactive Secure Function Computation. IEEE Trans. Inf. Theory Deepesh Data, Gowtham R. Kurri, Jithin Ravi, Vinod M. Prabhakaran
2020	B	`conf`	On Byzantine-Resilient High-Dimensional Stochastic Gradient Descent. ISIT Deepesh Data, Suhas N. Diggavi
2020	J	`jnl`	Qsparse-Local-SGD: Distributed SGD With Quantization, Sparsification, and Local Computations. IEEE J. Sel. Areas Inf. Theory Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi
2020	—	`conf`	SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Optimization. CDC Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
2020	J	`jnl`	SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization. CoRR Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
2020	J	`jnl`	Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs. CoRR Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh
2020	J	`jnl`	Successive Refinement of Privacy. CoRR Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi
2020	J	`jnl`	Successive Refinement of Privacy. IEEE J. Sel. Areas Inf. Theory Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri, Christina Fragouli, Suhas N. Diggavi
2019	B	`conf`	Byzantine-Tolerant Distributed Coordinate Descent. ISIT Deepesh Data, Suhas N. Diggavi
2019	B	`conf`	Data Encoding Methods for Byzantine-Resilient Distributed Optimization. ISIT Deepesh Data, Linqi Song, Suhas N. Diggavi
2019	J	`jnl`	Data Encoding for Byzantine-Resilient Distributed Optimization. CoRR Deepesh Data, Linqi Song, Suhas N. Diggavi
2019	A*	`conf`	Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations. NeurIPS Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi
2019	J	`jnl`	Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations. CoRR Debraj Basu, Deepesh Data, Can Karakus, Suhas N. Diggavi
2019	J	`jnl`	SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization. CoRR Navjot Singh, Deepesh Data, Jemin George, Suhas N. Diggavi
2018	—	`conf`	Data Encoding for Byzantine-Resilient Distributed Gradient Descent. Allerton Deepesh Data, Linqi Song, Suhas N. Diggavi
2018	J	`jnl`	Interactive Secure Function Computation. CoRR Deepesh Data, Gowtham R. Kurri, Jithin Ravi, Vinod M. Prabhakaran
2018	—	`conf`	Must the Communication Graph of MPC Protocols be an Expander? CRYPTO (3) Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek
2018	J	`jnl`	Must the Communication Graph of MPC Protocols be an Expander? IACR Cryptol. ePrint Arch. Elette Boyle, Ran Cohen, Deepesh Data, Pavel Hubácek
2018	—	`conf`	Towards Characterizing Securely Computable Two-Party Randomized Functions. Public Key Cryptography (1) Deepesh Data, Manoj Prabhakaran
2017	J	`jnl`	Secure Computation of Randomized Functions: Further Results. CoRR Deepesh Data, Vinod M. Prabhakaran
2017	B	`conf`	Secure computation of randomized functions: Further results. ITW Deepesh Data, Vinod M. Prabhakaran
2017	J	`jnl`	Towards Characterizing Securely Computable Two-Party Randomized Functions. IACR Cryptol. ePrint Arch. Deepesh Data, Manoj Prabhakaran
2016	J	`jnl`	Communication and Randomness Lower Bounds for Secure Computation. IEEE Trans. Inf. Theory Deepesh Data, Vinod M. Prabhakaran, Manoj M. Prabhakaran
2016	J	`jnl`	Secure Computation of Randomized Functions. CoRR Deepesh Data
2016	B	`conf`	Secure computation of randomized functions. ISIT Deepesh Data
2015	J	`jnl`	Communication and Randomness Lower Bounds for Secure Computation. CoRR Deepesh Data, Vinod M. Prabhakaran, Manoj M. Prabhakaran
2015	B	`conf`	On coding for secure computing. ISIT Deepesh Data, Vinod M. Prabhakaran
2015	J	`jnl`	On the Communication Complexity of Secure Computation. IACR Cryptol. ePrint Arch. Deepesh Data, Manoj Prabhakaran, Vinod M. Prabhakaran
2014	J	`jnl`	How to Securely Compute the Modulo-Two Sum of Binary Sources. CoRR Deepesh Data, Bikash Kumar Dey, Manoj Mishra, Vinod M. Prabhakaran
2014	B	`conf`	How to securely compute the modulo-two sum of binary sources. ITW Deepesh Data, Bikash Kumar Dey, Manoj Mishra, Vinod M. Prabhakaran
2014	—	`conf`	On the Communication Complexity of Secure Computation. CRYPTO (2) Deepesh Data, Manoj Prabhakaran, Vinod M. Prabhakaran
2013	—	`conf`	Communication requirements for secure computation. Allerton Deepesh Data, Vinod M. Prabhakaran
2013	J	`jnl`	On the Communication Complexity of Secure Computation. CoRR Deepesh Data, Vinod M. Prabhakaran, Manoj Prabhakaran