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2020 – today
- 2024
[j5]Seungeun Oh
, Hyelin Nam, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim:
Mix2SFL: Two-Way Mixup for Scalable, Accurate, and Communication-Efficient Split Federated Learning. IEEE Trans. Big Data 10(3): 238-248 (2024)- [c15]Zaid Tasneem, Akshat Dave, Abhishek Singh, Kushagra Tiwary, Praneeth Vepakomma, Ashok Veeraraghavan, Ramesh Raskar:
DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images. ECCV (59) 2024: 144-161 - [c14]Ibrahim Suat Evren, Praneeth Vepakomma:
Effects of Privacy-Inducing Noise on Welfare and Influence of Referendum Systems. FORC 2024: 1:1-1:20 - [i36]Zaid Tasneem, Akshat Dave, Abhishek Singh, Kushagra Tiwary, Praneeth Vepakomma, Ashok Veeraraghavan, Ramesh Raskar:
DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images. CoRR abs/2403.13199 (2024) - [i35]Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar:
Data Acquisition via Experimental Design for Decentralized Data Markets. CoRR abs/2403.13893 (2024) - [i34]Zheng Lin, Xuanjie Hu, Yuxin Zhang, Zhe Chen, Zihan Fang, Xianhao Chen, Ang Li, Praneeth Vepakomma, Yue Gao:
SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models. CoRR abs/2407.00952 (2024) - [i33]Seungeun Oh, Sihun Baek, Jihong Park, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim:
Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix. CoRR abs/2408.01040 (2024) - [i32]Raghav Singhal, Kaustubh Ponkshe, Praneeth Vepakomma:
Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models. CoRR abs/2410.09432 (2024) - 2023
- [j4]Saiteja Utpala, Praneeth Vepakomma, Nina Miolane:
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric. Trans. Mach. Learn. Res. 2023 (2023) - [c13]Praneeth Vepakomma, Yulia Kempner, Rodmy Paredes Alfaro, Ramesh Raskar:
Parallel Quasi-Concave Set Function Optimization for Scalability Even Without Submodularity. HPEC 2023: 1-8 - [c12]Abhishek Singh, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar:
Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release. NeurIPS 2023 - 2022
- [c11]Praneeth Vepakomma, Julia Balla, Ramesh Raskar:
PrivateMail: Supervised Manifold Learning of Deep Features with Privacy for Image Retrieval. AAAI 2022: 8503-8511 - [c10]Gharib Gharibi, Babak Poorebrahim Gilkalaye, Praneeth Vepakomma, Zachi Attia, Riddhiman Das, Suraj Kapa, Ramesh Raskar:
Blind Inference: An Automated Privacy-Preserving Prediction Service using Secure Multi-Party Computation for Medical Applications. AMIA 2022 - [c9]Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar:
Decouple-and-Sample: Protecting Sensitive Information in Task Agnostic Data Release. ECCV (13) 2022: 499-517 - [c8]Gharib Gharibi, Ravi Patel, Anissa Khan, Babak Poorebrahim Gilkalaye, Praneeth Vepakomma, Ramesh Raskar, Steve Penrod, Greg Storm, Riddhiman Das:
An Automated Framework for Distributed Deep Learning-A Tool Demo. ICDCS 2022: 1302-1305 - [c7]Seungeun Oh, Jihong Park, Praneeth Vepakomma, Sihun Baek, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim:
LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning. WWW 2022: 3347-3357 - [p1]Praneeth Vepakomma, Ramesh Raskar:
Split Learning: A Resource Efficient Model and Data Parallel Approach for Distributed Deep Learning. Federated Learning 2022: 439-451 - [i31]Ibrahim Suat Evren, Praneeth Vepakomma, Ramesh Raskar:
The Privacy-Welfare Trade-off: Effects of Differential Privacy on Influence & Welfare in Social Choice. CoRR abs/2201.10115 (2022) - [i30]Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar:
Decouple-and-Sample: Protecting sensitive information in task agnostic data release. CoRR abs/2203.13204 (2022) - [i29]Sihun Baek, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim:
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning. CoRR abs/2207.00234 (2022) - [i28]Praneeth Vepakomma, Mohammad Mohammadi Amiri, Clément L. Canonne, Ramesh Raskar, Alex Pentland:
Private independence testing across two parties. CoRR abs/2207.03652 (2022) - [i27]Seungeun Oh, Jihong Park, Sihun Baek, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim:
Differentially Private CutMix for Split Learning with Vision Transformer. CoRR abs/2210.15986 (2022) - 2021
- [j3]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [c6]Abhishek Singh, Ayush Chopra, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar:
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for Deep Neural Networks. CVPR 2021: 12125-12135 - [c5]Praneeth Vepakomma, Abhishek Singh, Emily Zhang, Otkrist Gupta, Ramesh Raskar:
NoPeek-Infer: Preventing face reconstruction attacks in distributed inference after on-premise training. FG 2021: 1-8 - [c4]Yusuke Koda, Jihong Park, Mehdi Bennis, Praneeth Vepakomma, Ramesh Raskar:
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning. GLOBECOM 2021: 1-6 - [i26]Praneeth Vepakomma, Julia Balla, Ramesh Raskar:
Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval. CoRR abs/2102.10802 (2021) - [i25]Yusuke Koda, Jihong Park, Mehdi Bennis, Praneeth Vepakomma, Ramesh Raskar:
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning. CoRR abs/2105.00395 (2021) - [i24]Praneeth Vepakomma, Yulia Kempner, Ramesh Raskar:
Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity. CoRR abs/2108.08758 (2021) - [i23]Praneeth Vepakomma, Subha Nawer Pushpita, Ramesh Raskar:
Private measurement of nonlinear correlations between data hosted across multiple parties. CoRR abs/2110.09670 (2021) - [i22]Ayush Chopra, Surya Kant Sahu, Abhishek Singh, Abhinav Java, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar:
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning. CoRR abs/2112.01637 (2021) - [i21]Shraman Pal, Mansi Uniyal, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Moongu Jeon, Jinho Choi:
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning. CoRR abs/2112.05929 (2021) - 2020
- [c3]Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, Ramesh Raskar:
NoPeek: Information leakage reduction to share activations in distributed deep learning. ICDM (Workshops) 2020: 933-942 - [i20]Ramesh Raskar, Isabel Schunemann, Rachel Barbar, Kristen Vilcans, Jim Gray, Praneeth Vepakomma, Suraj Kapa, Andrea Nuzzo, Rajiv Gupta, Alex Berke, Dazza Greenwood, Christian Keegan, Shriank Kanaparti, Robson Beaudry, David Stansbury, Beatriz Botero Arcila, Rishank Kanaparti, Vitor F. Pamplona, Francesco M. Benedetti, Alina Clough, Riddhiman Das, Kaushal Jain, Khahlil Louisy, Greg Nadeau, Vitor Pamplona, Steve Penrod, Yasaman Rajaee, Abhishek Singh, Greg Storm, John Werner:
Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic. CoRR abs/2003.08567 (2020) - [i19]Alex Berke, Michiel A. Bakker, Praneeth Vepakomma, Ramesh Raskar, Kent Larson, Alex 'Sandy' Pentland:
Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic. CoRR abs/2003.14412 (2020) - [i18]Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh:
Privacy in Deep Learning: A Survey. CoRR abs/2004.12254 (2020) - [i17]Ramesh Raskar, Greg Nadeau, John Werner, Rachel Barbar, Ashley Mehra, Gabriel Harp, Markus Leopoldseder, Bryan Wilson, Derrick Flakoll, Praneeth Vepakomma, Deepti Pahwa, Robson Beaudry, Emelin Flores, Maciej Popielarz, Akanksha Bhatia, Andrea Nuzzo, Matt Gee, Jay Summet, Rajeev Surati, Bikram Khastgir, Francesco Maria Benedetti, Kristen Vilcans, Sienna Leis, Khahlil Louisy:
COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers. CoRR abs/2006.05812 (2020) - [i16]Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr:
FedML: A Research Library and Benchmark for Federated Machine Learning. CoRR abs/2007.13518 (2020) - [i15]Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, Ramesh Raskar:
SplitNN-driven Vertical Partitioning. CoRR abs/2008.04137 (2020) - [i14]Priyanka Singh, Abhishek Singh, Gabriel Cojocaru, Praneeth Vepakomma, Ramesh Raskar:
PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic. CoRR abs/2008.06648 (2020) - [i13]Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, Ramesh Raskar:
NoPeek: Information leakage reduction to share activations in distributed deep learning. CoRR abs/2008.09161 (2020) - [i12]Abhishek Singh, Ayush Chopra, Vivek Sharma, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Ramesh Raskar:
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks. CoRR abs/2012.11025 (2020)
2010 – 2019
- 2019
- [j2]Praneeth Vepakomma, Yulia Kempner:
Diverse data selection via combinatorial quasi-concavity of distance covariance: A polynomial time global minimax algorithm. Discret. Appl. Math. 265: 182-191 (2019) - [i11]Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish, Aalekh Sharan:
Data Markets to support AI for All: Pricing, Valuation and Governance. CoRR abs/1905.06462 (2019) - [i10]Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta, Ramesh Raskar:
Detailed comparison of communication efficiency of split learning and federated learning. CoRR abs/1909.09145 (2019) - [i9]Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, G. N. Srinivasa Prasanna, Ramesh Raskar:
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest. CoRR abs/1909.12734 (2019) - [i8]Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar:
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries. CoRR abs/1910.02312 (2019) - [i7]Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar:
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations. CoRR abs/1910.03731 (2019) - [i6]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - [i5]Maarten G. Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, Ramesh Raskar:
Split Learning for collaborative deep learning in healthcare. CoRR abs/1912.12115 (2019) - 2018
- [i4]Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar:
Split learning for health: Distributed deep learning without sharing raw patient data. CoRR abs/1812.00564 (2018) - [i3]Sai Sri Sathya, Praneeth Vepakomma, Ramesh Raskar, Ranjan Ramachandra, Santanu Bhattacharya:
A Review of Homomorphic Encryption Libraries for Secure Computation. CoRR abs/1812.02428 (2018) - [i2]Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, Abhimanyu Dubey:
No Peek: A Survey of private distributed deep learning. CoRR abs/1812.03288 (2018) - 2017
- [j1]Prashant Shrivastava, Anupam Shukla, Praneeth Vepakomma, Neera Bhansali, Kshitij Verma:
A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease. Comput. Methods Programs Biomed. 139: 171-179 (2017) - 2016
- [i1]Praneeth Vepakomma, Chetan Tonde, Ahmed M. Elgammal:
Supervised Dimensionality Reduction via Distance Correlation Maximization. CoRR abs/1601.00236 (2016) - 2015
- [c2]Praneeth Vepakomma, Debraj De, Sajal K. Das, Shekhar Bhansali:
A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities. BSN 2015: 1-6 - [c1]Praneeth Vepakomma, Ahmed M. Elgammal:
Iterative Embedding with Robust Correction using Feedback of Error Observed. MLIS@ICML 2015: 36-40
Coauthor Index
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last updated on 2024-12-03 21:21 CET by the dblp team
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