Praneeth Vepakomma

76 papers A* 5A 1B 2C 1Misc 1Journal 57Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Combinatorial Privacy: Private Multi-Party Bitstream Grand Sum by Hiding in Birkhoff Polytopes. CoRR Praneeth Vepakomma
2026	J	`jnl`	Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning. Trans. Mach. Learn. Res. Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak, Lav R. Varshney, Praneeth Vepakomma
2026	J	`jnl`	Learning in the Null Space: Small Singular Values for Continual Learning. CoRR Cuong Anh Pham, Praneeth Vepakomma, Samuel Horváth
2025	J	`jnl`	ABBA: Highly Expressive Hadamard Product Adaptation for Large Language Models. CoRR Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak, Praneeth Vepakomma
2025	J	`jnl`	Convergence of Mean Shift Algorithms for Large Bandwidths and Simultaneous Accurate Clustering. CoRR Susovan Pal, Praneeth Vepakomma
2025	J	`jnl`	DP-Fusion: Token-Level Differentially Private Inference for Large Language Models. CoRR Rushil Thareja, Preslav Nakov, Praneeth Vepakomma, Nils Lukas
2025	J	`jnl`	Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning. CoRR Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak, Lav R. Varshney, Praneeth Vepakomma
2025	—	`conf`	FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Large Language Models. ACL (1) Raghav Singhal, Kaustubh Ponkshe, Praneeth Vepakomma
2025	J	`jnl`	HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models. CoRR Zheng Lin, Yuxin Zhang, Zhe Chen, Zihan Fang, Xianhao Chen, Praneeth Vepakomma, Wei Ni, Jun Luo, Yue Gao
2025	J	`jnl`	Offline and Online KL-Regularized RLHF under Differential Privacy. CoRR Yulian Wu, Rushil Thareja, Praneeth Vepakomma, Francesco Orabona
2025	J	`jnl`	Power Mechanism: Private Tabular Representation Release for Model Agnostic Consumption. CoRR Praneeth Vepakomma, Kaustubh Ponkshe
2025	J	`jnl`	Safety Subspaces are Not Distinct: A Fine-Tuning Case Study. CoRR Kaustubh Ponkshe, Shaan Shah, Raghav Singhal, Praneeth Vepakomma
2025	J	`jnl`	Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach. CoRR Chaouki Ben Issaid, Praneeth Vepakomma, Mehdi Bennis
2025	J	`jnl`	Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach. Trans. Mach. Learn. Res. Chaouki Ben Issaid, Praneeth Vepakomma, Mehdi Bennis
2025	—	`conf`	Towards a Unified Framework for Split Learning. EuroMLSys Boris Radovic, Marco Canini, Samuel Horváth, Veljko Pejovic, Praneeth Vepakomma
2024	A*	`conf`	Data Acquisition via Experimental Design for Data Markets. NeurIPS Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar
2024	J	`jnl`	Data Acquisition via Experimental Design for Decentralized Data Markets. CoRR Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar
2024	—	`conf`	DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images. ECCV (59) Zaid Tasneem, Akshat Dave, Abhishek Singh, Kushagra Tiwary, Praneeth Vepakomma, Ashok Veeraraghavan, Ramesh Raskar
2024	J	`jnl`	DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images. CoRR Zaid Tasneem, Akshat Dave, Abhishek Singh, Kushagra Tiwary, Praneeth Vepakomma, Ashok Veeraraghavan, Ramesh Raskar
2024	—	`conf`	Effects of Privacy-Inducing Noise on Welfare and Influence of Referendum Systems. FORC Ibrahim Suat Evren, Praneeth Vepakomma
2024	J	`jnl`	Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models. CoRR Raghav Singhal, Kaustubh Ponkshe, Praneeth Vepakomma
2024	J	`jnl`	Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning. CoRR Kaustubh Ponkshe, Raghav Singhal, Eduard Gorbunov, Alexey Tumanov, Samuel Horváth, Praneeth Vepakomma
2024	J	`jnl`	Mix2SFL: Two-Way Mixup for Scalable, Accurate, and Communication-Efficient Split Federated Learning. IEEE Trans. Big Data Seungeun Oh, Hyelin Nam, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim
2024	J	`jnl`	Predicting Survival of Hemodialysis Patients using Federated Learning. CoRR Abhiram Raju, Praneeth Vepakomma
2024	J	`jnl`	Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix. CoRR Seungeun Oh, Sihun Baek, Jihong Park, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim
2024	J	`jnl`	Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix. Trans. Mach. Learn. Res. Seungeun Oh, Sihun Baek, Jihong Park, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim
2024	J	`jnl`	SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models. CoRR Zheng Lin, Xuanjie Hu, Yuxin Zhang, Zhe Chen, Zihan Fang, Xianhao Chen, Ang Li, Praneeth Vepakomma, Yue Gao
2023	J	`jnl`	Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric. Trans. Mach. Learn. Res. Saiteja Utpala, Praneeth Vepakomma, Nina Miolane
2023	—	`conf`	Parallel Quasi-Concave Set Function Optimization for Scalability Even Without Submodularity. HPEC Praneeth Vepakomma, Yulia Kempner, Rodmy Paredes Alfaro, Ramesh Raskar
2023	A*	`conf`	Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release. NeurIPS Abhishek Singh, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
2022	A	`conf`	An Automated Framework for Distributed Deep Learning-A Tool Demo. ICDCS Gharib Gharibi, Ravi Patel, Anissa Khan, Babak Poorebrahim Gilkalaye, Praneeth Vepakomma, Ramesh Raskar, Steve Penrod, Greg Storm, Riddhiman Das
2022	Misc	`conf`	Blind Inference: An Automated Privacy-Preserving Prediction Service using Secure Multi-Party Computation for Medical Applications. AMIA Gharib Gharibi, Babak Poorebrahim Gilkalaye, Praneeth Vepakomma, Zachi Attia, Riddhiman Das, Suraj Kapa, Ramesh Raskar
2022	—	`conf`	Decouple-and-Sample: Protecting Sensitive Information in Task Agnostic Data Release. ECCV (13) Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
2022	J	`jnl`	Decouple-and-Sample: Protecting sensitive information in task agnostic data release. CoRR Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
2022	J	`jnl`	Differentially Private CutMix for Split Learning with Vision Transformer. CoRR Seungeun Oh, Jihong Park, Sihun Baek, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim
2022	A*	`conf`	LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning. WWW Seungeun Oh, Jihong Park, Praneeth Vepakomma, Sihun Baek, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim
2022	J	`jnl`	Private independence testing across two parties. CoRR Praneeth Vepakomma, Mohammad Mohammadi Amiri, Clément L. Canonne, Ramesh Raskar, Alex Pentland
2022	A*	`conf`	PrivateMail: Supervised Manifold Learning of Deep Features with Privacy for Image Retrieval. AAAI Praneeth Vepakomma, Julia Balla, Ramesh Raskar
2022	—	`ch.`	Split Learning: A Resource Efficient Model and Data Parallel Approach for Distributed Deep Learning. Federated Learning Praneeth Vepakomma, Ramesh Raskar
2022	J	`jnl`	The Privacy-Welfare Trade-off: Effects of Differential Privacy on Influence & Welfare in Social Choice. CoRR Ibrahim Suat Evren, Praneeth Vepakomma, Ramesh Raskar
2022	J	`jnl`	Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning. CoRR Sihun Baek, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim
2021	J	`jnl`	AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning. CoRR Ayush Chopra, Surya Kant Sahu, Abhishek Singh, Abhinav Java, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2021	B	`conf`	AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning. GLOBECOM Yusuke Koda, Jihong Park, Mehdi Bennis, Praneeth Vepakomma, Ramesh Raskar
2021	J	`jnl`	AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning. CoRR Yusuke Koda, Jihong Park, Mehdi Bennis, Praneeth Vepakomma, Ramesh Raskar
2021	A*	`conf`	DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for Deep Neural Networks. CVPR Abhishek Singh, Ayush Chopra, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
2021	J	`jnl`	Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval. CoRR Praneeth Vepakomma, Julia Balla, Ramesh Raskar
2021	B	`conf`	NoPeek-Infer: Preventing face reconstruction attacks in distributed inference after on-premise training. FG Praneeth Vepakomma, Abhishek Singh, Emily Zhang, Otkrist Gupta, Ramesh Raskar
2021	J	`jnl`	Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity. CoRR Praneeth Vepakomma, Yulia Kempner, Ramesh Raskar
2021	J	`jnl`	Private measurement of nonlinear correlations between data hosted across multiple parties. CoRR Praneeth Vepakomma, Subha Nawer Pushpita, Ramesh Raskar
2021	J	`jnl`	Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning. CoRR Shraman Pal, Mansi Uniyal, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Moongu Jeon, Jinho Choi
2020	J	`jnl`	Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic. CoRR 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
2020	J	`jnl`	Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic. CoRR Alex Berke, Michiel A. Bakker, Praneeth Vepakomma, Ramesh Raskar, Kent Larson, Alex 'Sandy' Pentland
2020	J	`jnl`	COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers. CoRR 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
2020	J	`jnl`	DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks. CoRR Abhishek Singh, Ayush Chopra, Vivek Sharma, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Ramesh Raskar
2020	J	`jnl`	FedML: A Research Library and Benchmark for Federated Machine Learning. CoRR 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
2020	—	`conf`	NoPeek: Information leakage reduction to share activations in distributed deep learning. ICDM (Workshops) Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, Ramesh Raskar
2020	J	`jnl`	NoPeek: Information leakage reduction to share activations in distributed deep learning. CoRR Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, Ramesh Raskar
2020	J	`jnl`	PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic. CoRR Priyanka Singh, Abhishek Singh, Gabriel Cojocaru, Praneeth Vepakomma, Ramesh Raskar
2020	J	`jnl`	Privacy in Deep Learning: A Survey. CoRR Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh
2020	J	`jnl`	SplitNN-driven Vertical Partitioning. CoRR Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, Ramesh Raskar
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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
2019	J	`jnl`	Data Markets to support AI for All: Pricing, Valuation and Governance. CoRR Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish, Aalekh Sharan
2019	J	`jnl`	Detailed comparison of communication efficiency of split learning and federated learning. CoRR Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta, Ramesh Raskar
2019	J	`jnl`	Diverse data selection via combinatorial quasi-concavity of distance covariance: A polynomial time global minimax algorithm. Discret. Appl. Math. Praneeth Vepakomma, Yulia Kempner
2019	J	`jnl`	ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations. CoRR Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar
2019	J	`jnl`	ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries. CoRR Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar
2019	J	`jnl`	Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest. CoRR Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, G. N. Srinivasa Prasanna, Ramesh Raskar
2019	J	`jnl`	Split Learning for collaborative deep learning in healthcare. CoRR Maarten G. Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, Ramesh Raskar
2018	J	`jnl`	A Review of Homomorphic Encryption Libraries for Secure Computation. CoRR Sai Sri Sathya, Praneeth Vepakomma, Ramesh Raskar, Ranjan Ramachandra, Santanu Bhattacharya
2018	J	`jnl`	No Peek: A Survey of private distributed deep learning. CoRR Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, Abhimanyu Dubey
2018	J	`jnl`	Split learning for health: Distributed deep learning without sharing raw patient data. CoRR Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar
2017	J	`jnl`	A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease. Comput. Methods Programs Biomed. Prashant Shrivastava, Anupam Shukla, Praneeth Vepakomma, Neera Bhansali, Kshitij Verma
2016	J	`jnl`	Supervised Dimensionality Reduction via Distance Correlation Maximization. CoRR Praneeth Vepakomma, Chetan Tonde, Ahmed M. Elgammal
2015	C	`conf`	A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities. BSN Praneeth Vepakomma, Debraj De, Sajal K. Das, Shekhar Bhansali
2015	—	`conf`	Iterative Embedding with Robust Correction using Feedback of Error Observed. MLIS@ICML Praneeth Vepakomma, Ahmed M. Elgammal