Joshi, Gauri

37 publications

TMLR 2026 Natural Policy Gradient for Average Reward Non-Stationary Reinforcement Learning Neharika Jali, Eshika Pathak, Pranay Sharma, Guannan Qu, Gauri Joshi

ICLR 2025 Debiasing Federated Learning with Correlated Client Participation Zhenyu Sun, Ziyang Zhang, Zheng Xu, Gauri Joshi, Pranay Sharma, Ermin Wei

ICML 2025 FedECADO: A Dynamical System Model of Federated Learning Aayushya Agarwal, Gauri Joshi, Lawrence Pileggi

AISTATS 2025 Federated Communication-Efficient Multi-Objective Optimization Baris Askin, Pranay Sharma, Gauri Joshi, Carlee Joe-Wong

AISTATS 2025 High-Probability Convergence Bounds for Online Nonlinear Stochastic Gradient Descent Under Heavy-Tailed Noise Aleksandar Armacki, Shuhua Yu, Pranay Sharma, Gauri Joshi, Dragana Bajovic, Dusan Jakovetic, Soummya Kar

TMLR 2025 Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning Divyansh Jhunjhunwala, Pranay Sharma, Zheng Xu, Gauri Joshi

NeurIPS 2025 Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning Arian Raje, Baris Askin, Divyansh Jhunjhunwala, Gauri Joshi

JMLR 2025 The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond Jiin Woo, Gauri Joshi, Yuejie Chi

AISTATS 2024 Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems Neharika Jali, Guannan Qu, Weina Wang, Gauri Joshi

UAI 2024 FedAST: Federated Asynchronous Simultaneous Training Baris Askin, Pranay Sharma, Carlee Joe-Wong, Gauri Joshi

AISTATS 2024 FedFisher: Leveraging Fisher Information for One-Shot Federated Learning Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi

ICML 2024 Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices Jiin Woo, Laixi Shi, Gauri Joshi, Yuejie Chi

TMLR 2024 Maximizing Global Model Appeal in Federated Learning Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi

TMLR 2024 On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang

TMLR 2024 Optimized Tradeoffs for Private Prediction with Majority Ensembling Shuli Jiang, Qiuyi Zhang, Gauri Joshi

NeurIPS 2023 Correlation Aware Sparsified Mean Estimation Using Random Projection Shuli Jiang, Pranay Sharma, Gauri Joshi

ICLR 2023 FedExP: Speeding up Federated Averaging via Extrapolation Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi

AISTATS 2023 Federated Learning Under Distributed Concept Drift Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang, Gauri Joshi, Phillip B. Gibbons

TMLR 2023 Federated Minimax Optimization with Client Heterogeneity Pranay Sharma, Rohan Panda, Gauri Joshi

NeurIPSW 2023 Heterogeneous LoRA for Federated Fine-Tuning of On-Device Foundation Models Yae Jee Cho, Luyang Liu, Zheng Xu, Aldi Fahrezi, Matt Barnes, Gauri Joshi

ICCV 2023 Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels Yae Jee Cho, Gauri Joshi, Dimitrios Dimitriadis

ICML 2023 On the Convergence of Federated Averaging with Cyclic Client Participation Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang

ICML 2023 The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond Jiin Woo, Gauri Joshi, Yuejie Chi

ICMLW 2023 Towards a Theoretical and Practical Understanding of One-Shot Federated Learning with Fisher Information Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi

AISTATS 2022 Towards Understanding Biased Client Selection in Federated Learning Yae Jee Cho, Jianyu Wang, Gauri Joshi

NeurIPSW 2022 Federated Learning Under Distributed Concept Drift Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang, Gauri Joshi, Phillip Gibbons

ICML 2022 Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Pranay Sharma, Rohan Panda, Gauri Joshi, Pramod Varshney

ICML 2022 Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling Sajad Khodadadian, Pranay Sharma, Gauri Joshi, Siva Theja Maguluri

UAI 2022 Fedvarp: Tackling the Variance Due to Partial Client Participation in Federated Learning Divyansh Jhunjhunwala, Pranay Sharma, Aushim Nagarkatti, Gauri Joshi

IJCAI 2022 Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, Dimitrios Dimitriadis

NeurIPSW 2022 To Federate or Not to Federate: Incentivizing Client Participation in Federated Learning Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi

FnTML 2021 Advances and Open Problems in Federated Learning 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

JMLR 2021 Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms Jianyu Wang, Gauri Joshi

UAI 2021 Deep Kernels with Probabilistic Embeddings for Small-Data Learning Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, T. Yong-Jin Han

NeurIPS 2021 Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation Divyansh Jhunjhunwala, Ankur Mallick, Advait Gadhikar, Swanand Kadhe, Gauri Joshi

NeurIPS 2020 Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, H. Vincent Poor

AISTATS 2018 Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-Offs in Distributed SGD Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, Priya Nagpurkar