Publications

BibTeX

@misc{askin2026emergent,
  author = {Baris Askin and Muhammed Ustaomeroglu and Anupam Nayak and Gauri Joshi and Guannan Qu and Carlee Joe-Wong},
  title  = {{Emergent and Subliminal Misalignment Through the Lens of Data-Mediated Transfer}},
  year   = {2026},
  url    = {https://arxiv.org/abs/2605.12798}
}

Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning

Neharika Jali, Anupam Nayak, and Gauri Joshi

Workshop on Efficient Reasoning at Conference on Language Modeling (COLM) 2026

BibTeX

@misc{jali2026not,
  author       = {Neharika Jali and Anupam Nayak and Gauri Joshi},
  title        = {{Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning}},
  howpublished = {Workshop on Efficient Reasoning at Conference on Language Modeling (COLM) 2026},
  year         = {2026},
  url          = {https://arxiv.org/abs/2604.05164}
}

Federate the Router: Learning Language Model Routers with Sparse and Decentralized Evaluations

Baris Askin, Shivam Patel, Anupam Nayak, Andrea Vigano, Jiin Woo, Gauri Joshi, and Carlee Joe-Wong

preprint

BibTeX

@misc{askin2026federate,
  author = {Baris Askin and Shivam Patel and Anupam Nayak and Andrea Vigano and Jiin Woo and Gauri Joshi and Carlee Joe-Wong},
  title  = {{Federate the Router: Learning Language Model Routers with Sparse and Decentralized Evaluations}},
  year   = {2026},
  url    = {https://www.arxiv.org/abs/2601.22318}
}

MELINOE: Fine-Tuning Enables Memory-Efficient Inference for Mixture-of-Experts Models

Arian Raje, Anupam Nayak, and Gauri Joshi

preprint

BibTeX

@misc{raje2026melinoe,
  author = {Arian Raje and Anupam Nayak and Gauri Joshi},
  title  = {{MELINOE: Fine-Tuning Enables Memory-Efficient Inference for Mixture-of-Experts Models}},
  year   = {2026},
  url    = {https://arxiv.org/pdf/2602.11192}
}

Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning

Baris Askin, Holger R. Roth, Zhenyu Sun, Carlee Joe-Wong, Gauri Joshi, and Ziyue Xu

preprint

BibTeX

@misc{askin2026reviving,
  author = {Baris Askin and Holger R. Roth and Zhenyu Sun and Carlee Joe-Wong and Gauri Joshi and Ziyue Xu},
  title  = {{Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning}},
  year   = {2026},
  url    = {https://arxiv.org/abs/2511.00655}
}

Sample Complexity of Average-Reward Q-Learning: From Single-agent to Federated Reinforcement Learning

Yuchen Jiao, Jiin Woo, Gen Li, Gauri Joshi, and Yuejie Chi

preprint

BibTeX

@misc{jiao2026sample,
  author = {Yuchen Jiao and Jiin Woo and Gen Li and Gauri Joshi and Yuejie Chi},
  title  = {{Sample Complexity of Average-Reward Q-Learning: From Single-agent to Federated Reinforcement Learning}},
  year   = {2026},
  url    = {https://arxiv.org/abs/2601.13642}
}

FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA

Divyansh Jhunjhunwala, Arian Raje, Madan Ravi Ganesh, Chaithanya Kumar Mummadi, Chaoqun Dong, Jiawei Zhou, Wan-Yi Lin, Gauri Joshi, and Zhenzhen Li

preprint

BibTeX

@misc{jhunjhunwala2026fedrpca,
  author = {Divyansh Jhunjhunwala and Arian Raje and Madan Ravi Ganesh and Chaithanya Kumar Mummadi and Chaoqun Dong and Jiawei Zhou and Wan-Yi Lin and Gauri Joshi and Zhenzhen Li},
  title  = {{FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA}},
  year   = {2026},
  url    = {https://arxiv.org/abs/2506.01194}
}

2026

FedGuide: Diffusion Prior Alignment and Value Baseline Guidance for Heterogeneous Federated Reinforcement Learning

Hector He and Gauri Joshi

Conference on Robot Learning (CoRL), 2026

BibTeX

@inproceedings{he2026fedguide,
  author    = {Hector He and Gauri Joshi},
  title     = {{FedGuide: Diffusion Prior Alignment and Value Baseline Guidance for Heterogeneous Federated Reinforcement Learning}},
  booktitle = {Conference on Robot Learning (CoRL), 2026},
  year      = {2026}
}

LOCUS: Low-Dimensional Model Embeddings for Efficient Model Exploration, Comparison and Selection

Shivam Patel, William Cocke, and Gauri Joshi

Proceedings of the AdaptFM workshop at the International Conference on Machine Learning (ICML) 2026

arXiv project page

BibTeX

@inproceedings{patel2026lowdimensional,
  author    = {Shivam Patel and William Cocke and Gauri Joshi},
  title     = {{LOCUS: Low-Dimensional Model Embeddings for Efficient Model Exploration, Comparison and Selection}},
  booktitle = {Proceedings of the AdaptFM workshop at the International Conference on Machine Learning (ICML) 2026},
  year      = {2026},
  url       = {https://arxiv.org/abs/2601.21082}
}

PubSwap: Public-Data Off-Policy Coordination for Federated RLVR

Anupam Nayak, Baris Askin, Muhammed Ustaomeroglu, Carlee Joe-Wong, and Gauri Joshi

Proceedings of the RLxF and DEMO workshops at the International Conference on Machine Learning (ICML) 2026

BibTeX

@inproceedings{nayak2026pubswap,
  author    = {Anupam Nayak and Baris Askin and Muhammed Ustaomeroglu and Carlee Joe-Wong and Gauri Joshi},
  title     = {{PubSwap: Public-Data Off-Policy Coordination for Federated RLVR}},
  booktitle = {Proceedings of the RLxF and DEMO workshops at the International Conference on Machine Learning (ICML) 2026},
  year      = {2026},
  url       = {https://arxiv.org/abs/2604.12160}
}

Adaptive Federated Learning via Dynamical System Model

Aayushya Agarwal, Gauri Joshi, and Larry Pileggi

Transactions of Machine Learning Research, May 2026

shorter version appeared in the Proceedings of the NeurIPS DynaFront workshop, Dec 2025

BibTeX

@article{agarwal2026adaptive,
  author  = {Aayushya Agarwal and Gauri Joshi and Larry Pileggi},
  title   = {{Adaptive Federated Learning via Dynamical System Model}},
  journal = {Transactions of Machine Learning Research},
  year    = {2026},
  month   = {5},
  url     = {https://arxiv.org/abs/2510.04203}
}

Improving the Convergence of Private Shuffled Gradient Methods with Public Data

Shuli Jiang, Pranay Sharma, Steven Wu, and Gauri Joshi

Conference on Uncertainty in Artificial Intelligence (UAI) 2026

BibTeX

@inproceedings{jiang2026improving,
  author    = {Shuli Jiang and Pranay Sharma and Steven Wu and Gauri Joshi},
  title     = {{Improving the Convergence of Private Shuffled Gradient Methods with Public Data}},
  booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI) 2026},
  year      = {2026},
  url       = {https://arxiv.org/abs/2502.03652}
}

Achieving Logarithmic Regret in KL-Regularized Zero-Sum Markov Games

Anupam Nayak, Tong Yang, Osman Yagan, Gauri Joshi, and Yuejie Chi

International Conference on Machine Learning (ICML), July 2026

BibTeX

@inproceedings{nayak2026achieving,
  author    = {Anupam Nayak and Tong Yang and Osman Yagan and Gauri Joshi and Yuejie Chi},
  title     = {{Achieving Logarithmic Regret in KL-Regularized Zero-Sum Markov Games}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2026},
  month     = {7},
  url       = {https://www.arxiv.org/abs/2510.13060}
}

Federated Learning is a Lens towards a Democratized Future for the Scaling Law Era

Harry Jiang, Baris Askin, Gauri Joshi, and Carlee Joe-Wong

International Conference on Machine Learning (ICML) position paper, July 2026

BibTeX

@inproceedings{jiang2026federated,
  author    = {Harry Jiang and Baris Askin and Gauri Joshi and Carlee Joe-Wong},
  title     = {{Federated Learning is a Lens towards a Democratized Future for the Scaling Law Era}},
  booktitle = {International Conference on Machine Learning (ICML) position paper},
  year      = {2026},
  month     = {7}
}

ProxRouter: Proximity-Weighted LLM Query Routing for Improved Robustness to Outliers

Shivam Patel, Neharika Jali, Ankur Mallick, and Gauri Joshi

International Conference on Artificial Intelligence and Statistics (AISTATS), May 2026

BibTeX

@inproceedings{patel2026proxrouter,
  author    = {Shivam Patel and Neharika Jali and Ankur Mallick and Gauri Joshi},
  title     = {{ProxRouter: Proximity-Weighted LLM Query Routing for Improved Robustness to Outliers}},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year      = {2026},
  month     = {5},
  url       = {https://arxiv.org/abs/2510.09852}
}

Internal Planning in Language Models: Characterizing Horizon and Branch Awareness

Muhammed Ustaomeroglu, Baris Askin, Gauri Joshi, Carlee Joe-Wong, and Guannan Qu

International Conference on Learning Representations (ICLR), Apr 2026

short version presented at the DeepMath Conference on the Mathematical Theory of Deep Neural Networks, Nov 2025

BibTeX

@inproceedings{ustaomeroglu2026internal,
  author    = {Muhammed Ustaomeroglu and Baris Askin and Gauri Joshi and Carlee Joe-Wong and Guannan Qu},
  title     = {{Internal Planning in Language Models: Characterizing Horizon and Branch Awareness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2026},
  month     = {4},
  url       = {https://arxiv.org/abs/2509.25260}
}

Navigating the Accuracy-Size Trade-Off with Flexible Model Merging

Akash Dhasade, Divyansh Jhunjhunwala, Milos Vujasinovic, Gauri Joshi, and Anne-Marie Kermarrec

International Conference on Learning Representations (ICLR), Apr 2026

BibTeX

@inproceedings{dhasade2026navigating,
  author    = {Akash Dhasade and Divyansh Jhunjhunwala and Milos Vujasinovic and Gauri Joshi and Anne-Marie Kermarrec},
  title     = {{Navigating the Accuracy-Size Trade-Off with Flexible Model Merging}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2026},
  month     = {4},
  url       = {https://arxiv.org/abs/2505.23209}
}

Natural Policy Gradient for Average Reward Non-Stationary RL

Neharika Jali, Eshika Pathak, Pranay Sharma, Guannan Qu, and Gauri Joshi

Transactions on Machine Learning Research (TMLR), Jan 2026

TMLR 2026 arXiv

BibTeX

@article{jali2026natural,
  author  = {Neharika Jali and Eshika Pathak and Pranay Sharma and Guannan Qu and Gauri Joshi},
  title   = {{Natural Policy Gradient for Average Reward Non-Stationary RL}},
  journal = {Transactions on Machine Learning Research (TMLR)},
  year    = {2026},
  month   = {1},
  url     = {https://arxiv.org/abs/2504.16415}
}

2025

Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning

Arian Raje, Baris Askin, Divyansh Jhunjhunwala, and Gauri Joshi

Neural Information Processing Systems (NeurIPS), Dec 2025

NeurIPS 2025 arXiv

BibTeX

@inproceedings{raje2025ravan,
  author    = {Arian Raje and Baris Askin and Divyansh Jhunjhunwala and Gauri Joshi},
  title     = {{Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning}},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year      = {2025},
  month     = {12},
  url       = {https://www.arxiv.org/abs/2506.05568}
}

Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning

Divyansh Jhunjhunwala, Pranay Sharma, Zheng Xu, and Gauri Joshi

Transactions on Machine Learning Research (TMLR), Oct 2025

TMLR 2025 arXiv

BibTeX

@article{jhunjhunwala2025initialization,
  author  = {Divyansh Jhunjhunwala and Pranay Sharma and Zheng Xu and Gauri Joshi},
  title   = {{Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning}},
  journal = {Transactions on Machine Learning Research (TMLR)},
  year    = {2025},
  month   = {10},
  url     = {https://arxiv.org/abs/2502.08024}
}

FedECADO: A Dynamical System Model of Federated Learning

Aayushya Agarwal, Gauri Joshi, and Larry Pileggi

International Conference on Machine Learning (ICML), July 2025

ICML 2025 arXiv

BibTeX

@inproceedings{agarwal2025fedecado,
  author    = {Aayushya Agarwal and Gauri Joshi and Larry Pileggi},
  title     = {{FedECADO: A Dynamical System Model of Federated Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2025},
  month     = {7},
  url       = {https://arxiv.org/abs/2410.09933}
}

Federated Communication-Efficient Multi-Objective Optimization

Baris Askin, Pranay Sharma, Carlee Joe-Wong, and Gauri Joshi

International Conference on Artificial Intelligence and Statistics (AISTATS), May 2025

AISTATS 2025 arXiv

BibTeX

@inproceedings{askin2025federated,
  author    = {Baris Askin and Pranay Sharma and Carlee Joe-Wong and Gauri Joshi},
  title     = {{Federated Communication-Efficient Multi-Objective Optimization}},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year      = {2025},
  month     = {5},
  url       = {https://arxiv.org/abs/2410.16398}
}

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, and Soummya Kar

International Conference on Artificial Intelligence and Statistics (AISTATS), May 2025

AISTATS 2025 arXiv

BibTeX

@inproceedings{armacki2025highprobability,
  author    = {Aleksandar Armacki and Shuhua Yu and Pranay Sharma and Gauri Joshi and Dragana Bajovic and Dusan Jakovetic and Soummya Kar},
  title     = {{High-probability Convergence Bounds for Online Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise}},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year      = {2025},
  month     = {5},
  url       = {https://arxiv.org/abs/2310.18784}
}

Debiasing Federated Learning with Correlated Client Participation

Zhenyu Sun, Ziyang Zhang, Zheng Xu, Gauri Joshi, Pranay Sharma, and Ermin Wei

International Conference on Learning Representations (ICLR), Apr 2025

ICLR 2025 arXiv

BibTeX

@inproceedings{sun2025debiasing,
  author    = {Zhenyu Sun and Ziyang Zhang and Zheng Xu and Gauri Joshi and Pranay Sharma and Ermin Wei},
  title     = {{Debiasing Federated Learning with Correlated Client Participation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2025},
  month     = {4},
  url       = {https://arxiv.org/abs/2410.01209}
}

The Blessing of Heterogeneity in Federated Q-learning: Linear Speedup and Beyond

Jiin Woo, Gauri Joshi, and Yuejie Chi

Journal on Machine Learning Research (JMLR), Feb 2025

JMLR 2025 arXiv

BibTeX

@article{woo2025the,
  author  = {Jiin Woo and Gauri Joshi and Yuejie Chi},
  title   = {{The Blessing of Heterogeneity in Federated Q-learning: Linear Speedup and Beyond}},
  journal = {Journal on Machine Learning Research (JMLR)},
  year    = {2025},
  month   = {2},
  url     = {https://arxiv.org/abs/2305.10697}
}

2024

Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees

Aleksandar Armacki, Shuhua Yu, Pranay Sharma, Gauri Joshi, Dragana Bajovic, Dusan Jakovetic, and Soummya Kar

preprint

BibTeX

@misc{armacki2024nonlinear,
  author = {Aleksandar Armacki and Shuhua Yu and Pranay Sharma and Gauri Joshi and Dragana Bajovic and Dusan Jakovetic and Soummya Kar},
  title  = {{Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees}},
  year   = {2024},
  url    = {https://arxiv.org/abs/2410.13954}
}

Job Assignment in Machine Learning Inference Systems with Accuracy Constraints

Tuhinangshu Choudhury, Weina Wang, and Gauri Joshi

Performance Evaluation Journal, Elsevier, Dec 2024

BibTeX

@article{choudhury2024job,
  author  = {Tuhinangshu Choudhury and Weina Wang and Gauri Joshi},
  title   = {{Job Assignment in Machine Learning Inference Systems with Accuracy Constraints}},
  journal = {Performance Evaluation Journal, Elsevier},
  year    = {2024},
  month   = {12},
  url     = {https://www.sciencedirect.com/science/article/pii/S0166531624000683}
}

Optimized Tradeoffs for Private Majority Ensembling

Shuli Jiang, Qiuyu Zhang, and Gauri Joshi

Transaction on Machine Learning Research (TMLR), 2024

TMLR 2024 arXiv

BibTeX

@misc{jiang2024optimized,
  author       = {Shuli Jiang and Qiuyu Zhang and Gauri Joshi},
  title        = {{Optimized Tradeoffs for Private Majority Ensembling}},
  howpublished = {Transaction on Machine Learning Research (TMLR), 2024},
  year         = {2024},
  url          = {https://arxiv.org/abs/2411.17965}
}

Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models

Yae Jee Cho, Luyang Liu, Zheng Xu, Aldi Fahrezi, Matt Barnes, and Gauri Joshi

Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2024

shorter version in Workshop on Federated Learning in the Age of Foundation Models, Neural Information Processing Systems (NeurIPS), Dec 2023

EMNLP 2024 arXiv

BibTeX

@inproceedings{cho2024heterogeneous,
  author    = {Yae Jee Cho and Luyang Liu and Zheng Xu and Aldi Fahrezi and Matt Barnes and Gauri Joshi},
  title     = {{Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models}},
  booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year      = {2024},
  month     = {11},
  url       = {https://arxiv.org/abs/2401.06432}
}

Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices

Jiin Woo, Laixi Shi, Gauri Joshi, and Yuejie Chi

International Conference on Machine Learning (ICML), July 2024

ICML 2024 arXiv

BibTeX

@inproceedings{woo2024federated,
  author    = {Jiin Woo and Laixi Shi and Gauri Joshi and Yuejie Chi},
  title     = {{Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2024},
  month     = {7},
  url       = {https://arxiv.org/abs/2402.05876}
}

FedAST: Federated Asynchronous Simultaneous Training

Baris Askin, Pranay Sharma, Carlee Joe-Wong, and Gauri Joshi

Conference on Uncertainty in Artificial Intelligence (UAI), July 2024

UAI 2024 arXiv

BibTeX

@inproceedings{askin2024fedast,
  author    = {Baris Askin and Pranay Sharma and Carlee Joe-Wong and Gauri Joshi},
  title     = {{FedAST: Federated Asynchronous Simultaneous Training}},
  booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI)},
  year      = {2024},
  month     = {7},
  url       = {https://arxiv.org/abs/2406.00302}
}

Erasure Coded Neural Network Inference via Fisher Averaging

Divyansh Jhunjhunwala, Neharika Jali, Gauri Joshi, and Shiqiang Wang

International Symposium on Information Theory (ISIT), July 2024

ISIT 2024 arXiv

BibTeX

@inproceedings{jhunjhunwala2024erasure,
  author    = {Divyansh Jhunjhunwala and Neharika Jali and Gauri Joshi and Shiqiang Wang},
  title     = {{Erasure Coded Neural Network Inference via Fisher Averaging}},
  booktitle = {International Symposium on Information Theory (ISIT)},
  year      = {2024},
  month     = {7},
  url       = {https://arxiv.org/abs/2409.01420}
}

On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data

Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, and Tong Zhang

Transactions of Machine Learning Research (TMLR), June 2024

TMLR 2024 arXiv

BibTeX

@article{wang2024on,
  author  = {Jianyu Wang and Rudrajit Das and Gauri Joshi and Satyen Kale and Zheng Xu and Tong Zhang},
  title   = {{On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data}},
  journal = {Transactions of Machine Learning Research (TMLR)},
  year    = {2024},
  month   = {6},
  url     = {http://arxiv.org/abs/2206.04723}
}

Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems

Neharika Jali, Guannan Qu, Weina Wang, and Gauri Joshi

International Conference on Artificial Intelligence and Statistics (AISTATS), May 2024

AISTATS 2024 arXiv

BibTeX

@inproceedings{jali2024efficient,
  author    = {Neharika Jali and Guannan Qu and Weina Wang and Gauri Joshi},
  title     = {{Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems}},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year      = {2024},
  month     = {5},
  url       = {https://arxiv.org/abs/2402.01147}
}

FedFisher: Leveraging Fisher Information for One-Shot Federated Learning

Divyansh Jhunjhunwala, Shiqiang Wang, and Gauri Joshi

International Conference on Artificial Intelligence and Statistics (AISTATS), May 2024

AISTATS 2024 arXiv

BibTeX

@inproceedings{jhunjhunwala2024fedfisher,
  author    = {Divyansh Jhunjhunwala and Shiqiang Wang and Gauri Joshi},
  title     = {{FedFisher: Leveraging Fisher Information for One-Shot Federated Learning}},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year      = {2024},
  month     = {5},
  url       = {https://arxiv.org/abs/2403.12329}
}

Maximizing Global Model Appeal in Federated Learning

Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, and Gauri Joshi

Transactions on Machine Learning Research (TMLR), Apr 2024

TMLR 2024 arXiv

BibTeX

@article{cho2024maximizing,
  author  = {Yae Jee Cho and Divyansh Jhunjhunwala and Tian Li and Virginia Smith and Gauri Joshi},
  title   = {{Maximizing Global Model Appeal in Federated Learning}},
  journal = {Transactions on Machine Learning Research (TMLR)},
  year    = {2024},
  month   = {4},
  url     = {https://arxiv.org/abs/2205.14840}
}

On Improved Distributed Random Reshuffling Over Networks

Pranay Sharma, Jiarui Li, and Gauri Joshi

International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2024

ICASSP 2024

BibTeX

@inproceedings{sharma2024on,
  author    = {Pranay Sharma and Jiarui Li and Gauri Joshi},
  title     = {{On Improved Distributed Random Reshuffling Over Networks}},
  booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
  year      = {2024},
  month     = {4},
  url       = {https://ieeexplore.ieee.org/abstract/document/10447202}
}

2023

Correlation aware Sparsified Mean Estimation Using Random Projection

Shuli Jiang, Pranay Sharma, and Gauri Joshi

Neural Information Processing Systems (NeurIPS), Dec 2023

NeurIPS 2023 arXiv

BibTeX

@inproceedings{jiang2023correlation,
  author    = {Shuli Jiang and Pranay Sharma and Gauri Joshi},
  title     = {{Correlation aware Sparsified Mean Estimation Using Random Projection}},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year      = {2023},
  month     = {12},
  url       = {https://arxiv.org/abs/2310.18868}
}

Federated Minimax Optimization with Client Heterogeneity

Pranay Sharma, Rohan Panda, and Gauri Joshi

Transactions on Machine Learning Research (TMLR), Dec 2023

TMLR 2023 arXiv

BibTeX

@article{sharma2023federated,
  author  = {Pranay Sharma and Rohan Panda and Gauri Joshi},
  title   = {{Federated Minimax Optimization with Client Heterogeneity}},
  journal = {Transactions on Machine Learning Research (TMLR)},
  year    = {2023},
  month   = {12},
  url     = {https://arxiv.org/abs/2302.04249}
}

Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels

Yae Jee Cho, Gauri Joshi, and Dimitrios Dimitriadis

International Conference on Computer Vision (ICCV), Oct 2023

ICCV 2023 arXiv

BibTeX

@inproceedings{cho2023local,
  author    = {Yae Jee Cho and Gauri Joshi and Dimitrios Dimitriadis},
  title     = {{Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels}},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year      = {2023},
  month     = {10},
  url       = {http://arxiv.org/abs/2307.08809}
}

Towards a Theoretical and Practical Understanding of One-Shot Federated Learning with Fisher Information

Divyansh Jhunjhunwala, Shiqiang Wang, and Gauri Joshi

Federated Learning and Analytics workshop at ICML, July 2023

BibTeX

@inproceedings{jhunjhunwala2023towards,
  author    = {Divyansh Jhunjhunwala and Shiqiang Wang and Gauri Joshi},
  title     = {{Towards a Theoretical and Practical Understanding of One-Shot Federated Learning with Fisher Information}},
  booktitle = {Federated Learning and Analytics workshop at ICML},
  year      = {2023},
  month     = {7},
  url       = {https://openreview.net/forum?id=YjvTJlcb8T}
}

The Blessing of Heterogeneity in Federated Q-learning: Linear Speedup and Beyond

Jiin Woo, Gauri Joshi, and Yuejie Chi

International Conference on Machine Learning (ICML), July 2023

ICML 2023 arXiv

BibTeX

@inproceedings{woo2023the,
  author    = {Jiin Woo and Gauri Joshi and Yuejie Chi},
  title     = {{The Blessing of Heterogeneity in Federated Q-learning: Linear Speedup and Beyond}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2023},
  month     = {7},
  url       = {https://arxiv.org/abs/2305.10697}
}

On the Convergence of Federated Averaging with Cyclic Client Participation

Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, and Tong Zhang

International Conference on Machine Learning (ICML), July 2023

ICML 2023 arXiv

BibTeX

@inproceedings{cho2023on,
  author    = {Yae Jee Cho and Pranay Sharma and Gauri Joshi and Zheng Xu and Satyen Kale and Tong Zhang},
  title     = {{On the Convergence of Federated Averaging with Cyclic Client Participation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2023},
  month     = {7},
  url       = {https://arxiv.org/abs/2302.03109}
}

FedExP: Speeding up Federated Averaging via Extrapolation

Divyansh Jhunjhunwala, Shiqiang Wang, and Gauri Joshi

International Conference on Learning Representations (ICLR), May 2023

Selected for a Spotlight presentation (top 25% of accepted papers)

ICLR 2023 arXiv

BibTeX

@inproceedings{jhunjhunwala2023fedexp,
  author    = {Divyansh Jhunjhunwala and Shiqiang Wang and Gauri Joshi},
  title     = {{FedExP: Speeding up Federated Averaging via Extrapolation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2023},
  month     = {5},
  url       = {https://arxiv.org/abs/2301.09604}
}

Federated Learning under Distributed Concept Drift
Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang, Gauri Joshi, and Phillip B. Gibbons

International Conference on Artificial Intelligence and Statistics (AISTATS), Apr 2023

Selected for an Oral presentation (top 6% of accepted papers)

short version presented at the Workshop on Distribution Shifts at Neural Information Processing Systems, Dec 2022
AISTATS 2023arXiv
BibTeX
```
@inproceedings{jothimurugesan2023federated,
  author    = {Ellango Jothimurugesan and Kevin Hsieh and Jianyu Wang and Gauri Joshi and Phillip B. Gibbons},
  title     = {{Federated Learning under Distributed Concept Drift}},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year      = {2023},
  month     = {4},
  url       = {https://arxiv.org/abs/2206.00799}
}
```

Communication-Efficient and Model-Heterogeneous Personalized Federated Learning via Clustered Knowledge Transfer

Yae Jee Cho, Jianyu Wang, Tarun Chiruvolu, and Gauri Joshi

IEEE Journal of Selected Topics in Signal Processing, Jan 2023

BibTeX

@article{cho2023communicationefficient,
  author  = {Yae Jee Cho and Jianyu Wang and Tarun Chiruvolu and Gauri Joshi},
  title   = {{Communication-Efficient and Model-Heterogeneous Personalized Federated Learning via Clustered Knowledge Transfer}},
  journal = {IEEE Journal of Selected Topics in Signal Processing},
  year    = {2023},
  month   = {1},
  url     = {https://arxiv.org/abs/2109.08119}
}

Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer

Yae Jee Cho, Jianyu Wang, Tarun Chiruvolu, and Gauri Joshi

IEEE Journal of Selected Topics in Signal Processing, 2023

BibTeX

@article{cho2023personalized,
  author  = {Yae Jee Cho and Jianyu Wang and Tarun Chiruvolu and Gauri Joshi},
  title   = {{Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer}},
  journal = {IEEE Journal of Selected Topics in Signal Processing, 2023},
  year    = {2023},
  url     = {https://arxiv.org/abs/2109.08119}
}

2022

Optimization Algorithms for Distributed Machine Learning

Gauri Joshi

Spring Nature Synthesis Lectures on Learning, Networking and Algorithms, Dec 2022

BibTeX

@misc{joshi2022optimization,
  author       = {Gauri Joshi},
  title        = {{Optimization Algorithms for Distributed Machine Learning}},
  howpublished = {Spring Nature Synthesis Lectures on Learning, Networking and Algorithms},
  year         = {2022},
  month        = {12},
  url          = {https://link.springer.com/book/10.1007/978-3-031-19067-4}
}

To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning

Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, and Gauri Joshi

FedML Workshop at Neural Information Processing Systems, Dec 2022

Selected for an oral presentation

BibTeX

@inproceedings{cho2022to,
  author    = {Yae Jee Cho and Divyansh Jhunjhunwala and Tian Li and Virginia Smith and Gauri Joshi},
  title     = {{To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning}},
  booktitle = {FedML Workshop at Neural Information Processing Systems},
  year      = {2022},
  month     = {12},
  url       = {https://openreview.net/forum?id=pG08eM0CQba}
}

Rateless Sum Recovery Codes for Distributed Non-linear Computations

Ankur Mallick and Gauri Joshi

Information Theory Workshop (ITW), Nov 2022

ITW 2022

BibTeX

@inproceedings{mallick2022rateless,
  author    = {Ankur Mallick and Gauri Joshi},
  title     = {{Rateless Sum Recovery Codes for Distributed Non-linear Computations}},
  booktitle = {Information Theory Workshop (ITW)},
  year      = {2022},
  month     = {11},
  url       = {https://par.nsf.gov/servlets/purl/10389236}
}

Multi-Model Federated Learning with Provable Guarantees

Neelkamal Bhuyan, Sharayu Moharir, and Gauri Joshi

EAI Valuetools, Nov 2022

BibTeX

@misc{bhuyan2022multimodel,
  author       = {Neelkamal Bhuyan and Sharayu Moharir and Gauri Joshi},
  title        = {{Multi-Model Federated Learning with Provable Guarantees}},
  howpublished = {EAI Valuetools},
  year         = {2022},
  month        = {11},
  url          = {https://arxiv.org/abs/2207.04330}
}

MATCHA: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization

Jianyu Wang, Anit Sahu, Gauri Joshi, and Soummya Kar

IEEE Transactions on Signal Processing, Oct 2022

BibTeX

@article{wang2022matcha,
  author  = {Jianyu Wang and Anit Sahu and Gauri Joshi and Soummya Kar},
  title   = {{MATCHA: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization}},
  journal = {IEEE Transactions on Signal Processing},
  year    = {2022},
  month   = {10},
  url     = {https://arxiv.org/abs/1905.09435}
}

Tackling Heterogeneous Traffic in Multi-access Systems via Erasure Coded Servers

Tuhinangshu Choudhury, Weina Wang, and Gauri Joshi

ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Oct 2022

Best Paper Award

BibTeX

@inproceedings{choudhury2022tackling,
  author    = {Tuhinangshu Choudhury and Weina Wang and Gauri Joshi},
  title     = {{Tackling Heterogeneous Traffic in Multi-access Systems via Erasure Coded Servers}},
  booktitle = {ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)},
  year      = {2022},
  month     = {10},
  url       = {https://arxiv.org/abs/2207.03983}
}

Correlated Combinatorial Bandits for Online Resource Allocation

Samarth Gupta, Jinhang Zuo, Carlee Joe-Wong, Gauri Joshi, and Osman Yagan

ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Oct 2022

Best Poster Award for a short version presented at SIGMETRICS 2022

BibTeX

@inproceedings{gupta2022correlated,
  author    = {Samarth Gupta and Jinhang Zuo and Carlee Joe-Wong and Gauri Joshi and Osman Yagan},
  title     = {{Correlated Combinatorial Bandits for Online Resource Allocation}},
  booktitle = {ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)},
  year      = {2022},
  month     = {10}
}

FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning

Divyansh Jhunjhunwala, Pranay Sharma, Aushim Nagarkatti, and Gauri Joshi

Conference on Uncertainty in Artificial Intelligence (UAI), Aug 2022

UAI 2022 arXiv

BibTeX

@inproceedings{jhunjhunwala2022fedvarp,
  author    = {Divyansh Jhunjhunwala and Pranay Sharma and Aushim Nagarkatti and Gauri Joshi},
  title     = {{FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning}},
  booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI)},
  year      = {2022},
  month     = {8},
  url       = {https://arxiv.org/abs/2207.14130}
}

Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling

Sajad Khodadadian, Pranay Sharma, Gauri Joshi, and Siva Theja Maguluri

International Conference on Machine Learning (ICML), July 2022

selected for a long presentation (2.1% of submitted papers)

ICML 2022 arXiv

BibTeX

@inproceedings{khodadadian2022federated,
  author    = {Sajad Khodadadian and Pranay Sharma and Gauri Joshi and Siva Theja Maguluri},
  title     = {{Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2022},
  month     = {7},
  url       = {https://arxiv.org/abs/2206.10185}
}

Federated Minimax Optimization: Improved Convergence Analyses and Algorithms

Pranay Sharma, Rohan Panda, Gauri Joshi, and Pramod K. Varshney

International Conference on Machine Learning (ICML), July 2022

ICML 2022 arXiv

BibTeX

@inproceedings{sharma2022federated,
  author    = {Pranay Sharma and Rohan Panda and Gauri Joshi and Pramod K. Varshney},
  title     = {{Federated Minimax Optimization: Improved Convergence Analyses and Algorithms}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2022},
  month     = {7},
  url       = {https://arxiv.org/abs/2203.04850}
}

Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning

Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, and Dimitrios Dimitriadis

International Joint Conference on Artificial Intelligence (IJCAI), July 2022

IJCAI 2022 arXiv

BibTeX

@inproceedings{cho2022heterogeneous,
  author    = {Yae Jee Cho and Andre Manoel and Gauri Joshi and Robert Sim and Dimitrios Dimitriadis},
  title     = {{Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning}},
  booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
  year      = {2022},
  month     = {7},
  url       = {https://arxiv.org/abs/2204.12703}
}

Chapter on Communication-Efficient Distributed Optimization Algorithms

Shiqiang Wang and Gauri Joshi

in the Book on "Federated Learning: A Comprehensive Overview of Methods and Applications", edited by Heiko Ludwig and Nathalie Baracaldo

Springer Publications, July 2022

BibTeX

@incollection{wang2022chapter,
  author    = {Shiqiang Wang and Gauri Joshi},
  title     = {{Chapter on Communication-Efficient Distributed Optimization Algorithms}},
  booktitle = {in the Book on "Federated Learning: A Comprehensive Overview of Methods and Applications", edited by Heiko Ludwig and Nathalie Baracaldo},
  year      = {2022},
  url       = {https://www.barnesandnoble.com/w/federated-learning-heiko-ludwig/1140928320}
}

Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-vector Multiplication

Ankur Mallick, Malhar Chaudhari, Ganesh Palanikumar, Utsav Sheth, and Gauri Joshi

featured as a Research Highlight in the Communications of the ACM, May 2022

BibTeX

@article{mallick2022ratelessb,
  author  = {Ankur Mallick and Malhar Chaudhari and Ganesh Palanikumar and Utsav Sheth and Gauri Joshi},
  title   = {{Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-vector Multiplication}},
  journal = {\highlightfeatured as a Research Highlight in the Communications of the ACM},
  year    = {2022},
  month   = {5},
  url     = {https://dl.acm.org/doi/abs/10.1145/3524298}
}

Matchmaker: Data Drift Mitigation in Machine Learning for Large-Scale Systems

Ankur Mallick, Kevin Hsieh, Behnaz Arzani, and Gauri Joshi

Conference on Machine Learning and Systems (MLSys), Aug 2022

MLSys 2022

BibTeX

@inproceedings{mallick2022matchmaker,
  author    = {Ankur Mallick and Kevin Hsieh and Behnaz Arzani and Gauri Joshi},
  title     = {{Matchmaker: Data Drift Mitigation in Machine Learning for Large-Scale Systems}},
  booktitle = {Conference on Machine Learning and Systems (MLSys)},
  year      = {2022},
  month     = {8},
  url       = {https://proceedings.mlsys.org/paper_files/paper/2022/hash/069a002768bcb31509d4901961f23b3c-Abstract.html}
}

A Dynamic Reweighting Strategy for Fair Federated Learning

Zhiyuan Zhao and Gauri Joshi

International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2022

ICASSP 2022

BibTeX

@inproceedings{zhao2022a,
  author    = {Zhiyuan Zhao and Gauri Joshi},
  title     = {{A Dynamic Reweighting Strategy for Fair Federated Learning}},
  booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
  year      = {2022},
  month     = {5},
  url       = {https://ieeexplore.ieee.org/document/9746300}
}

Towards Understanding Biased Client Selection in Federated Learning

Yae Jee Cho, Jianyu Wang, and Gauri Joshi

International Conference on Artificial Intelligence and Statistics (AISTATS), March 2022

AISTATS 2022 arXiv

BibTeX

@inproceedings{cho2022client,
  author    = {Yae Jee Cho and Jianyu Wang and Gauri Joshi},
  title     = {{Towards Understanding Biased Client Selection in Federated Learning}},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year      = {2022},
  month     = {3},
  url       = {https://arxiv.org/abs/2010.01243}
}

Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks

Sen Lin, Ming Shi, Anish Arora, Raef Bassily, Elisa Bertino, Constantine Caramanis, Kaushik Chowdhury, Eylem Ekici, Atilla Eryilmaz, Stratis Ioannidis, Nan Jiang, Gauri Joshi, Jim Kurose, Yingbin Liang, Zhiqiang Lin, Jia Liu, Mingyan Liu, Tommaso Melodia, Aryan Mokhtari, Rob Nowak, Sewoong Oh, Srini Parthasarathy, Chunyi Peng, Hulya Seferoglu, Ness Shroff, Sanjay Shakkottai, Kannan Srinivassan, Ameet Talwalkar, Aylin Yener, and Lei Ying

IEEE International Conference on Collaboration and Internet Computing (CIC), 2022

BibTeX

@inproceedings{lin2022leveraging,
  author    = {Sen Lin and Ming Shi and Anish Arora and Raef Bassily, Elisa Bertino and Constantine Caramanis and Kaushik Chowdhury, Eylem Ekici and Atilla Eryilmaz and Stratis Ioannidis and Nan Jiang and Gauri Joshi and Jim Kurose and Yingbin Liang and Zhiqiang Lin and Jia Liu and Mingyan Liu and Tommaso Melodia and Aryan Mokhtari and Rob Nowak and Sewoong Oh and Srini Parthasarathy and Chunyi Peng and Hulya Seferoglu and Ness Shroff and Sanjay Shakkottai and Kannan Srinivassan and Ameet Talwalkar and Aylin Yener and Lei Ying},
  title     = {{Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks}},
  booktitle = {IEEE International Conference on Collaboration and Internet Computing (CIC), 2022},
  year      = {2022}
}

2021

Local Adaptivity in Federated Learning: Convergence and Consistency

Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary Charles, Luyang Liu, and Gauri Joshi

preprint, 2021

BibTeX

@misc{wang2021local,
  author       = {Jianyu Wang and Zheng Xu and Zachary Garrett and Zachary Charles and Luyang Liu and Gauri Joshi},
  title        = {{Local Adaptivity in Federated Learning: Convergence and Consistency}},
  howpublished = {preprint, 2021},
  year         = {2021},
  url          = {https://arxiv.org/abs/2106.02305}
}

FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients

Jianyu Wang, Hang Qi, Ankit Singh Rawat, Sashank Reddi, Sagar Waghmare, Felix X. Yu, and Gauri Joshi

preprint, 2021

BibTeX

@misc{wang2021fedlite,
  author       = {Jianyu Wang and Hang Qi and Ankit Singh Rawat and Sashank Reddi and Sagar Waghmare and Felix X. Yu and Gauri Joshi},
  title        = {{FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients}},
  howpublished = {preprint, 2021},
  year         = {2021},
  url          = {https://arxiv.org/abs/2201.11865}
}

Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation

Divyansh Jhunjhunwala, Ankur Mallick, Advait Gadhikar, Swanand Kadhe, and Gauri Joshi

Neural Information Processing Systems (NeurIPS), Dec 2021

NeurIPS 2021 arXiv

BibTeX

@inproceedings{jhunjhunwala2021leveraging,
  author    = {Divyansh Jhunjhunwala and Ankur Mallick and Advait Gadhikar and Swanand Kadhe and Gauri Joshi},
  title     = {{Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation}},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year      = {2021},
  month     = {12},
  url       = {https://arxiv.org/pdf/2110.07751.pdf}
}

Runtime estimation for machine learning tasks

Parijat Dube, Gauri Joshi, Priya Nagpurkar, Stefania Costache, Diana Jeanne Arroyo, and Zehra Noman Sura

US Patent number 11200512, Dec 2021

BibTeX

@misc{dube2021runtime,
  author       = {Parijat Dube and Gauri Joshi and Priya Nagpurkar and Stefania Costache and Diana Jeanne Arroyo and Zehra Noman Sura},
  title        = {{Runtime estimation for machine learning tasks}},
  howpublished = {US Patent number 11200512},
  year         = {2021},
  month        = {12},
  url          = {https://patentimages.storage.googleapis.com/c6/b7/38/c3ad6ba7feb9dc/US11200512.pdf}
}

Adaptive learning rate schedule in distributed stochastic gradient descent

Parijat Dube, Sanghamitra Dutta, Gauri Joshi, and Priya Nagpurkar

US Patent number 11182689, Nov 2021

BibTeX

@misc{dube2021adaptive,
  author       = {Parijat Dube and Sanghamitra Dutta and Gauri Joshi and Priya Nagpurkar},
  title        = {{Adaptive learning rate schedule in distributed stochastic gradient descent}},
  howpublished = {US Patent number 11182689},
  year         = {2021},
  month        = {11},
  url          = {https://patents.google.com/patent/US11182689B2/en}
}

Service Rate Region: A New Aspect of Coded Distributed System Design

Mehmet Aktas, Gauri Joshi, Swanand Kadhe, Fatemeh Kazemi, and Emina Soljanin

IEEE Transactions on Information Theory, Oct 2021

BibTeX

@article{aktas2021service,
  author  = {Mehmet Aktas and Gauri Joshi and Swanand Kadhe and Fatemeh Kazemi and Emina Soljanin},
  title   = {{Service Rate Region: A New Aspect of Coded Distributed System Design}},
  journal = {IEEE Transactions on Information Theory},
  year    = {2021},
  month   = {10},
  url     = {https://arxiv.org/pdf/2009.01598.pdf}
}

Rateless Codes for Distributed Non-linear Computations

Ankur Mallick, Sophie Smith, and Gauri Joshi

International Symposium on Topics in Coding, Sept 2021

BibTeX

@inproceedings{mallick2021rateless,
  author    = {Ankur Mallick and Sophie Smith and Gauri Joshi},
  title     = {{Rateless Codes for Distributed Non-linear Computations}},
  booktitle = {International Symposium on Topics in Coding},
  year      = {2021},
  month     = {9},
  url       = {https://ieeexplore.ieee.org/document/9594268}
}

Slow and Stale Gradients Can Win the Race

Sanghamitra Dutta, Jianyu Wang, and Gauri Joshi

Journal on Selected Areas of Information Theory (JSAIT) Special Issue, 2021

JSAIT 2021 arXiv

BibTeX

@article{dutta2021slow,
  author  = {Sanghamitra Dutta and Jianyu Wang and Gauri Joshi},
  title   = {{Slow and Stale Gradients Can Win the Race}},
  journal = {Journal on Selected Areas of Information Theory (JSAIT) Special Issue, 2021},
  year    = {2021},
  url     = {https://arxiv.org/abs/1803.01113}
}

A Novel Framework for the Analysis and Design of Heterogeneous Federated Learning

Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor

IEEE Transactions on Signal Processing, Sept 2021

BibTeX

@article{wang2021a,
  author  = {Jianyu Wang and Qinghua Liu and Hao Liang and Gauri Joshi and H. Vincent Poor},
  title   = {{A Novel Framework for the Analysis and Design of Heterogeneous Federated Learning}},
  journal = {IEEE Transactions on Signal Processing},
  year    = {2021},
  month   = {9},
  url     = {https://ieeexplore.ieee.org/document/9521822}
}

A Field Guide to Federated Optimization

53 authors including primary editors Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi and H. Brendan McMahan

Technical Report, July 2021

BibTeX

@misc{joshi2021a,
  title        = {{A Field Guide to Federated Optimization}},
  howpublished = {Technical Report},
  year         = {2021},
  month        = {7},
  url          = {https://arxiv.org/abs/2107.06917}
}

Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning

Divyansh Jhunjhunwala, Advait Gadhikar, Gauri Joshi, and Yonina C. Eldar

International Conference on Acoustics, Speech, and Signal Processing (ICASSP), June 2021

ICASSP 2021 arXiv

BibTeX

@inproceedings{jhunjhunwala2021adaptive,
  author    = {Divyansh Jhunjhunwala and Advait Gadhikar and Gauri Joshi and Yonina C. Eldar},
  title     = {{Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning}},
  booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
  year      = {2021},
  month     = {6},
  url       = {https://arxiv.org/abs/2102.04487}
}

Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms

Jianyu Wang and Gauri Joshi

Journal of Machine Learning Research (JMLR), 2021

JMLR 2021 arXiv

BibTeX

@article{wang2021cooperative,
  author  = {Jianyu Wang and Gauri Joshi},
  title   = {{Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms}},
  journal = {Journal of Machine Learning Research (JMLR), 2021},
  year    = {2021},
  url     = {https://arxiv.org/abs/1808.07576}
}

Advances and Open Problems in Federated Learning

58 authors, including Peter Kairouz, Brendan McMahan, Jianyu Wang and Gauri Joshi

Foundations and Trends in Machine Learning, 2021

BibTeX

@misc{joshi2021advances,
  title        = {{Advances and Open Problems in Federated Learning}},
  howpublished = {Foundations and Trends in Machine Learning, 2021},
  year         = {2021},
  url          = {https://arxiv.org/abs/1912.04977}
}

Machine Learning on Volatile Instances

Xiaoxi Zhang, Jianyu Wang, Li-Feng Lee, Tom Yang, Akansha Kalra, Gauri Joshi, and Carlee Joe-Wong

IEEE/ACM Transactions on Networking, Sept 2021

BibTeX

@article{zhang2021machine,
  author  = {Xiaoxi Zhang and Jianyu Wang and Li-Feng Lee and Tom Yang and Akansha Kalra and Gauri Joshi and Carlee Joe-Wong},
  title   = {{Machine Learning on Volatile Instances}},
  journal = {IEEE/ACM Transactions on Networking},
  year    = {2021},
  month   = {9},
  url     = {https://arxiv.org/abs/2003.05649}
}

Synergy via Redundancy: Adaptive Replication Strategies and Fundamental Limits

Gauri Joshi and Dhruva Kaushal

IEEE ACM/Transactions on Networking, 2021

BibTeX

@article{joshi2021synergy,
  author  = {Gauri Joshi and Dhruva Kaushal},
  title   = {{Synergy via Redundancy: Adaptive Replication Strategies and Fundamental Limits}},
  journal = {IEEE ACM/Transactions on Networking, 2021},
  year    = {2021}
}

Job Dispatching Policies for Queueing Systems with Unknown Service Rates

Tuhinangshu Choudhury, Gauri Joshi, Weina Wang, and Sanjay Shakkottai

ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks (MobiHoc), July 2021

BibTeX

@inproceedings{choudhury2021job,
  author    = {Tuhinangshu Choudhury and Gauri Joshi and Weina Wang and Sanjay Shakkottai},
  title     = {{Job Dispatching Policies for Queueing Systems with Unknown Service Rates}},
  booktitle = {ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks (MobiHoc)},
  year      = {2021},
  month     = {7},
  url       = {https://arxiv.org/abs/2106.04707}
}

Best-arm Identification in Correlated Multi-armed Bandits

Samarth Gupta, Gauri Joshi, and Osman Yagan

Journal on Selected Areas of Information Theory (JSAIT) Special Issue on Sequential, Active, and Reinforcement Learning, 2021

JSAIT 2021 PDF

BibTeX

@article{gupta2021bestarm,
  author  = {Samarth Gupta and Gauri Joshi and Osman Yagan},
  title   = {{Best-arm Identification in Correlated Multi-armed Bandits}},
  journal = {Journal on Selected Areas of Information Theory (JSAIT) Special Issue on Sequential, Active, and Reinforcement Learning, 2021},
  year    = {2021},
  url     = {https://ieeexplore.ieee.org/document/9437336}
}

Multi-armed Bandits with Correlated Arms

Samarth Gupta, Shreyas Chaudhari, Gauri Joshi, and Osman Yagan

IEEE Transactions on Information Theory, May 2021

shorter version appeared in the ICML Workshop on Theoretical Foundations of RL, July 2020

BibTeX

@article{gupta2021multiarmed,
  author  = {Samarth Gupta and Shreyas Chaudhari and Gauri Joshi and Osman Yagan},
  title   = {{Multi-armed Bandits with Correlated Arms}},
  journal = {IEEE Transactions on Information Theory},
  year    = {2021},
  month   = {5},
  url     = {https://arxiv.org/abs/1911.03959}
}

A Unified Approach to Translate Classic Bandit Algorithms to the Structured Bandit Setting

Samarth Gupta, Shreyas Chaudhari, Subhojyoti Mukherjee, Gauri Joshi, and Osman Yagan

Journal on Selected Areas of Information Theory (JSAIT) Special Issue on Estimation and Inference, 2021

shorter version in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), June 2021

JSAIT 2021 arXiv

BibTeX

@article{gupta2021a,
  author  = {Samarth Gupta and Shreyas Chaudhari and Subhojyoti Mukherjee and Gauri Joshi and Osman Yagan},
  title   = {{A Unified Approach to Translate Classic Bandit Algorithms to the Structured Bandit Setting}},
  journal = {Journal on Selected Areas of Information Theory (JSAIT) Special Issue on Estimation and Inference, 2021},
  year    = {2021},
  url     = {https://arxiv.org/pdf/1810.08164.pdf}
}

Deep Kernels with Probabilistic Embeddings for Small-Data Learning

Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, and T. Yong-Jin Han

Conference on Uncertainty in Artificial Intelligence (UAI), July 2021

Selected for an Oral Presentation

shorter version in the ICLR From Shallow to Deep: Overcoming Limited and Adverse Data (S2D-OLAD) Workshop, May 2021

UAI 2021 arXiv

BibTeX

@inproceedings{mallick2021deep,
  author    = {Ankur Mallick and Chaitanya Dwivedi and Bhavya Kailkhura and Gauri Joshi and T. Yong-Jin Han},
  title     = {{Deep Kernels with Probabilistic Embeddings for Small-Data Learning}},
  booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI)},
  year      = {2021},
  month     = {7},
  url       = {https://arxiv.org/abs/1910.05858}
}

2020

Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization

Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor

Neural Information Processing Systems (NeurIPS), Dec 2020

Best Poster Award at the NSF TRIPODS workshop on communication-efficient distributed optimization

NeurIPS 2020 arXiv

BibTeX

@inproceedings{wang2020tackling,
  author    = {Jianyu Wang and Qinghua Liu and Hao Liang and Gauri Joshi and H. Vincent Poor},
  title     = {{Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization}},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  year      = {2020},
  month     = {12},
  url       = {https://arxiv.org/abs/2007.07481}
}

Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning

Yae Jee Cho, Samarth Gupta, Gauri Joshi, and Osman Yagan

Asilomar Conference, Nov 2020

BibTeX

@inproceedings{cho2020banditbased,
  author    = {Yae Jee Cho and Samarth Gupta and Gauri Joshi and Osman Yagan},
  title     = {{Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning}},
  booktitle = {Asilomar Conference},
  year      = {2020},
  month     = {11},
  url       = {http://arxiv.org/abs/2012.08009}
}

Exploring the Error-Runtime Trade-off in Decentralized Optimization

Jianyu Wang, Anit Sahu, Gauri Joshi, and Soummya Kar

Asilomar Conference, Nov 2020

BibTeX

@inproceedings{wang2020exploring,
  author    = {Jianyu Wang and Anit Sahu and Gauri Joshi and Soummya Kar},
  title     = {{Exploring the Error-Runtime Trade-off in Decentralized Optimization}},
  booktitle = {Asilomar Conference},
  year      = {2020},
  month     = {11}
}

Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-vector Multiplication

Ankur Mallick, Malhar Chaudhari, Ganesh Palanikumar, Utsav Sheth, and Gauri Joshi

ACM SIGMETRICS, June 2020

Best Paper Award

BibTeX

@inproceedings{mallick2020rateless,
  author    = {Ankur Mallick and Malhar Chaudhari and Ganesh Palanikumar and Utsav Sheth and Gauri Joshi},
  title     = {{Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-vector Multiplication}},
  booktitle = {ACM SIGMETRICS},
  year      = {2020},
  month     = {6},
  url       = {https://arxiv.org/abs/1804.10331}
}

Correlated Multi-armed Bandits with a Latent Random Source

Samarth Gupta, Gauri Joshi, and Osman Yagan

International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020

ICASSP 2020 arXiv

BibTeX

@inproceedings{gupta2020correlated,
  author    = {Samarth Gupta and Gauri Joshi and Osman Yagan},
  title     = {{Correlated Multi-armed Bandits with a Latent Random Source}},
  booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
  year      = {2020},
  month     = {5},
  url       = {https://arxiv.org/abs/1808.05904}
}

Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD

Jianyu Wang, Hao Liang, and Gauri Joshi

International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020

ICASSP 2020 PDF

BibTeX

@inproceedings{wang2020overlap,
  author    = {Jianyu Wang and Hao Liang and Gauri Joshi},
  title     = {{Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD}},
  booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
  year      = {2020},
  month     = {5},
  url       = {https://ieeexplore.ieee.org/document/9053834}
}

Machine Learning on Volatile Instances

Xiaoxi Zhang, Jianyu Wang, Gauri Joshi, and Carlee Joe-Wong

IEEE Intl. Conf. on Computer Communications (INFOCOM), April 2020

INFOCOM 2020 PDF

BibTeX

@inproceedings{zhang2020machine,
  author    = {Xiaoxi Zhang and Jianyu Wang and Gauri Joshi and Carlee Joe-Wong},
  title     = {{Machine Learning on Volatile Instances}},
  booktitle = {IEEE Intl. Conf. on Computer Communications (INFOCOM)},
  year      = {2020},
  month     = {4},
  url       = {https://dl.acm.org/doi/10.1109/INFOCOM41043.2020.9155448}
}

Accelerating Deep Learning by Focusing on the Biggest Losers

Angela H. Jiang, Daniel L.-K. Wong, Giulio Zhou, David G. Andersen, Jeffrey Dean, Gregory R. Ganger, Gauri Joshi, Michael Kaminksy, Michael Kozuch, Zachary C. Lipton, and Padmanabhan Pillai

preprint

BibTeX

@misc{jiang2020accelerating,
  author = {Angela H. Jiang and Daniel L.-K. Wong and Giulio Zhou and David G. Andersen and Jeffrey Dean and Gregory R. Ganger and Gauri Joshi and Michael Kaminksy and Michael Kozuch and Zachary C. Lipton and Padmanabhan Pillai},
  title  = {{Accelerating Deep Learning by Focusing on the Biggest Losers}},
  year   = {2020},
  url    = {https://arxiv.org/abs/1910.00762}
}

Probabilistic Neighbourhood Component Analysis: Sample-Efficient Uncertainty Estimation in Deep Learning

Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, and T. Yong-Jin Han

ICML Workshop on Uncertainty and Robustness in Deep Learning, July 2020

BibTeX

@inproceedings{mallick2020probabilistic,
  author    = {Ankur Mallick and Chaitanya Dwivedi and Bhavya Kailkhura and Gauri Joshi and T. Yong-Jin Han},
  title     = {{Probabilistic Neighbourhood Component Analysis: Sample-Efficient Uncertainty Estimation in Deep Learning}},
  booktitle = {ICML Workshop on Uncertainty and Robustness in Deep Learning},
  year      = {2020},
  month     = {July}
}

2019

Rateless Codes for Distributed Computations with Sparse Compressed Matrices

Ankur Mallick and Gauri Joshi

international Symposium on Information Theory (ISIT), July 2019

ISIT 2019 PDF

BibTeX

@inproceedings{mallick2019rateless,
  author    = {Ankur Mallick and Gauri Joshi},
  title     = {{Rateless Codes for Distributed Computations with Sparse Compressed Matrices}},
  booktitle = {international Symposium on Information Theory (ISIT)},
  year      = {2019},
  month     = {7},
  url       = {https://ieeexplore.ieee.org/document/8849306}
}

Fast and Efficient Distributed Matrix-Vector Multiplication Using Rateless Fountain Codes

Ankur Mallick, Malhar Chaudhari, and Gauri Joshi

International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2019

ICASSP 2019 PDF

BibTeX

@inproceedings{mallick2019fast,
  author    = {Ankur Mallick and Malhar Chaudhari and Gauri Joshi},
  title     = {{Fast and Efficient Distributed Matrix-Vector Multiplication Using Rateless Fountain Codes}},
  booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2019},
  year      = {2019},
  url       = {https://ieeexplore.ieee.org/document/8682347}
}

Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD

Jianyu Wang and Gauri Joshi

SysML Conference, March 2019

BibTeX

@inproceedings{wang2019adaptive,
  author    = {Jianyu Wang and Gauri Joshi},
  title     = {{Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD}},
  booktitle = {SysML Conference},
  year      = {2019},
  month     = {3},
  url       = {https://arxiv.org/abs/1810.08313}
}

Efficient Straggler Replication in Large-scale Parallel Computing

Da Wang, Gauri Joshi, and Gregory Wornell

ACM Trans. on Modeling and Perf. Eval. of Comp. Systems, 2019

BibTeX

@misc{wang2019efficient,
  author       = {Da Wang and Gauri Joshi and Gregory Wornell},
  title        = {{Efficient Straggler Replication in Large-scale Parallel Computing}},
  howpublished = {ACM Trans. on Modeling and Perf. Eval. of Comp. Systems, 2019},
  year         = {2019},
  url          = {http://arxiv.org/abs/1503.03128}
}

MATCHA: Speeding up Decentralized SGD via Matching Decomposition Sampling

Jianyu Wang, Anit Sahu, Gauri Joshi, and Soummya Kar

Neural Information Processing Systems (NeurIPS) Federated Learning workshop, Dec 2019

Distinguished Student Paper Award

NeurIPS 2019

BibTeX

@inproceedings{wang2019matcha,
  author    = {Jianyu Wang and Anit Sahu and Gauri Joshi and Soummya Kar},
  title     = {{MATCHA: Speeding up Decentralized SGD via Matching Decomposition Sampling}},
  booktitle = {Neural Information Processing Systems (NeurIPS) Federated Learning workshop},
  year      = {2019},
  month     = {Dec},
  url       = {https://arxiv.org/abs/1905.09435}
}

2018

Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD

Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, and Priya Nagpurkar

International Conference on Artificial Intelligence and Statistics (AISTATS), Apr 2018

AISTATS 2018 arXiv

BibTeX

@inproceedings{dutta2018slow,
  author    = {Sanghamitra Dutta and Gauri Joshi and Soumyadip Ghosh and Parijat Dube and Priya Nagpurkar},
  title     = {{Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD}},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  year      = {2018},
  month     = {4},
  url       = {https://arxiv.org/abs/1803.01113}
}

Active Distribution Learning from Indirect Samples

Samarth Gupta, Gauri Joshi, and Osman Yagan

Allerton Conference on Communication, Control and Computing, Oct 2018

BibTeX

@inproceedings{gupta2018active,
  author    = {Samarth Gupta and Gauri Joshi and Osman Yagan},
  title     = {{Active Distribution Learning from Indirect Samples}},
  booktitle = {Allerton Conference on Communication, Control and Computing},
  year      = {2018},
  month     = {10},
  url       = {https://arxiv.org/abs/1808.05334}
}

Service Capacity Region of Content Access from Erasure Coded Storage

Sarah E. Anderson, Ann Johnston, Gauri Joshi, Gretchen L. Matthews, Carolyn Mayer, and Emina Soljanin

IEEE Information Theory Workshop, Nov 2018

BibTeX

@inproceedings{anderson2018service,
  author    = {Sarah E. Anderson and Ann Johnston and Gauri Joshi and Gretchen L. Matthews and Carolyn Mayer and Emina Soljanin},
  title     = {{Service Capacity Region of Content Access from Erasure Coded Storage}},
  booktitle = {IEEE Information Theory Workshop},
  year      = {2018},
  month     = {11}
}

2017

Synergy via Redundancy: Boosting Service Capacity via Adaptive Task Replication

Gauri Joshi

ACM/IFIP Performance, Nov 2017

BibTeX

@misc{joshi2017synergy,
  author       = {Gauri Joshi},
  title        = {{Synergy via Redundancy: Boosting Service Capacity via Adaptive Task Replication}},
  howpublished = {ACM/IFIP Performance},
  year         = {2017},
  month        = {11}
}

On the Service Capacity Region of Accessing Erasure Coded Content

Mehmet Aktas, Sarah E. Anderson, Ann Johnston, Gauri Joshi, Swanand Kadhe, Gretchen L. Matthews, Carolyn Mayer, and Emina Soljanin

Allerton Conference on Communication, Control, and Computing, Oct 2017

BibTeX

@inproceedings{aktas2017on,
  author    = {Mehmet Aktas and Sarah E. Anderson and Ann Johnston and Gauri Joshi and Swanand Kadhe and Gretchen L. Matthews and Carolyn Mayer and Emina Soljanin},
  title     = {{On the Service Capacity Region of Accessing Erasure Coded Content}},
  booktitle = {Allerton Conference on Communication, Control, and Computing},
  year      = {2017},
  month     = {10}
}

Efficient Redundancy Techniques for Latency Reduction in Cloud Systems

Gauri Joshi, Emina Soljanin, and Gregory Wornell

ACM Trans. on Modeling and Perf. Eval. of Comp. Systems, vol. 2, no. 12, May 2017

BibTeX

@misc{joshi2017efficient,
  author       = {Gauri Joshi and Emina Soljanin and Gregory Wornell},
  title        = {{Efficient Redundancy Techniques for Latency Reduction in Cloud Systems}},
  howpublished = {ACM Trans. on Modeling and Perf. Eval. of Comp. Systems, vol. 2, no. 12},
  year         = {2017},
  month        = {5}
}

Boosting Service Capacity via Adaptive Task Replication

Gauri Joshi

ACM Sigmetrics MAMA workshop, Jun 2017

BibTeX

@inproceedings{joshi2017boosting,
  author    = {Gauri Joshi},
  title     = {{Boosting Service Capacity via Adaptive Task Replication}},
  booktitle = {ACM Sigmetrics MAMA workshop},
  year      = {2017},
  month     = {6}
}

Spot Market Service for Machine Learning Jobs

Stefania Costache, Gauri Joshi, Parijat Dube, Asser Tantawi, and Alaa Youssef

US Patent, 2017

BibTeX

@misc{patent2017spot,
  author       = {Stefania Costache and Gauri Joshi and Parijat Dube and Asser Tantawi and Alaa Youssef},
  title        = {{Spot Market Service for Machine Learning Jobs}},
  howpublished = {US Patent, 2017},
  year         = {2017}
}

2015-2016

Efficient Redundancy Techniques to Reduce Delay in Cloud Systems

Doctoral Thesis, MIT, June 2016

BibTeX

@phdthesis{joshi2016efficient,
  author = {Gauri Joshi},
  title  = {{Efficient Redundancy Techniques to Reduce Delay in Cloud Systems}},
  school = {Doctoral Thesis, MIT},
  year   = {2016},
  month  = {6}
}

Efficient Replication of Queued Tasks to Reduce Latency in Cloud Systems

Gauri Joshi, Emina Soljanin, and Gregory Wornell

Allerton Conference on Communication, Control and Computing, Oct 2015

BibTeX

@inproceedings{joshi2015efficient,
  author    = {Gauri Joshi and Emina Soljanin and Gregory Wornell},
  title     = {{Efficient Replication of Queued Tasks to Reduce Latency in Cloud Systems}},
  booktitle = {Allerton Conference on Communication, Control and Computing},
  year      = {2015},
  month     = {10}
}

Queues with Redundancy: Latency-Cost Analysis

Gauri Joshi, Emina Soljanin, and Gregory Wornell

Mathematical Modeling and Analysis (MAMA) Workshop, Jun 2015

BibTeX

@inproceedings{joshi2015queues,
  author    = {Gauri Joshi and Emina Soljanin and Gregory Wornell},
  title     = {{Queues with Redundancy: Latency-Cost Analysis}},
  booktitle = {Mathematical Modeling and Analysis (MAMA) Workshop},
  year      = {2015},
  month     = {6}
}

Using Straggler Replication to Reduce Latency in Large-scale Parallel Computing

Da Wang, Gauri Joshi, and Gregory Wornell

Distributed Cloud Computing (DCC) Workshop, Jun 2015

BibTeX

@inproceedings{wang2015using,
  author    = {Da Wang and Gauri Joshi and Gregory Wornell},
  title     = {{Using Straggler Replication to Reduce Latency in Large-scale Parallel Computing}},
  booktitle = {Distributed Cloud Computing (DCC) Workshop},
  year      = {2015},
  month     = {6}
}

Playback Delay in On-Demand Streaming Communication with Feedback

Kaveh Mahadaviani, Ashish Khisti, Gauri Joshi, and Gregory Wornell

International Symposium on Information Theory (ISIT), July 2015

BibTeX

@inproceedings{mahadaviani2015playback,
  author    = {Kaveh Mahadaviani and Ashish Khisti and Gauri Joshi and Gregory Wornell},
  title     = {{Playback Delay in On-Demand Streaming Communication with Feedback}},
  booktitle = {International Symposium on Information Theory (ISIT)},
  year      = {2015},
  month     = {7}
}

Throughput-Smoothness Trade-offs in Streaming Communication

Gauri Joshi, Yuval Kochman, and Gregory Wornell

preprint, Nov 2015

BibTeX

@article{joshi2015throughput,
  author = {Gauri Joshi and Yuval Kochman and Gregory Wornell},
  title  = {{Throughput-Smoothness Trade-offs in Streaming Communication}},
  year   = {2015},
  month  = {Nov}
}

2014

Efficient Job Replication for Fast Response Times in Parallel Computation

Da Wang, Gauri Joshi, and Gregory Wornell

ACM Sigmetrics short paper Jun 2014

BibTeX

@inproceedings{wang2014efficient,
  author    = {Da Wang and Gauri Joshi and Gregory Wornell},
  title     = {{Efficient Job Replication for Fast Response Times in Parallel Computation}},
  booktitle = {ACM Sigmetrics short paper},
  year      = {2014},
  month     = {6}
}

Throughput-Smoothness Trade-offs in Multicasting an Ordered Packet Stream

Gauri Joshi, Yuval Kochman, and Gregory Wornell

International Symposium on Network Coding, Jun 2014

BibTeX

@inproceedings{joshi2014throughputsmoothness,
  author    = {Gauri Joshi and Yuval Kochman and Gregory Wornell},
  title     = {{Throughput-Smoothness Trade-offs in Multicasting an Ordered Packet Stream}},
  booktitle = {International Symposium on Network Coding},
  year      = {2014},
  month     = {6}
}

On the Delay-Storage Trade-off in Content Download from Distributed Storage

Gauri Joshi, Yanpei Liu, and Emina Soljanin

IEEE Journal on Selected Areas of Communications, vol. 32, no. 5, May 2014

BibTeX

@article{joshi2014on,
  author  = {Gauri Joshi and Yanpei Liu and Emina Soljanin},
  title   = {{On the Delay-Storage Trade-off in Content Download from Distributed Storage}},
  journal = {IEEE Journal on Selected Areas of Communications, vol. 32, no. 5},
  year    = {2014},
  month   = {5}
}

Effect of Block-wise Feedback on the Throughput-Delay Trade-off in Streaming

Gauri Joshi, Yuval Kochman, and Gregory Wornell

INFOCOM workshop on Contemporary Video, Apr 2014

BibTeX

@inproceedings{joshi2014effect,
  author    = {Gauri Joshi and Yuval Kochman and Gregory Wornell},
  title     = {{Effect of Block-wise Feedback on the Throughput-Delay Trade-off in Streaming}},
  booktitle = {INFOCOM workshop on Contemporary Video},
  year      = {2014},
  month     = {4}
}

2012-2013

Round-robin Overlapping Generations Coding for Fast Content Download

Gauri Joshi and Emina Soljanin

International Symposium on Information Theory (ISIT), Jul 2013

BibTeX

@inproceedings{joshi2013roundrobin,
  author    = {Gauri Joshi and Emina Soljanin},
  title     = {{Round-robin Overlapping Generations Coding for Fast Content Download}},
  booktitle = {International Symposium on Information Theory (ISIT)},
  year      = {2013},
  month     = {7}
}

Coding for Fast Content Download

Gauri Joshi, Yanpei Liu, and Emina Soljanin

Allerton Conference on Communication, Control and Computing, Oct 2012

BibTeX

@inproceedings{joshi2012coding,
  author    = {Gauri Joshi and Yanpei Liu and Emina Soljanin},
  title     = {{Coding for Fast Content Download}},
  booktitle = {Allerton Conference on Communication, Control and Computing},
  year      = {2012},
  month     = {10}
}

On Playback Delay in Streaming Communication

Gauri Joshi, Yuval Kochman, and Gregory Wornell

International Symposium on Information Theory (ISIT), Jul 2012

BibTeX

@inproceedings{joshi2012on,
  author    = {Gauri Joshi and Yuval Kochman and Gregory Wornell},
  title     = {{On Playback Delay in Streaming Communication}},
  booktitle = {International Symposium on Information Theory (ISIT)},
  year      = {2012},
  month     = {7}
}

On Playback Delay in Streaming Communication

Masters Thesis, MIT, May 2012

William Martin Memorial Best Thesis Award

BibTeX

@mastersthesis{joshi2012onb,
  author = {Gauri Joshi},
  title  = {{On Playback Delay in Streaming Communication}},
  school = {Masters Thesis, MIT},
  year   = {2012},
  month  = {5}
}

2010-2011

Fountain Codes

Gauri Joshi, Joong Bum Rhim, John Sun, and Da Wang

Project Report 6.451, Dec 2010

BibTeX

@misc{joshi2010fountain,
  author       = {Gauri Joshi and Joong Bum Rhim and John Sun and Da Wang},
  title        = {{Fountain Codes}},
  howpublished = {Project Report 6.451},
  year         = {2010},
  month        = {12}
}

Downlink Erlang Capacity of Cellular OFDMA

Gauri Joshi, Harshad Maral, and Abhay Karandikar

National Conference on Communications, Jan 2011

BibTeX

@inproceedings{joshi2011downlink,
  author    = {Gauri Joshi and Harshad Maral and Abhay Karandikar},
  title     = {{Downlink Erlang Capacity of Cellular OFDMA}},
  booktitle = {National Conference on Communications},
  year      = {2011},
  month     = {1}
}

Optimal Relay Placement for Cellular Coverage Extension

Gauri Joshi and Abhay Karandikar

National Conference on Communications, Jan 2011

BibTeX

@inproceedings{joshi2011optimal,
  author    = {Gauri Joshi and Abhay Karandikar},
  title     = {{Optimal Relay Placement for Cellular Coverage Extension}},
  booktitle = {National Conference on Communications},
  year      = {2011},
  month     = {1}
}

Performance Analysis of Active Handoff in CDMA2000 Femtocells

Gauri Joshi, Mehmet Yavuz, and Chirag Patel

National Conference on Communications, Jan 2010

BibTeX

@inproceedings{joshi2010performance,
  author    = {Gauri Joshi and Mehmet Yavuz and Chirag Patel},
  title     = {{Performance Analysis of Active Handoff in CDMA2000 Femtocells}},
  booktitle = {National Conference on Communications},
  year      = {2010},
  month     = {1}
}

On Relay-assisted Cellular Networks

Masters Thesis, IIT Bombay, June 2010