Gauri Joshi
27 papers · 2018–2026 · 9 venues · across top CS/AI venues
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Research topics
Machine Learning
(24)
Optimization & Theory
(21)
Artificial Intelligence
(20)
Learning Paradigms Β· Artificial Intelligence
(19)
Learning Paradigms Β· Machine Learning
(10)
Learning Types Β· Machine Learning
(5)
Deep Learning
(4)
Reinforcement Learning
(4)
Application Areas
(4)
Methods
(4)
Mathematics & Optimization
(3)
Core AI
(3)
Core Methods
(3)
Learning Types Β· Deep Learning
(3)
Optimization
(3)
Keywords
federated learning
(19)
communication efficiency
(6)
distributed learning
(5)
convergence analysis
(5)
linear speedup
(4)
stochastic gradient descent
(4)
importance averaging
(3)
federated averaging
(3)
non-convex optimization
(3)
tabular markov decision process
(2)
model compression
(2)
federated q-learning
(2)
client participation
(2)
reinforcement learning
(2)
knowledge distillation
(2)
heterogeneous data
(2)
sample complexity
(2)
semi-supervised learning
(2)
local updates
(2)
ensemble learning
(1)
Papers
Improving the Convergence of Private Shuffled Gradient Methods with Public Data
UAI 2026
Debiasing Federated Learning with Correlated Client Participation
ICLR 2025
FedECADO: A Dynamical System Model of Federated Learning
ICML 2025
Federated Communication-Efficient Multi-Objective Optimization
AISTATS 2025
High-probability Convergence Bounds for Online Nonlinear Stochastic Gradient Descent under Heavy-tailed Noise
AISTATS 2025
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
JMLR 2025
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems
AISTATS 2024
FedAST: Federated Asynchronous Simultaneous Training
UAI 2024
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
ICML 2024
FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
AISTATS 2024
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
EMNLP 2024
Correlation Aware Sparsified Mean Estimation Using Random Projection
NeurIPS 2023
Federated Learning under Distributed Concept Drift
AISTATS 2023
FedExP: Speeding Up Federated Averaging via Extrapolation
ICLR 2023
Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels
ICCV 2023
On the Convergence of Federated Averaging with Cyclic Client Participation
ICML 2023
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
ICML 2023
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
ICML 2022
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling
ICML 2022
Fedvarp: Tackling the variance due to partial client participation in federated learning
UAI 2022
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
IJCAI 2022
Towards Understanding Biased Client Selection in Federated Learning
AISTATS 2022
Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms
JMLR 2021
Deep kernels with probabilistic embeddings for small-data learning
UAI 2021
Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation
NeurIPS 2021
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
NeurIPS 2020
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
AISTATS 2018