Gauri Joshi
Professor
Studies distributed and federated machine learning optimization and efficient large-scale learning.
Gauri Joshi's lab at CMU works on learning theory & optimization, trustworthy & safe ML and efficient training & inference. 32 papers since 2023, 20 at top venues, mostly AISTATS and ICLR. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 63%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 5 papers2026 · T1 3 · collecting
- 8 papers2025 · T1 6
- 10 papers2024 · T1 6
- 9 papers2023 · T1 5
T1 venuesOtherVenues: AISTATS, ICLR, ICML, NeurIPS, EMNLP
Research focuspaper titles that use each topic's words
- Learning theory & optimization16 papers
- Efficient training & inference8 papers
- Trustworthy & safe ML0 papers
- studies distributed0 papers
- federated machine0 papers
Paper list
From DBLP.
20265 papers
- Trans. Mach. Learn. Res.Adaptive Federated Learning via Dynamical System Model (opens in a new tab)
- ICLR (T1)Internal Planning in Language Models: Characterizing Horizon and Branch Awareness (opens in a new tab)
- Trans. Mach. Learn. Res.Natural Policy Gradient for Average Reward Non-Stationary Reinforcement Learning (opens in a new tab)
- ICLR (T1)Navigating the Accuracy-Size Trade-Off with Flexible Model Merging (opens in a new tab)
- SIGCOMM (T1)Toward WAN-Aware LLM Training Across Heterogeneous, Geo-Distributed Sites (opens in a new tab)
20258 papers
- ICLR (T1)Debiasing Federated Learning with Correlated Client Participation (opens in a new tab)
- ICML (T1)FedECADO: A Dynamical System Model of Federated Learning (opens in a new tab)
- AISTATS (T1)Federated Communication-Efficient Multi-Objective Optimization (opens in a new tab)
- AISTATS (T1)High-probability Convergence Bounds for Online Nonlinear Stochastic Gradient Descent under Heavy-tailed Noise (opens in a new tab)
- Trans. Mach. Learn. Res.Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning (opens in a new tab)
- Perform. EvaluationJob assignment in machine learning inference systems with accuracy constraints (opens in a new tab)
- NeurIPS (T1)Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning (opens in a new tab)
- JMLR (T1)The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond (opens in a new tab)
202410 papers
- AISTATS (T1)Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems (opens in a new tab)
- ISITErasure Coded Neural Network Inference via Fisher Averaging (opens in a new tab)
- UAI (T1)FedAST: Federated Asynchronous Simultaneous Training (opens in a new tab)
- ICML (T1)Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices (opens in a new tab)
- AISTATS (T1)FedFisher: Leveraging Fisher Information for One-Shot Federated Learning (opens in a new tab)
- EMNLP (T1)Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models (opens in a new tab)
- Trans. Mach. Learn. Res.Maximizing Global Model Appeal in Federated Learning (opens in a new tab)
- ICASSP (T1)On Improved Distributed Random Reshuffling over Networks (opens in a new tab)
- Trans. Mach. Learn. Res.On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data (opens in a new tab)
- Trans. Mach. Learn. Res.Optimized Tradeoffs for Private Prediction with Majority Ensembling (opens in a new tab)
20239 papers
- IEEE J. Sel. Top. Signal Process.Communication-Efficient and Model-Heterogeneous Personalized Federated Learning via Clustered Knowledge Transfer (opens in a new tab)
- SIGMETRICS Perform. Evaluation Rev.Correlated Combinatorial Bandits for Online Resource Allocation (opens in a new tab)
- NeurIPS (T1)Correlation Aware Sparsified Mean Estimation Using Random Projection (opens in a new tab)
- AISTATS (T1)Federated Learning under Distributed Concept Drift (opens in a new tab)
- Trans. Mach. Learn. Res.Federated Minimax Optimization with Client Heterogeneity (opens in a new tab)
- ICLR (T1)FedExP: Speeding Up Federated Averaging via Extrapolation (opens in a new tab)
- ICCV (T1)Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels (opens in a new tab)
- ICML (T1)On the Convergence of Federated Averaging with Cyclic Client Participation (opens in a new tab)
- SIGMETRICS Perform. Evaluation Rev.Tackling Heterogeneous Traffic in Multi-access Systems via Erasure Coded Servers (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
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Data
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