Salman Avestimehr
29 papers · 2017–2026 · 9 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (8) π§ Keyword Pioneer π Interdisciplinary Bridge π Conference Polyglot (9) π Cross-Pollinator (14)
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Conference Polyglot
(9)
π
Academic Marathon
(8)
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(52)
π₯
Mega-Team
(24)
π
Grand Slam
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Keyword Champion
(2)
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Trend Setter
π₯
Unstoppable
(6)
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Century Club
(28)
ποΈ
Keyword Collector
(130)
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Prolific Year
(5)
Conferences
NIPS (9)
ACL (5)
EMNLP (4)
NAACL (4)
AAAI (2)
CVPR (2)
ECCV (1)
ICLR (1)
ICML (1)
Top co-authors
Research topics
Keywords
federated learning
(9)
large language model
(6)
uncertainty estimation
(4)
distributed training
(3)
uncertainty quantification
(3)
model compression
(3)
generative model
(3)
bayesian hierarchical model
(2)
non-iid datum
(2)
decentralized training
(2)
text classification
(2)
edge computing
(2)
personalized model
(2)
token probability
(2)
parameter-efficient fine-tuning
(2)
question answering
(2)
knowledge distillation
(2)
distributed learning
(2)
privacy-preserving machine learning
(2)
convolutional neural network
(2)
Papers
GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation
AAAI 2026
Creating a Lens of Chinese Culture: A Multimodal Dataset for Chinese Pun Rebus Art Understanding
ACL 2025
Reconsidering LLM Uncertainty Estimation Methods in the Wild
ACL 2025
MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines
EMNLP 2025
TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs
EMNLP 2025
Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
NAACL 2025
All Rivers Run to the Sea: Private Learning with Asymmetric Flows
CVPR 2024
CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning
ECCV 2024
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency
EMNLP 2024
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference
EMNLP 2024
Ethos: Rectifying Language Models in Orthogonal Parameter Space
NAACL 2024
MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs
ACL 2024
Revisiting OPRO: The Limitations of Small-Scale LLMs as Optimizers
ACL 2024
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
ICLR 2024
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
CVPR 2023
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
NIPS 2023
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
NIPS 2022
Self-Aware Personalized Federated Learning
NIPS 2022
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data
AAAI 2022
ActPerFL: Active Personalized Federated Learning
ACL 2022
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks
NAACL 2022
Federated Learning with Noisy User Feedback
NAACL 2022
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models
ICML 2021
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
NIPS 2020
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
NIPS 2020
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
NIPS 2020
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
NIPS 2018
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
NIPS 2018
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication
NIPS 2017