← Authors

Tao LIN

52 papers · 2017–2025 · 9 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🌈 Renaissance Researcher (9) 🌍 Conference Polyglot (9) πŸƒ Academic Marathon (8) 🐝 Cross-Pollinator (12) πŸ—ΊοΈ Taxonomy Completionist (69) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🧬 Topic Evolution πŸ† Grand Slam πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (182) ⚑ Prolific Year (10) πŸ’Ž Century Club (52) πŸ”₯ Unstoppable (9) ❓ The Questioner (2)

Conferences

ICLR (14) NIPS (14) ICML (10) AAAI (3) ICCV (3) CVPR (2) ECCV (2) EMNLP (2) IJCAI (2)

Top co-authors

Martin Jaggi (9) Xiaoying Tang (6) Yongxin Guo (6) Yiling Chen (5) Lin Yang (3) Sebastian U Stich (3) Xinyi Shang (3) peng sun (3) Yuxuan Sun (3) Kai Zhang (3)

Keywords

federated learning (9) data heterogeneity (4) neural network optimization (3) distribution shift (3) mechanism design (3) decentralized learning (3) knowledge distillation (3) model training (2) convergence rate (2) client sampling (2) neural network training (2) model compression (2) long short-term memory (2) bayesian inference (2) ensemble learning (2) distributed learning (2) domain adaptation (2) sample complexity (2) adversarial training (2) regret bound (2)

Papers

Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing ICCV 2025
GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost ICLR 2025
CollabEdit: Towards Non-destructive Collaborative Knowledge Editing ICLR 2025
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration ICLR 2025
Generalized Principal-Agent Problem with a Learning Agent ICLR 2025
DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference ICLR 2025
Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models ICLR 2025
Learn How to Query from Unlabeled Data Streams in Federated Learning AAAI 2025
CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology CVPR 2025
ELICIT: LLM Augmentation Via External In-context Capability ICLR 2025
IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents EMNLP 2025
Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies ICLR 2025
STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding? ICCV 2025
User-Creator Feature Polarization in Recommender Systems with Dual Influence NIPS 2024
Towards Robust Multi-Modal Reasoning via Model Selection ICLR 2024
On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm CVPR 2024
Learning Thresholds with Latent Values and Censored Feedback ICLR 2024
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning ICLR 2024
Out-of-Bounding-Box Triggers: A Stealthy Approach to Cheat Object Detectors ECCV 2024
PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology ECCV 2024
Multi-Sender Persuasion: A Computational Perspective ICML 2024
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering ICML 2024
Efficiency for Free: Ideal Data Are Transportable Representations NIPS 2024
Bias Detection via Signaling NIPS 2024
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging NIPS 2024
Prediction with Incomplete Data under Agnostic Mask Distribution Shift IJCAI 2023
Sample Complexity of Forecast Aggregation NIPS 2023
DELTA: Diverse Client Sampling for Fasting Federated Learning NIPS 2023
From Monopoly to Competition: Optimal Contests Prevail AAAI 2023
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier ICCV 2023
Test-Time Robust Personalization for Federated Learning ICLR 2023
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction ICML 2023
Online Restless Bandits with Unobserved States ICML 2023
Revisiting Weighted Aggregation in Federated Learning with Neural Networks ICML 2023
On Pitfalls of Test-Time Adaptation ICML 2023
Adversarial training for high-stakes reliability NIPS 2022
How Many Representatives Do We Need? The Optimal Size of a Congress Voting on Binary Issues AAAI 2022
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning NIPS 2021
Consensus Control for Decentralized Deep Learning ICML 2021
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data ICML 2021
RelaySum for Decentralized Deep Learning on Heterogeneous Data NIPS 2021
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them NIPS 2020
Dynamic Model Pruning with Feedback ICLR 2020
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models EMNLP 2020
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling NIPS 2020
Ensemble Distillation for Robust Model Fusion in Federated Learning NIPS 2020
Extrapolation for Large-batch Training in Deep Learning ICML 2020
Learning Utilities and Equilibria in Non-Truthful Auctions NIPS 2020
Don't Use Large Mini-batches, Use Local SGD ICLR 2020
Exploring interpretable LSTM neural networks over multi-variable data ICML 2019
Training DNNs with Hybrid Block Floating Point NIPS 2018
Hybrid Neural Networks for Learning the Trend in Time Series IJCAI 2017