5 citations · 7 across the 13 of their papers we have counts for
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Deciphering Personalization: Towards Fine-Grained Explainability in Natural Language for Personalized Image Generation Models
Haoming Wang, Wei Gao
Image generation models are usually personalized in practical uses in order to better meet the individual users' heterogeneous needs, but most personalized models lack explainabili…
DSADF: Thinking Fast and Slow for Decision Making
Zhihao Dou, Dongfei Cui, Jun Yan +5
Although Reinforcement Learning (RL) agents are effective in well-defined environments, they often struggle to generalize their learned policies to dynamic settings due to their re…
Never Start from Scratch: Expediting On-Device LLM Personalization via Explainable Model Selection
Haoming Wang, Boyuan Yang, Xiangyu Yin +1
Personalization of Large Language Models (LLMs) is important in practical applications to accommodate the individual needs of different mobile users. Due to data privacy concerns,…
FreezeAsGuard: Mitigating Illegal Adaptation of Diffusion Models via Selective Tensor Freezing
Kai Huang, Haoming Wang, Wei Gao
Text-to-image diffusion models can be fine-tuned in custom domains to adapt to specific user preferences, but such adaptability has also been utilized for illegal purposes, such as…
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
Haoming Wang, Wei Gao
Federated Learning (FL) can be affected by data and device heterogeneities, caused by clients' different local data distributions and latencies in uploading model updates (i.e., st…