3 papers
cs.LG2025
Provably Robust Adaptation for Language-Empowered Foundation Models
Yuni Lai, Xiaoyu Xue, Linghui Shen +5
Language-empowered foundation models (LeFMs), such as CLIP and GraphCLIP, have transformed multimodal learning by aligning visual (or graph) features with textual representations,…
cs.CL2025
ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shaping
Shuang Chen, Yue Guo, Yimeng Ye +7
Recent advances in multimodal large reasoning models (MLRMs) have substantially improved their ability to solve complex textual and visual tasks. However, these models tend to over…
cs.LG2024
QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation
Akash Dhasade, Yaohong Ding, Song Guo +3
Federated Unlearning (FU) aims to delete specific training data from an ML model trained using Federated Learning (FL). We introduce QuickDrop, an efficient and original FU method…