9 citations · 15 across the 19 of their papers we have counts for
6 papers · 1 filter
SMoLoRA: Exploring and Defying Dual Catastrophic Forgetting in Continual Visual Instruction Tuning
Ziqi Wang, Chang Che, Qi Wang +3
Visual instruction tuning (VIT) enables multimodal large language models (MLLMs) to effectively handle a wide range of vision tasks by framing them as language-based instructions.…
Offline Reinforcement Learning with OOD State Correction and OOD Action Suppression
Yixiu Mao, Qi Wang, Chen Chen +2
In offline reinforcement learning (RL), addressing the out-of-distribution (OOD) action issue has been a focus, but we argue that there exists an OOD state issue that also impairs…
Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta Learning
Yiqin Lv, Qi Wang, Dong Liang +1
Meta learning is a promising paradigm in the era of large models and task distributional robustness has become an indispensable consideration in real-world scenarios. Recent advanc…
Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation
Cheems Wang, Yiqin Lv, Yixiu Mao +3
Meta-learning is a practical learning paradigm to transfer skills across tasks from a few examples. Nevertheless, the existence of task distribution shifts tends to weaken meta-lea…
Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation
Yuchen Yang, Yingdong Shi, Cheems Wang +3
Fine-tuning pretrained large models to downstream tasks is an important problem, which however suffers from huge memory overhead due to large-scale parameters. This work strives to…
GO4Align: Group Optimization for Multi-Task Alignment
Jiayi Shen, Cheems Wang, Zehao Xiao +2
This paper proposes \textit{GO4Align}, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks. To achieve this, we desi…