no code implementations • 13 Apr 2024 • Chenming Shang, Shiji Zhou, Hengyuan Zhang, Xinzhe Ni, Yujiu Yang, Yuwang Wang
Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process.
no code implementations • 3 Feb 2024 • Yifei He, Shiji Zhou, Guojun Zhang, Hyokun Yun, Yi Xu, Belinda Zeng, Trishul Chilimbi, Han Zhao
To overcome this limitation, we propose Multi-Task Learning with Excess Risks (ExcessMTL), an excess risk-based task balancing method that updates the task weights by their distances to convergence instead.
1 code implementation • 15 Dec 2023 • Zhi Zhang, Qizhe Zhang, Zijun Gao, Renrui Zhang, Ekaterina Shutova, Shiji Zhou, Shanghang Zhang
With the growing size of pre-trained models, full fine-tuning and storing all the parameters for various downstream tasks is costly and infeasible.
no code implementations • 6 Sep 2023 • Tianchi Cai, Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Xierui Song, Li Yu, Lihong Gu, Xiaodong Zeng, Jinjie Gu, Guannan Zhang
We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation.
no code implementations • 25 Aug 2023 • Tianchi Cai, Shenliao Bao, Jiyan Jiang, Shiji Zhou, Wenpeng Zhang, Lihong Gu, Jinjie Gu, Guannan Zhang
Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards.
no code implementations • CVPR 2023 • Lianzhe Wang, Shiji Zhou, Shanghang Zhang, Xu Chu, Heng Chang, Wenwu Zhu
Despite the broad interest in meta-learning, the generalization problem remains one of the significant challenges in this field.
no code implementations • 13 Aug 2022 • Xin Wang, Heng Chang, Beini Xie, Tian Bian, Shiji Zhou, Daixin Wang, Zhiqiang Zhang, Wenwu Zhu
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications.
no code implementations • 25 Apr 2022 • Jing Dong, Shiji Zhou, Baoxiang Wang, Han Zhao
We thus study the problem of supervised gradual domain adaptation, where labeled data from shifting distributions are available to the learner along the trajectory, and we aim to learn a classifier on a target data distribution of interest.
1 code implementation • NeurIPS 2021 • Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu
Graph Convolutional Networks (GCNs) are promising deep learning approaches in learning representations for graph-structured data.
no code implementations • 29 Sep 2021 • Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Lihong Gu, Xiaodong Zeng, Wenwu Zhu
This paper presents a systematic study of multi-objective online learning.
no code implementations • 29 Sep 2021 • Lianzhe Wang, Shiji Zhou, Shanghang Zhang, Wenpeng Zhang, Heng Chang, Wenwu Zhu
Even though meta-learning has attracted research wide attention in recent years, the generalization problem of meta-learning is still not well addressed.
no code implementations • 11 Jun 2021 • Shiji Zhou, Han Zhao, Shanghang Zhang, Lianzhe Wang, Heng Chang, Zhi Wang, Wenwu Zhu
Our theoretical results show that OSAMD can fast adapt to changing environments with active queries.