1 citations · 3 across the 7 of their papers we have counts for
7 papers
MOON: Multi-Objective OrthoNormalized Updates for Multitask Learning
Shiji Zhou, Kunlin Lyu, Lei Zhang +2
Multi-objective optimization (MOO) has demonstrated significant success in multi-task learning by mitigating task conflicts through gradient manipulation. However, most existing me…
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
Tianchi Cai, Jiyan Jiang, Wenpeng Zhang +7
We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing b…
Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems
Tianchi Cai, Shenliao Bao, Jiyan Jiang +5
Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards. However, most…
Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling
Bowei He, Yinan Mao, Shiji Zhou +2
Current learning-based edge caching schemes usually suffer from dynamic content popularity, e.g., in the emerging short video platforms, users' request patterns shift significantly…
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification
Xin Wang, Heng Chang, Beini Xie +5
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications. Despite the huge success in le…
Algorithms and Theory for Supervised Gradual Domain Adaptation
Jing Dong, Shiji Zhou, Baoxiang Wang +1
The phenomenon of data distribution evolving over time has been observed in a range of applications, calling the needs of adaptive learning algorithms. We thus study the problem of…
Online Continual Adaptation with Active Self-Training
Shiji Zhou, Han Zhao, Shanghang Zhang +4
Models trained with offline data often suffer from continual distribution shifts and expensive labeling in changing environments. This calls for a new online learning paradigm wher…