13 citations · 19 across the 4 of their papers we have counts for
5 papers · 1 filter
Perturbation-Based Two-Stage Multi-Domain Active Learning
Rui He, Zeyu Dai, Shan He +1
In multi-domain learning (MDL) scenarios, high labeling effort is required due to the complexity of collecting data from various domains. Active Learning (AL) presents an encouragi…
Multi-Domain Learning From Insufficient Annotations
Rui He, Shengcai Liu, Jiahao Wu +2
Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains. Conventional approaches emphasize domain-…
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning
Shaohui Peng, Xing Hu, Rui Zhang +9
Hierarchical reinforcement learning (HRL) effectively improves agents' exploration efficiency on tasks with sparse reward, with the guide of high-quality hierarchical structures (e…
A New Knowledge Gradient-based Method for Constrained Bayesian Optimization
Wenjie Chen, Shengcai Liu, Ke Tang
Black-box problems are common in real life like structural design, drug experiments, and machine learning. When optimizing black-box systems, decision-makers always consider multip…
An Empirical Study of MAUC in Multi-class Problems with Uncertain Cost Matrices
Rui Wang, Ke Tang
Cost-sensitive learning relies on the availability of a known and fixed cost matrix. However, in some scenarios, the cost matrix is uncertain during training, and re-train a classi…