400 citations · 420 across the 7 of their papers we have counts for
7 papers
Regret Analysis for Hierarchical Experts Bandit Problem
Qihan Guo, Siwei Wang, Jun Zhu
We study an extension of standard bandit problem in which there are R layers of experts. Multi-layered experts make selections layer by layer and only the experts in the last layer…
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data
Wenkai Li, Cheng Feng, Ting Chen +1
Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. In recent years, a large number of deep neural network-based method…
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization
Wentse Chen, Shiyu Huang, Yuan Chiang +4
Most reinforcement learning algorithms seek a single optimal strategy that solves a given task. However, it can often be valuable to learn a diverse set of solutions, for instance,…
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
Shilong Liu, Feng Li, Hao Zhang +5
We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This…
TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations
Shiyu Huang, Wenze Chen, Longfei Zhang +6
Deep reinforcement learning (DRL) has achieved super-human performance on complex video games (e.g., StarCraft II and Dota II). However, current DRL systems still suffer from chall…
Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization
Shiyu Huang, Bin Wang, Dong Li +3
Circuit routing has been a historically challenging problem in designing electronic systems such as very large-scale integration (VLSI) and printed circuit boards (PCBs). The main…
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy
Nicolas Duminy, Sao Mai Nguyen, Junshuai Zhu +2
In open-ended continuous environments, robots need to learn multiple parameterised control tasks in hierarchical reinforcement learning. We hypothesise that the most complex tasks…