2 citations · 6 across the 7 of their papers we have counts for
8 papers
Online GentleAdaBoost -- Technical Report
Chapman Siu
We study the online variant of GentleAdaboost, where we combine a weak learner to a strong learner in an online fashion. We provide an approach to extend the batch approach to an o…
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures
Chapman Siu, Jason Traish, Richard Yi Da Xu
We propose using regularization for Multi-Agent Reinforcement Learning rather than learning explicit cooperative structures called {\em Multi-Agent Regularized Q-learning} (MARQ).…
Dual Behavior Regularized Reinforcement Learning
Chapman Siu, Jason Traish, Richard Yi Da Xu
Reinforcement learning has been shown to perform a range of complex tasks through interaction with an environment or collected leveraging experience. However, many of these approac…
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning
Chapman Siu, Jason Traish, Richard Yi Da Xu
This paper introduces Greedy UnMix (GUM) for cooperative multi-agent reinforcement learning (MARL). Greedy UnMix aims to avoid scenarios where MARL methods fail due to overestimati…
Residual Networks Behave Like Boosting Algorithms
Chapman Siu
We show that Residual Networks (ResNet) is equivalent to boosting feature representation, without any modification to the underlying ResNet training algorithm. A regret bound based…
TreeGrad: Transferring Tree Ensembles to Neural Networks
Chapman Siu
Gradient Boosting Decision Tree (GBDT) are popular machine learning algorithms with implementations such as LightGBM and in popular machine learning toolkits like Scikit-Learn. Man…
Automatic Induction of Neural Network Decision Tree Algorithms
Chapman Siu
This work presents an approach to automatically induction for non-greedy decision trees constructed from neural network architecture. This construction can be used to transfer weig…
Diverse Online Feature Selection
Chapman Siu, Richard Yi Da Xu
Online feature selection has been an active research area in recent years. We propose a novel diverse online feature selection method based on Determinantal Point Processes (DPP).…