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pro vyhledávání: '"Siu, Chapman"'
Autor:
Siu, Chapman
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 online approach with theoretical justifications through application of
Externí odkaz:
http://arxiv.org/abs/2308.14004
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 approaches presume optimal or near optimal experiences or the presence of a c
Externí odkaz:
http://arxiv.org/abs/2109.09037
We propose using regularization for Multi-Agent Reinforcement Learning rather than learning explicit cooperative structures called {\em Multi-Agent Regularized Q-learning} (MARQ). Many MARL approaches leverage centralized structures in order to explo
Externí odkaz:
http://arxiv.org/abs/2109.09038
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 overestimation of values as part of the large joint state-action space. It aims to
Externí odkaz:
http://arxiv.org/abs/2109.09034
Autor:
Siu, Chapman
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 on Online Gradient Boosting theory is proved and suggests that ResNet
Externí odkaz:
http://arxiv.org/abs/1909.11790
Autor:
Siu, Chapman
Gradient Boosting Decision Tree (GBDT) are popular machine learning algorithms with implementations such as LightGBM and in popular machine learning toolkits like Scikit-Learn. Many implementations can only produce trees in an offline manner and in a
Externí odkaz:
http://arxiv.org/abs/1904.11132
Autor:
Siu, Chapman
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 weights when growing or pruning a decision tree, allowing non-greedy decis
Externí odkaz:
http://arxiv.org/abs/1811.10735
Autor:
Siu, Chapman, Da Xu, Richard Yi
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). Our model aims to provide diverse features which can be composed in ei
Externí odkaz:
http://arxiv.org/abs/1806.04308