Search Results for author:
Found 8 papers, 3 papers with code
Online GentleAdaBoost -- Technical Report
We study the online variant of GentleAdaboost, where we combine a weak learner to a strong learner in an online fashion.
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures
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
We demonstrate the flexibility of this approach and how it can be adapted to online contexts where the environment is available to collect experiences and a variety of other contexts.
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning
This paper introduces Greedy UnMix (GUM) for cooperative multi-agent reinforcement learning (MARL).
Residual Networks Behave Like Boosting Algorithms
We show that Residual Networks (ResNet) is equivalent to boosting feature representation, without any modification to the underlying ResNet training algorithm.
TreeGrad: Transferring Tree Ensembles to Neural Networks
Gradient Boosting Decision Tree (GBDT) are popular machine learning algorithms with implementations such as LightGBM and in popular machine learning toolkits like Scikit-Learn.
Automatic Induction of Neural Network Decision Tree Algorithms
This work presents an approach to automatically induction for non-greedy decision trees constructed from neural network architecture.
Diverse Online Feature Selection
The framework aims to promote diversity based on the kernel produced on a feature level, through at most three stages: feature sampling, local criteria and global criteria for feature selection.
