Batched Lazy Decision Trees
We introduce a batched lazy algorithm for supervised classification using decision trees. It avoids unnecessary visits to irrelevant nodes when it is used to make predictions with either eagerly or lazily trained decision trees. A set of experiments demonstrate that the proposed algorithm can outperform both the conventional and lazy decision tree algorithms in terms of computation time as well as memory consumption, without compromising accuracy.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationSimilar Papers 제목 키워드 기반
A Surprisingly Simple Continuous-Action POMDP Solver: Lazy Cross-Entropy Search Over Policy Trees
The Partially Observable Markov Decision Process (POMDP) provides a principled framework for decision making in stochastic partially observable environments. However, computing good solutions for problems with continuous…
Decision MakingLazyBatching: An SLA-aware Batching System for Cloud Machine Learning Inference
In cloud ML inference systems, batching is an essential technique to increase throughput which helps optimize total-cost-of-ownership. Prior graph batching combines the individual DNN graphs into a single one, allowing m…
BIG-bench Machine LearningSchedulingBatched Differentiable Rigid Body Dynamics in PyTorch for GPU-Accelerated Robot Learning
As robot control shifts toward large-scale reinforcement learning with in-loop dynamics computation, the community's reliance on CPU-bound libraries such as Pinocchio creates a throughput bottleneck in GPU-based training…
Reinforcement LearningLazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act
Traditionally, Reinforcement Learning (RL) aims at deciding how to act optimally for an artificial agent. We argue that deciding when to act is equally important. As humans, we drift from default, instinctive or memorize…
Atari GamesDecision Makingreinforcement-learningReinforcement Learning (RL)Semi-Parametric Batched Global Multi-Armed Bandits with Covariates
The multi-armed bandits (MAB) framework is a widely used approach for sequential decision-making, where a decision-maker selects an arm in each round with the goal of maximizing long-term rewards. Moreover, in many pract…
Decision MakingMulti-Armed BanditsRecommendation SystemsSequential Decision Making