Autostacker: A Compositional Evolutionary Learning System

2018-03-02 · Boyuan Chen, Harvey Wu, Warren Mo, Ishanu Chattopadhyay, Hod Lipson

We introduce an automatic machine learning (AutoML) modeling architecture called Autostacker, which combines an innovative hierarchical stacking architecture and an Evolutionary Algorithm (EA) to perform efficient parameter search. Neither prior domain knowledge about the data nor feature preprocessing is needed. Using EA, Autostacker quickly evolves candidate pipelines with high predictive accuracy. These pipelines can be used as is or as a starting point for human experts to build on. Autostacker finds innovative combinations and structures of machine learning models, rather than selecting a single model and optimizing its hyperparameters. Compared with other AutoML systems on fifteen datasets, Autostacker achieves state-of-art or competitive performance both in terms of test accuracy and time cost.

📄 PDF Abstract BibTeX arXiv:1803.00684

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AutoML BIG-bench Machine Learning Hyperparameter Optimization

Autostacker: an Automatic Evolutionary Hierarchical Machine Learning System

2018-01-01 · ICLR 2018 1 · Boyuan Chen, Warren Mo, Ishanu Chattopadhyay, Hod Lipson

This work provides an automatic machine learning (AutoML) modelling architecture called Autostacker. Autostacker improves the prediction accuracy of machine learning baselines by utilizing an innovative hierarchical stac…

AutoML BIG-bench Machine Learning

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2018-11-20 · Qi Fei, Xia Zhaohui, Tang Gaoyang, Yang Hang 외

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexi…

AutoML BIG-bench Machine Learning

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We introduce AlphaD3M, an automatic machine learning (AutoML) system based on meta reinforcement learning using sequence models with self play. AlphaD3M is based on edit operations performed over machine learning pipelin…

AutoML BIG-bench Machine Learning Meta Reinforcement Learning reinforcement-learning+2

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2019-04-19 · ICLR 2020 1 · Michael Cogswell, Jiasen Lu, Stefan Lee, Devi Parikh 외

Recent work has studied the emergence of language among deep reinforcement learning agents that must collaborate to solve a task. Of particular interest are the factors that cause language to be compositional -- i.e., ex…

Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)

Compositionality and the lexicon in evolutionary semantics

2026-06-25 · Fausto Carcassi arxiv

Formal semantics has shown that sentence meanings arise by recursively composing lexical meanings, yet much of the literature on semantic universals models either lexicons with fixed signal structures or holistic composi…