On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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14 results for “Auto-sklearn”

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learningcs.LG

Auto-sklearn 2.0 simplifies AutoML with meta-learning and meta-feature-free techniques.

problem Designing efficient machine learning pipelines for large datasets under time constraints.

method PoSH Auto-sklearn uses meta-learning and bandit strategy for budget allocation.

result Reduces relative error by up to a factor of 4.5 and improves performance in 10 minutes.

AutoML meta-learning bandit strategy meta-feature-free hands-free

2020-07-08abs ↗pdf ↗

Benchmarking Automatic Machine Learning Frameworkscs.LG

AutoML benchmarks compare different frameworks.

problem No objective comparison of AutoML frameworks exists.

method Benchmarked four open-source AutoML solutions using open-source datasets.

result auto-sklearn outperformed TPOT in classification and regression tasks respectively.

AutoML Benchmarking Machine Learning Auto-sklearn TPOT

2018-08-17abs ↗pdf ↗

ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learningcs.LG

A hybrid RL-Bayesian search configures machine learning pipelines efficiently.

problem Optimizing hyper-parameters in a hierarchical conditional space.

method Combines Reinforcement Learning and Bayesian Optimization.

result Outperforms state-of-the-art methods in pipeline optimization.

machine learning pipeline optimization hyperparameter tuning Bayesian optimization Reinforcement Learning

2019-04-10abs ↗pdf ↗

Automated Multi-Label Classification based on ML-Plancs.LG

This paper automates multi-label classification using an extended AutoML tool.

problem Lack of automated solutions for multi-label classification.

method Extends ML-Plan for multi-class classification to multi-label classification using MEKA.

result The proposed approach yields superb results and performs significantly better than baselines.

AutoML Multi-label Classification Machine Learning WEKA MEKA

2018-11-09abs ↗pdf ↗

Automated Machine Learning with Monte-Carlo Tree Searchcs.LG

AutoML uses MCTS to optimize machine learning algorithms and hyperparameters.

problem Optimizing machine learning algorithms and hyperparameters efficiently.

method MCTS-based approach for hybrid optimization of machine learning portfolios.

result Mosaic outperforms Auto-Sklearn on OpenML 100 benchmark and Scikit-learn portfolio.

AutoML Monte-Carlo Tree Search Machine Learning Optimization Hyperparameter Tuning

2019-06-01abs ↗pdf ↗

DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learningcs.NE

A graph-based evolutionary algorithm automates machine learning workflows.

problem Automated machine learning to reduce manual operations.

method Graph-based architecture for flexible model combinations, evolutionary algorithm with mutation and heredity operators, Bayesian hyper-parameter optimization.

result State-of-the-art performance compared to other AutoML systems.

AutoML Evolutionary Algorithm Machine Learning Bayesian Optimization Graph-based Architecture

2018-11-20abs ↗pdf ↗

Towards AutoML in the presence of Drift: first resultscs.LG

AutoML tackles evolving data with drift detection.

problem Handling changing data distributions in AutoML.

method Extended Auto-Sklearn with drift detection mechanisms.

result Demonstrated effectiveness of the proposed methodology.

AutoML Drift Detection Lifelong Learning Concept Drift Benchmark Data

2019-07-24abs ↗pdf ↗

AVATAR -- Machine Learning Pipeline Evaluation Using Surrogate Modelcs.LG

AVATAR uses a surrogate model to quickly evaluate ML pipelines, saving time and resources.

problem Time-consuming evaluation of ML pipelines limits exploration of complex models.

method AVATAR employs a surrogate model to assess pipeline validity without execution.

result AVATAR accelerates ML pipeline evaluation, improving efficiency in complex scenarios.

machine learning pipeline evaluation surrogate model time efficiency pipeline optimisation

2020-01-30abs ↗pdf ↗

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datastat.ML

AutoGluon-Tabular automates tabular data ML with single line Python.

problem Training accurate ML models on unprocessed tabular data.

method Ensembling multiple models in multiple layers.

result Outperforms competitors in accuracy and robustness.

AutoML tabular data machine learning ensemble stacking

2020-03-13abs ↗pdf ↗

An ADMM Based Framework for AutoML Pipeline Configurationcs.LG

A new AutoML framework uses ADMM to optimize machine learning pipeline configurations.

problem Optimizing machine learning pipelines with mixed integer and continuous variables.

method Leverages ADMM to decompose and solve the optimization problem.

result Significant gains in comparison to other AutoML frameworks.

AutoML ADMM Machine Learning Pipelines Mixed Integer Continuous Variables

2019-05-01abs ↗pdf ↗

Automatic Gradient Boostingstat.ML

Simplifies AutoML by using gradient boosting alone.

problem Model selection and hyperparameter optimization for real-world applications.

method Gradient boosting as the sole learning algorithm, combined with model-based hyperparameter tuning and feature encoding.

result Achieves comparable results on half of the datasets and best results on two compared to current AutoML projects.

AutoML Gradient Boosting Model Selection Hyperparameter Optimization Real-world Applications

2018-07-10abs ↗pdf ↗

Automatic Componentwise Boosting: An Interpretable AutoML Systemstat.ML

AutoML system boosts interpretability without sacrificing performance.

problem Laypeople struggle with interpreting complex ensembles of black-box models.

method Componentwise boosting algorithm for interpretable additive models.

result Interpretable models are competitive in performance and user-friendly.

AutoML interpretable models componentwise boosting machine learning interpretability

2021-09-12abs ↗pdf ↗

Predicting the Outcome of Judicial Decisions made by the European Court of Human Rightscs.LG

Machine learning predicts ECHR judgments on human rights violations.

problem Predicting the outcome of ECHR judgments on human rights violations.

method Auto-sklearn for model selection, N-grams, word embeddings, doc2vec, echr2vec for feature extraction, cross-validation for accuracy assessment.

result Features from echr2vec embedding provided the highest cross-validation accuracy for 5 Articles, overall test accuracy was 68.83%.

European Court of Human Rights Judicial Decisions Machine Learning Human Rights Binary Classification

2019-12-16abs ↗pdf ↗

Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for Czechiastat.ML

Study uses SAR data to estimate forest vegetation indices, improving monitoring of temperate forests.

problem Limitations of optical satellite data in monitoring forest ecosystems, especially due to atmospheric effects.

method Estimating four vegetation indices (LAI, FAPAR, EVI, NDVI) using multitemporal Sentinel-1 SAR and ancillary data.

result Accurate estimation of forest vegetation indices using SAR data, achieving high

R^2

and low MAE values.

SAR data forest monitoring vegetation indices machine learning Sentinel-1

2023-11-13abs ↗pdf ↗