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.

169,291 papers · 148 categories

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11 results for “H2O”

Testing the Robustness of AutoML Systemscs.LG

Study on AutoML robustness with dirty data.

problem Robustness of AutoML-generated pipelines with noisy data.

method Investigated TPOT, H2O, and AutoKeras systems; analyzed accuracy and pipeline structure.

result Dirty data can improve robustness of AutoML solutions.

AutoML Robustness Dirty Data Machine Learning TPOT

2020-05-06abs ↗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 ↗

airpred: A Flexible R Package Implementing Methods for Predicting Air Pollutionstat.ML

A flexible R package predicts air pollution levels from unmonitored areas.

problem Lack of ambient monitors for PM2.5 and other pollutants.

method Developed a flexible R package using H2O for spatio-temporal modeling.

result Predicts multiple pollutants including PM2.5 from unmonitored areas.

air pollution PM2.5 R package spatio-temporal modeling H2O

2018-05-29abs ↗pdf ↗

Targeted Learning with Daily EHR Datastat.AP

A scalable approach to analyze daily EHR data for causal inference.

problem Inference challenges in analyzing daily EHR data due to computational cost.

method Developed a fast and scalable targeted learning approach using longitudinal TMLE and xgboost/h2o.

result Demonstrated the practical impact of using 90-day intervals for inference.

EHR data causal inference targeted learning TMLE xgboost

2017-05-27abs ↗pdf ↗

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillationcs.LG

FAST-DAD distills complex ensemble models into faster, more accurate individual models.

problem Deploying complex AutoML ensemble predictors on tabular data is slow, large, and opaque.

method Data augmentation strategy based on Gibbs sampling from a self-attention pseudolikelihood estimator.

result FAST-DAD distillation produces significantly better individual models than standard training.

AutoML ensemble models tabular data distillation data augmentation

2020-06-25abs ↗pdf ↗

SySCD: A System-Aware Parallel Coordinate Descent Algorithmcs.LG

SySCD improves SCD scalability and speeds up training.

problem Scalability issues in parallel SCD algorithms.

method Developed a system-aware parallel SCD algorithm (SySCD) to avoid bottlenecks.

result Offers up to x42 speedup compared to state-of-the-art GLM solvers.

parallel computing coordinate descent scalability system-aware machine learning

2019-11-18abs ↗pdf ↗

Parallel training of linear models without compromising convergencecs.LG

Optimizes parallel training of linear models, improving convergence.

problem Improving convergence of parallel training of linear models.

method Data partitioning scheme across threads to improve convergence.

result Achieved up to 42x speedup in convergence compared to state of the art implementations.

linear models parallel training convergence data partitioning cache optimization

2018-11-05abs ↗pdf ↗

Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Researchstat.ML

Study improves prediction of UK road accidents' severity using AI.

problem Improving prediction of UK road traffic accident severity.

method Combination of machine learning, econometric, and statistical methods on historical data.

result XGBoost model with RMSE of 0.176 and MAE of 0.087 outperforms naive forecasting.

UK road traffic accidents AI machine learning econometrics time series forecasting

2023-09-23abs ↗pdf ↗

Breadth-first, Depth-next Training of Random Forestscs.LG

The paper optimizes RF training by improving tree building algorithms and CPU optimizations.

problem Improving the training performance of Random Forest models on CPU architectures.

method Investigated and improved tree building algorithms (BFS, DFS, hybrid BFS-DFS) and proposed optimizations.

result The hybrid BFS-DFS algorithm outperforms both BFS and DFS, and is more robust.

Random Forest Tree Building CPU Optimization Hybrid Algorithm Training Performance

2019-10-15abs ↗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 ↗

NeuralSens: Sensitivity Analysis of Neural Networkscs.LG

Package provides sensitivity analysis for neural networks.

problem Understanding how input variables affect neural network predictions.

method Partial derivatives method to calculate sensitivities.

result Allows evaluation of variable importance and input-output relationships.

neural networks sensitivity analysis partial derivatives variable importance input-output relationships

2020-02-26abs ↗pdf ↗