David Salinas
11 papers · 2016–2025 · 5 venues · across top CS/AI venues
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Data Science & Analytics
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Deep Learning
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transfer learning
(4)
quantile regression
(3)
time series
(2)
hyperparameter optimization
(2)
time series forecasting
(2)
deep learning
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gaussian copula
(2)
recurrent neural network
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gaussian process
(1)
low-rank approximation
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bayesian inference
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covariance estimation
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neural architecture search
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optimization
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feature extraction
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bayesian optimization
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distributed learning
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model evaluation
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hyperparameter tuning
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Papers
Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimization
AutoML 2025
Tuning LLM Judge Design Decisions for 1/1000 of the Cost
ICML 2025
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
AutoML 2024
Optimizing Hyperparameters with Conformal Quantile Regression
ICML 2023
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research
AutoML 2022
A Quantile-based Approach for Hyperparameter Transfer Learning
ICML 2020
GluonTS: Probabilistic and Neural Time Series Modeling in Python
JMLR 2020
DataWig: Missing Value Imputation for Tables
JMLR 2019
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes
NeurIPS 2019
Probabilistic Forecasting with Spline Quantile Function RNNs
AISTATS 2019
Bayesian Intermittent Demand Forecasting for Large Inventories
NeurIPS 2016