David Salinas

According to our database, David Salinas authored at least 35 papers between 2009 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey.

Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, François-Xavier Aubet, Laurent Callot, Tim Januschowski

ACM Comput. Surv., 2023

Deep Non-Parametric Time Series Forecaster.

Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski

CoRR, 2023

TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications.

David Salinas, Nick Erickson

CoRR, 2023

Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation.

Sigrid Passano Hellan, Huibin Shen, François-Xavier Aubet, David Salinas, Aaron Klein

CoRR, 2023

Optimizing Hyperparameters with Conformal Quantile Regression.

David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau

Proceedings of the International Conference on Machine Learning, 2023

2022

Criteria for Classifying Forecasting Methods.

Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, Laurent Callot

CoRR, 2022

Multi-Objective Model Selection for Time Series Forecasting.

Oliver Borchert, David Salinas, Valentin Flunkert, Tim Januschowski, Stephan Günnemann

CoRR, 2022

Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research.

David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau

Proceedings of the International Conference on Automated Machine Learning, 2022

2021

Meta-Forecasting by combining Global Deep Representations with Local Adaptation.

Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias W. Seeger, Cédric Archambeau

CoRR, 2021

Multi-objective Asynchronous Successive Halving.

Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau

CoRR, 2021

A multi-objective perspective on jointly tuning hardware and hyperparameters.

David Salinas, Valerio Perrone, Olivier Cruchant, Cédric Archambeau

CoRR, 2021

A resource-efficient method for repeated HPO and NAS problems.

Giovanni Zappella, David Salinas, Cédric Archambeau

CoRR, 2021

2020

GluonTS: Probabilistic and Neural Time Series Modeling in Python.

Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Sundar Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang

J. Mach. Learn. Res., 2020

The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models.

Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas, Jan Gasthaus

CoRR, 2020

Neural forecasting: Introduction and literature overview.

Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Bernie Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Laurent Callot, Tim Januschowski

CoRR, 2020

Elastic Machine Learning Algorithms in Amazon SageMaker.

Edo Liberty, Zohar S. Karnin, Bing Xiang, Laurence Rouesnel, Baris Coskun, Ramesh Nallapati, Julio Delgado, Amir Sadoughi, Yury Astashonok, Piali Das, Can Balioglu, Saswata Chakravarty, Madhav Jha, Philip Gautier, David Arpin, Tim Januschowski, Valentin Flunkert, Yuyang Wang, Jan Gasthaus, Lorenzo Stella, Syama Sundar Rangapuram, David Salinas, Sebastian Schelter, Alex Smola

Proceedings of the 2020 International Conference on Management of Data, 2020

A Quantile-based Approach for Hyperparameter Transfer Learning.

David Salinas, Huibin Shen, Valerio Perrone

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

DataWig: Missing Value Imputation for Tables.

Felix Bießmann, Tammo Rukat, Philipp Schmidt, Prathik Naidu, Sebastian Schelter, Andrey Taptunov, Dustin Lange, David Salinas

J. Mach. Learn. Res., 2019

A Copula approach for hyperparameter transfer learning.

David Salinas, Huibin Shen, Valerio Perrone

CoRR, 2019

GluonTS: Probabilistic Time Series Models in Python.

Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Sundar Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang

CoRR, 2019

High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes.

David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

When Convexity Helps Collapsing Complexes.

Dominique Attali, André Lieutier, David Salinas

Proceedings of the 35th International Symposium on Computational Geometry, 2019

Probabilistic Forecasting with Spline Quantile Function RNNs.

Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

On Challenges in Machine Learning Model Management.

Sebastian Schelter, Felix Bießmann, Tim Januschowski, David Salinas, Stephan Seufert, Gyuri Szarvas

IEEE Data Eng. Bull., 2018

"Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data.

Felix Bießmann, David Salinas, Sebastian Schelter, Philipp Schmidt, Dustin Lange

Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018

2017

Probabilistic Demand Forecasting at Scale.

Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias W. Seeger, Bernie Wang

Proc. VLDB Endow., 2017

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale.

Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert

CoRR, 2017

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks.

Valentin Flunkert, David Salinas, Jan Gasthaus

CoRR, 2017

2016

Bayesian Intermittent Demand Forecasting for Large Inventories.

Matthias W. Seeger, David Salinas, Valentin Flunkert

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Structure-Aware Mesh Decimation.

David Salinas, Florent Lafarge, Pierre Alliez

Comput. Graph. Forum, 2015

2013

Vietoris-Rips complexes also provide topologically correct reconstructions of sampled shapes.

Dominique Attali, André Lieutier, David Salinas

Comput. Geom., 2013

2012

Efficient Data Structure for Representing and Simplifying Simplicial complexes in High Dimensions.

Dominique Attali, André Lieutier, David Salinas

Int. J. Comput. Geom. Appl., 2012

2011

A Rule-Based Framework for Modular Development of In-Game Interactive Dialogue Simulation.

Emmett Tomai, David Salinas, Rosendo Salazar

Proceedings of the Intelligent Narrative Technologies IV, 2011

2010

NewsClipping: An automatic multimedia news clipping application.

J. Gregorio Escalada, Helenca Duxans, David Conejero, Albert Asensio, David Salinas

Proceedings of the 2010 International Workshop on Content-Based Multimedia Indexing, 2010

2009

Image Computation for Polynomial Dynamical Systems Using the Bernstein Expansion.

Thao Dang, David Salinas

Proceedings of the Computer Aided Verification, 21st International Conference, 2009