David Salinas

Publication Activity (10 Years)

Years Active: 2009-2023
Publications (10 Years): 31

Top Topics

Demand Forecasting

Top Venues

J. Mach. Learn. Res.

Proc. VLDB Endow.

Publications

Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski
Deep Non-Parametric Time Series Forecaster. CoRR (2023)
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
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Comput. Surv. 55 (6) (2023)
David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
Optimizing Hyperparameters with Conformal Quantile Regression. CoRR (2023)
David Salinas, Nick Erickson
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications. CoRR (2023)
David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
Optimizing Hyperparameters with Conformal Quantile Regression. ICML (2023)
Sigrid Passano Hellan, Huibin Shen, François-Xavier Aubet, David Salinas, Aaron Klein
Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation. CoRR (2023)
Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, Laurent Callot
Criteria for Classifying Forecasting Methods. CoRR (2022)
Oliver Borchert, David Salinas, Valentin Flunkert, Tim Januschowski, Stephan Günnemann
Multi-Objective Model Selection for Time Series Forecasting. CoRR (2022)
David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML (2022)
Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau
Multi-objective Asynchronous Successive Halving. CoRR (2021)
Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias W. Seeger, Cédric Archambeau
Meta-Forecasting by combining Global Deep Representations with Local Adaptation. CoRR (2021)
Giovanni Zappella, David Salinas, Cédric Archambeau
A resource-efficient method for repeated HPO and NAS problems. CoRR (2021)
David Salinas, Valerio Perrone, Olivier Cruchant, Cédric Archambeau
A multi-objective perspective on jointly tuning hardware and hyperparameters. CoRR (2021)
Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas, Jan Gasthaus
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models. CoRR (2020)
David Salinas, Huibin Shen, Valerio Perrone
A Quantile-based Approach for Hyperparameter Transfer Learning. ICML (2020)
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
GluonTS: Probabilistic and Neural Time Series Modeling in Python. J. Mach. Learn. Res. 21 (2020)
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
Neural forecasting: Introduction and literature overview. CoRR (2020)
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
Elastic Machine Learning Algorithms in Amazon SageMaker. SIGMOD Conference (2020)
David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes. CoRR (2019)
Felix Bießmann, Tammo Rukat, Philipp Schmidt, Prathik Naidu, Sebastian Schelter, Andrey Taptunov, Dustin Lange, David Salinas
DataWig: Missing Value Imputation for Tables. J. Mach. Learn. Res. 20 (2019)
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
GluonTS: Probabilistic Time Series Models in Python. CoRR (2019)
David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes. NeurIPS (2019)
Dominique Attali, André Lieutier, David Salinas
When Convexity Helps Collapsing Complexes. SoCG (2019)
David Salinas, Huibin Shen, Valerio Perrone
A Copula approach for hyperparameter transfer learning. CoRR (2019)
Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski
Probabilistic Forecasting with Spline Quantile Function RNNs. AISTATS (2019)
Sebastian Schelter, Felix Bießmann, Tim Januschowski, David Salinas, Stephan Seufert, Gyuri Szarvas
On Challenges in Machine Learning Model Management. IEEE Data Eng. Bull. 41 (4) (2018)
Felix Bießmann, David Salinas, Sebastian Schelter, Philipp Schmidt, Dustin Lange
"Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data. CIKM (2018)
Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. CoRR (2017)
Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias W. Seeger, Bernie Wang
Probabilistic Demand Forecasting at Scale. Proc. VLDB Endow. 10 (12) (2017)
Valentin Flunkert, David Salinas, Jan Gasthaus
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. CoRR (2017)
Matthias W. Seeger, David Salinas, Valentin Flunkert
Bayesian Intermittent Demand Forecasting for Large Inventories. NIPS (2016)
David Salinas, Florent Lafarge, Pierre Alliez
Structure-Aware Mesh Decimation. Comput. Graph. Forum 34 (6) (2015)
Dominique Attali, André Lieutier, David Salinas
Vietoris-Rips complexes also provide topologically correct reconstructions of sampled shapes. Comput. Geom. 46 (4) (2013)
Dominique Attali, André Lieutier, David Salinas
Efficient Data Structure for Representing and Simplifying Simplicial complexes in High Dimensions. Int. J. Comput. Geom. Appl. 22 (4) (2012)
Emmett Tomai, David Salinas, Rosendo Salazar
A Rule-Based Framework for Modular Development of In-Game Interactive Dialogue Simulation. Intelligent Narrative Technologies (2011)
Dominique Attali, André Lieutier, David Salinas
Efficient data structure for representing and simplifying simplicial complexes in high dimensions. SCG (2011)
Dominique Attali, André Lieutier, David Salinas
Vietoris-rips complexes also provide topologically correct reconstructions of sampled shapes. SCG (2011)
J. Gregorio Escalada, Helenca Duxans, David Conejero, Albert Asensio, David Salinas
NewsClipping: An automatic multimedia news clipping application. CBMI (2010)
Thao Dang, David Salinas
Image Computation for Polynomial Dynamical Systems Using the Bernstein Expansion. CAV (2009)