Image Computation for Polynomial Dynamical Systems Using the Bernstein Expansion. - Thao Dang 001; David Salinas (CAV, 2009)

NewsClipping: An automatic multimedia news clipping application. - J. Gregorio Escalada; Helenca Duxans; David Conejero; Albert Asensio; David Salinas (CBMI, 2010)

A Rule-Based Framework for Modular Development of In-Game Interactive Dialogue Simulation. - Emmett Tomai; David Salinas; Rosendo Salazar (Intelligent Narrative Technologies, 2011)

Efficient Data Structure for Representing and Simplifying Simplicial complexes in High Dimensions. - Dominique Attali; André Lieutier; David Salinas (Int. J. Comput. Geom. Appl., 2012)

Vietoris-Rips complexes also provide topologically correct reconstructions of sampled shapes. - Dominique Attali; André Lieutier; David Salinas (Comput. Geom., 2013)

Structure-Aware Mesh Decimation. - David Salinas; Florent Lafarge; Pierre Alliez (Comput. Graph. Forum, 2015)

Bayesian Intermittent Demand Forecasting for Large Inventories. - Matthias W. Seeger; David Salinas; Valentin Flunkert (NIPS, 2016)

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. - Valentin Flunkert; David Salinas; Jan Gasthaus (CoRR, 2017)

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. - Matthias W. Seeger; Syama Sundar Rangapuram; Yuyang Wang 001; David Salinas; Jan Gasthaus; Tim Januschowski; Valentin Flunkert (CoRR, 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 001 (Proc. VLDB Endow., 2017)

"Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data. - Felix Bießmann; David Salinas; Sebastian Schelter; Philipp Schmidt 002; Dustin Lange (CIKM, 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)

Probabilistic Forecasting with Spline Quantile Function RNNs. - Jan Gasthaus; Konstantinos Benidis; Yuyang Wang 001; Syama Sundar Rangapuram; David Salinas; Valentin Flunkert; Tim Januschowski (AISTATS, 2019)

When Convexity Helps Collapsing Complexes. - Dominique Attali; André Lieutier; David Salinas (SoCG, 2019)

High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes. - David Salinas; Michael Bohlke-Schneider; Laurent Callot; Roberto Medico; Jan Gasthaus (NeurIPS, 2019)

GluonTS: Probabilistic Time Series Models in Python. - Alexander Alexandrov 001; 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; et al. (CoRR, 2019)

A Copula approach for hyperparameter transfer learning. - David Salinas; Huibin Shen; Valerio Perrone (CoRR, 2019)

DataWig: Missing Value Imputation for Tables. - Felix Bießmann; Tammo Rukat; Philipp Schmidt 002; Prathik Naidu; Sebastian Schelter; Andrey Taptunov; Dustin Lange; David Salinas (J. Mach. Learn. Res., 2019)

A Quantile-based Approach for Hyperparameter Transfer Learning. - David Salinas; Huibin Shen; Valerio Perrone (ICML, 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; et al. (SIGMOD Conference, 2020)

Neural forecasting: Introduction and literature overview. - Konstantinos Benidis; Syama Sundar Rangapuram; Valentin Flunkert; Bernie Wang 001; Danielle C. Maddix; Ali Caner Türkmen; Jan Gasthaus; Michael Bohlke-Schneider; David Salinas; Lorenzo Stella; Laurent Callot; Tim Januschowski (CoRR, 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)

GluonTS: Probabilistic and Neural Time Series Modeling in Python. - Alexander Alexandrov 001; 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; et al. (J. Mach. Learn. Res., 2020)

A resource-efficient method for repeated HPO and NAS problems. - Giovanni Zappella; 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)

Multi-objective Asynchronous Successive Halving. - Robin Schmucker; Michele Donini; Muhammad Bilal Zafar; David Salinas; Cédric Archambeau (CoRR, 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)

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 (AutoML, 2022)

Multi-Objective Model Selection for Time Series Forecasting. - Oliver Borchert; David Salinas; Valentin Flunkert; Tim Januschowski; Stephan Günnemann (CoRR, 2022)

Criteria for Classifying Forecasting Methods. - Tim Januschowski; Jan Gasthaus; Yuyang Wang 001; David Salinas; Valentin Flunkert; Michael Bohlke-Schneider; Laurent Callot (CoRR, 2022)

Optimizing Hyperparameters with Conformal Quantile Regression. - David Salinas; Jacek Golebiowski; Aaron Klein; Matthias W. Seeger; Cédric Archambeau (ICML, 2023)

Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation. - Sigrid Passano Hellan; Huibin Shen; François-Xavier Aubet; David Salinas; Aaron Klein (CoRR, 2023)

Deep Non-Parametric Time Series Forecaster. - Syama Sundar Rangapuram; Jan Gasthaus; Lorenzo Stella; Valentin Flunkert; David Salinas; Yuyang Wang 001; Tim Januschowski (CoRR, 2023)

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. - Konstantinos Benidis; Syama Sundar Rangapuram; Valentin Flunkert; Yuyang Wang 001; Danielle C. Maddix; Ali Caner Türkmen; Jan Gasthaus; Michael Bohlke-Schneider; David Salinas; Lorenzo Stella; François-Xavier Aubet; Laurent Callot; et al. (ACM Comput. Surv., 2023)

TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications. - David Salinas; Nick Erickson (AutoML, 2024)

ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. - Jannis Becktepe; Julian Dierkes; Carolin Benjamins; Aditya Mohan; David Salinas; Raghu Rajan; Frank Hutter; Holger H. Hoos; Marius Lindauer; Theresa Eimer (CoRR, 2024)

GAMformer: In-Context Learning for Generalized Additive Models. - Andreas Mueller; Julien Siems; Harsha Nori; David Salinas; Arber Zela; Rich Caruana; Frank Hutter (CoRR, 2024)

Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models. - Sathya Kamesh Bhethanabhotla; Omar Swelam; Julien Siems; David Salinas; Frank Hutter (CoRR, 2024)

The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features. - Shi Bin Hoo; Samuel Müller 005; David Salinas; Frank Hutter (CoRR, 2025)

Tuning LLM Judges Hyperparameters. - David Salinas; Omar Swelam; Frank Hutter (CoRR, 2025)

EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks. - Michael Arbel; David Salinas; Frank Hutter (CoRR, 2025)

Improving LLM-based Global Optimization with Search Space Partitioning. - Andrej Schwanke; Lyubomir Ivanov; David Salinas; Fabio Ferreira; Aaron Klein; Frank Hutter; Arber Zela (CoRR, 2025)

TabArena: A Living Benchmark for Machine Learning on Tabular Data. - Nick Erickson; Lennart Purucker; Andrej Tschalzev; David Holzmüller; Prateek Mutalik Desai; David Salinas; Frank Hutter (CoRR, 2025)