Salinas, David

20 publications

ICLR 2026 Improving LLM-Based Global Optimization with Search Space Partitioning Andrej Schwanke, Lyubomir Ivanov, David Salinas, Fabio Ferreira, Aaron Klein, Frank Hutter, Arber Zela

TMLR 2026 MixtureVitae: Open Web-Scale Pretraining Dataset with High Quality Instruction and Reasoning Data Built from Permissive-First Text Sources Huu Nguyen, Victor May, Harsh Raj, Marianna Nezhurina, Yishan Wang, Yanqi Luo, Vu Minh Chien, Taishi Nakamura, Ken Tsui, Van Khue Nguyen, David Salinas, Aleksandra Krasnodębska, Christoph Schuhmann, Mats Leon Richter, Xuan-Son Vu, Jenia Jitsev

NeurIPS 2025 EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Network Michael Arbel, David Salinas, Frank Hutter

AutoML 2025 Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimization Sigrid Passano Hellan, Huibin Shen, Francois-Xavier Aubet, David Salinas, Aaron Klein

NeurIPS 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

ICML 2025 Tuning LLM Judge Design Decisions for 1/1000 of the Cost David Salinas, Omar Swelam, Frank Hutter

NeurIPSW 2024 GAMformer: Exploring In-Context Learning for Generalized Additive Models Andreas C Mueller, Julien Siems, Harsha Nori, David Salinas, Arber Zela, Rich Caruana, Frank Hutter

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

AutoML 2024 TabRepo: A Large Scale Repository of Tabular Model Evaluations and Its AutoML Applications David Salinas, Nick Erickson

NeurIPSW 2024 The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features Shi Bin Hoo, Samuel Müller, David Salinas, Frank Hutter

NeurIPSW 2024 The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features Shi Bin Hoo, Samuel Müller, David Salinas, Frank Hutter

ICML 2023 Optimizing Hyperparameters with Conformal Quantile Regression David Salinas, Jacek Golebiowski, Aaron Klein, Matthias Seeger, Cedric Archambeau

AutoML 2022 Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research David Salinas, Matthias Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cedric Archambeau

ICMLW 2021 A Resource-Efficient Method for Repeated HPO and NAS Problems Giovanni Zappella, David Salinas, Cedric Archambeau

ICML 2020 A Quantile-Based Approach for Hyperparameter Transfer Learning David Salinas, Huibin Shen, Valerio Perrone

MLOSS 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 Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang

JMLR 2019 DataWig: Missing Value Imputation for Tables Felix Biessmann, Tammo Rukat, Phillipp Schmidt, Prathik Naidu, Sebastian Schelter, Andrey Taptunov, Dustin Lange, David Salinas

NeurIPS 2019 High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus

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

NeurIPS 2016 Bayesian Intermittent Demand Forecasting for Large Inventories Matthias W Seeger, David Salinas, Valentin Flunkert