David Salinas - RankMe

55 papers A* 6A 2C 1Journal 38Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. J. Data-centric Mach. Learn. Res. Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger Hoos, Marius Lindauer, Theresa Eimer
2026	J	`jnl`	Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting. CoRR Azul Garza, Reneé Rosillo, Rodrigo Mendoza-Smith, David Salinas, Andrew Robert Williams, Arjun Ashok, Mononito Goswami, José Martín Juárez
2026	J	`jnl`	Multi-Objective Hierarchical Optimization with Large Language Models. CoRR Andrej Schwanke, Lyubomir Ivanov, David Salinas, Frank Hutter, Arber Zela
2025	J	`jnl`	EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks. CoRR Michael Arbel, David Salinas, Frank Hutter
2025	J	`jnl`	Improving LLM-based Global Optimization with Search Space Partitioning. CoRR Andrej Schwanke, Lyubomir Ivanov, David Salinas, Fabio Ferreira, Aaron Klein, Frank Hutter, Arber Zela
2025	J	`jnl`	MixtureVitae: Open Web-Scale Pretraining Dataset With High Quality Instruction and Reasoning Data Built from Permissive-First Text Sources. CoRR Huu Nguyen, Victor May, Harsh Raj, Marianna Nezhurina, Yishan Wang, Yanqi Luo, Minh Chien Vu, Taishi Nakamura, Ken Tsui, Van Khue Nguyen, David Salinas, Aleksandra Krasnodebska, Christoph Schuhmann, Mats Leon Richter, Xuan-Son Vu, Jenia Jitsev
2025	—	`conf`	Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimization. AutoML Sigrid Passano Hellan, Huibin Shen, Francois-Xavier Aubet, David Salinas, Aaron Klein
2025	J	`jnl`	Open-sci-ref-0.01: open and reproducible reference baselines for language model and dataset comparison. CoRR Marianna Nezhurina, Jörg K. H. Franke, Taishi Nakamura, Timur Carstensen, Niccolò Ajroldi, Ville Komulainen, David Salinas, Jenia Jitsev
2025	J	`jnl`	TabArena: A Living Benchmark for Machine Learning on Tabular Data. CoRR Nick Erickson, Lennart Purucker, Andrej Tschalzev, David Holzmüller, Prateek Mutalik Desai, David Salinas, Frank Hutter
2025	J	`jnl`	The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features. CoRR Shi Bin Hoo, Samuel Müller, David Salinas, Frank Hutter
2025	A*	`conf`	Tuning LLM Judge Design Decisions for 1/1000 of the Cost. ICML David Salinas, Omar Swelam, Frank Hutter
2025	J	`jnl`	Tuning LLM Judges Hyperparameters. CoRR David Salinas, Omar Swelam, Frank Hutter
2024	J	`jnl`	ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. CoRR Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger H. Hoos, Marius Lindauer, Theresa Eimer
2024	J	`jnl`	GAMformer: In-Context Learning for Generalized Additive Models. CoRR Andreas Mueller, Julien Siems, Harsha Nori, David Salinas, Arber Zela, Rich Caruana, Frank Hutter
2024	J	`jnl`	Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models. CoRR Sathya Kamesh Bhethanabhotla, Omar Swelam, Julien Siems, David Salinas, Frank Hutter
2024	—	`conf`	TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications. AutoML David Salinas, Nick Erickson
2023	J	`jnl`	Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Comput. Surv. 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
2023	J	`jnl`	Deep Non-Parametric Time Series Forecaster. CoRR Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski
2023	J	`jnl`	Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation. CoRR Sigrid Passano Hellan, Huibin Shen, François-Xavier Aubet, David Salinas, Aaron Klein
2023	A*	`conf`	Optimizing Hyperparameters with Conformal Quantile Regression. ICML David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
2023	J	`jnl`	Optimizing Hyperparameters with Conformal Quantile Regression. CoRR David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
2023	J	`jnl`	TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications. CoRR David Salinas, Nick Erickson
2022	J	`jnl`	Criteria for Classifying Forecasting Methods. CoRR Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, Laurent Callot
2022	J	`jnl`	Multi-Objective Model Selection for Time Series Forecasting. CoRR Oliver Borchert, David Salinas, Valentin Flunkert, Tim Januschowski, Stephan Günnemann
2022	—	`conf`	Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau
2021	J	`jnl`	A multi-objective perspective on jointly tuning hardware and hyperparameters. CoRR David Salinas, Valerio Perrone, Olivier Cruchant, Cédric Archambeau
2021	J	`jnl`	A resource-efficient method for repeated HPO and NAS problems. CoRR Giovanni Zappella, David Salinas, Cédric Archambeau
2021	J	`jnl`	Meta-Forecasting by combining Global Deep Representations with Local Adaptation. CoRR Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias W. Seeger, Cédric Archambeau
2021	J	`jnl`	Multi-objective Asynchronous Successive Halving. CoRR Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau
2020	A*	`conf`	A Quantile-based Approach for Hyperparameter Transfer Learning. ICML David Salinas, Huibin Shen, Valerio Perrone
2020	—	`conf`	Elastic Machine Learning Algorithms in Amazon SageMaker. SIGMOD Conference 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
2020	J	`jnl`	GluonTS: Probabilistic and Neural Time Series Modeling in Python. J. Mach. Learn. Res. 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
2020	J	`jnl`	Neural forecasting: Introduction and literature overview. CoRR 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
2020	J	`jnl`	The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models. CoRR Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas, Jan Gasthaus
2019	J	`jnl`	A Copula approach for hyperparameter transfer learning. CoRR David Salinas, Huibin Shen, Valerio Perrone
2019	J	`jnl`	DataWig: Missing Value Imputation for Tables. J. Mach. Learn. Res. Felix Bießmann, Tammo Rukat, Philipp Schmidt, Prathik Naidu, Sebastian Schelter, Andrey Taptunov, Dustin Lange, David Salinas
2019	J	`jnl`	GluonTS: Probabilistic Time Series Models in Python. CoRR 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
2019	J	`jnl`	High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes. CoRR David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus
2019	A*	`conf`	High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes. NeurIPS David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus
2019	A	`conf`	Probabilistic Forecasting with Spline Quantile Function RNNs. AISTATS Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, Tim Januschowski
2019	A*	`conf`	When Convexity Helps Collapsing Complexes. SoCG Dominique Attali, André Lieutier, David Salinas
2018	A	`conf`	"Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data. CIKM Felix Bießmann, David Salinas, Sebastian Schelter, Philipp Schmidt, Dustin Lange
2018	J	`jnl`	On Challenges in Machine Learning Model Management. IEEE Data Eng. Bull. Sebastian Schelter, Felix Bießmann, Tim Januschowski, David Salinas, Stephan Seufert, Gyuri Szarvas
2017	J	`jnl`	Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. CoRR Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert
2017	J	`jnl`	DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. CoRR Valentin Flunkert, David Salinas, Jan Gasthaus
2017	J	`jnl`	Probabilistic Demand Forecasting at Scale. Proc. VLDB Endow. Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias W. Seeger, Bernie Wang
2016	—	`conf`	Bayesian Intermittent Demand Forecasting for Large Inventories. NIPS Matthias W. Seeger, David Salinas, Valentin Flunkert
2015	J	`jnl`	Structure-Aware Mesh Decimation. Comput. Graph. Forum David Salinas, Florent Lafarge, Pierre Alliez
2013	J	`jnl`	Vietoris-Rips complexes also provide topologically correct reconstructions of sampled shapes. Comput. Geom. Dominique Attali, André Lieutier, David Salinas
2012	J	`jnl`	Efficient Data Structure for Representing and Simplifying Simplicial complexes in High Dimensions. Int. J. Comput. Geom. Appl. Dominique Attali, André Lieutier, David Salinas
2011	—	`conf`	A Rule-Based Framework for Modular Development of In-Game Interactive Dialogue Simulation. Intelligent Narrative Technologies Emmett Tomai, David Salinas, Rosendo Salazar
2011	—	`conf`	Efficient data structure for representing and simplifying simplicial complexes in high dimensions. SCG Dominique Attali, André Lieutier, David Salinas
2011	—	`conf`	Vietoris-rips complexes also provide topologically correct reconstructions of sampled shapes. SCG Dominique Attali, André Lieutier, David Salinas
2010	C	`conf`	NewsClipping: An automatic multimedia news clipping application. CBMI J. Gregorio Escalada, Helenca Duxans, David Conejero, Albert Asensio, David Salinas
2009	A*	`conf`	Image Computation for Polynomial Dynamical Systems Using the Bernstein Expansion. CAV Thao Dang, David Salinas