Classification of Sparse Time Series via Supervised Matrix Factorization. - Josif Grabocka; Alexandros Nanopoulos; Lars Schmidt-Thieme (AAAI, 2012)

Efficient Classification of Long Time-Series. - Josif Grabocka; Erind Bedalli; Lars Schmidt-Thieme (ICT Innovations, 2012)

Invariant Time-Series Classification. - Josif Grabocka; Alexandros Nanopoulos; Lars Schmidt-Thieme (ECML/PKDD (2), 2012)

Supervised Dimensionality Reduction via Nonlinear Target Estimation. - Josif Grabocka; Lucas Drumond; Lars Schmidt-Thieme (DaWaK, 2013)

Invariant Factorization Of Time-Series. - Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2013)

Time-Series Classification Through Histograms of Symbolic Polynomials. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (CoRR, 2013)

Realistic optimal policies for energy-efficient train driving. - Josif Grabocka; Alexandros Dalkalitsis; Athanasios Lois; Evangelos Katsaros; Lars Schmidt-Thieme (ITSC, 2014)

Learning time-series shapelets. - Josif Grabocka; Nicolas Schilling; Martin Wistuba; Lars Schmidt-Thieme (KDD, 2014)

Supervised Nonlinear Factorizations Excel In Semi-supervised Regression. - Josif Grabocka; Erind Bedalli; Lars Schmidt-Thieme (PAKDD (1), 2014)

Invariant time-series factorization. - Josif Grabocka; Lars Schmidt-Thieme (Data Min. Knowl. Discov., 2014)

Optimal Time-Series Motifs. - Josif Grabocka; Nicolas Schilling; Lars Schmidt-Thieme (CoRR, 2015)

Scalable Discovery of Time-Series Shapelets. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (CoRR, 2015)

Ultra-Fast Shapelets for Time Series Classification. - Martin Wistuba; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2015)

Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (IEEE Trans. Knowl. Data Eng., 2015)

Learning Through Non-linearly Supervised Dimensionality Reduction. - Josif Grabocka; Lars Schmidt-Thieme (Trans. Large Scale Data Knowl. Centered Syst., 2015)

Learning DTW-Shapelets for Time-Series Classification. - Mit Shah; Josif Grabocka; Nicolas Schilling; Martin Wistuba; Lars Schmidt-Thieme (CODS, 2016)

Fast classification of univariate and multivariate time series through shapelet discovery. - Josif Grabocka; Martin Wistuba; Lars Schmidt-Thieme (Knowl. Inf. Syst., 2016)

Latent Time-Series Motifs. - Josif Grabocka; Nicolas Schilling; Lars Schmidt-Thieme (ACM Trans. Knowl. Discov. Data, 2016)

Personalized Deep Learning for Tag Recommendation. - Hanh T. H. Nguyen; Martin Wistuba; Josif Grabocka; Lucas Rêgo Drumond; Lars Schmidt-Thieme (PAKDD (1), 2017)

Channel masking for multivariate time series shapelets. - Dripta S. Raychaudhuri; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2017)

NeuralWarp: Time-Series Similarity with Warping Networks. - Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2018)

Gait Verification using Deep Learning with a Pairwise Loss. - Vijaya Krishna Yalavarthi; Josif Grabocka; Hareesh Mandalapu; Lars Schmidt-Thieme (BIOSIG, 2019)

Multi-Label Network Classification via Weighted Personalized Factorizations. - Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme (ICAART (2), 2019)

Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights. - Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme (ICAART (Revised Selected Papers), 2019)

A Hybrid Convolutional Approach for Parking Availability Prediction. - Hadi Samer Jomaa; Josif Grabocka; Lars Schmidt-Thieme; Alexander Borek (IJCNN, 2019)

Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings. - Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme (KDD, 2019)

Ring-Star: A Sparse Topology for Faster Model Averaging in Decentralized Parallel SGD. - Mohsan Jameel; Josif Grabocka; Mofassir ul Islam Arif; Lars Schmidt-Thieme (PKDD/ECML Workshops (1), 2019)

A Deep Multi-task Approach for Residual Value Forecasting. - Ahmed Rashed; Shayan Jawed; Jens Rehberg; Josif Grabocka; Lars Schmidt-Thieme; Andre Hintsches (ECML/PKDD (3), 2019)

Attribute-aware non-linear co-embeddings of graph features. - Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme (RecSys, 2019)

Data-Driven Vehicle Trajectory Forecasting. - Shayan Jawed; Eya Boumaiza; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2019)

Learning Surrogate Losses. - Josif Grabocka; Randolf Scholz; Lars Schmidt-Thieme (CoRR, 2019)

In Hindsight: A Smooth Reward for Steady Exploration. - Hadi S. Jomaa; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2019)

Hyp-RL : Hyperparameter Optimization by Reinforcement Learning. - Hadi S. Jomaa; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2019)

Chameleon: Learning Model Initializations Across Tasks With Different Schemas. - Lukas Brinkmeyer; Rafael Rêgo Drumond; Randolf Scholz; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2019)

Phantom Embeddings: Using Embeddings Space for Model Regularization in Deep Neural Networks. - Mofassir ul Islam Arif; Mohsan Jameel; Josif Grabocka; Lars Schmidt-Thieme (LWDA, 2020)

Self-supervised Learning for Semi-supervised Time Series Classification. - Shayan Jawed; Josif Grabocka; Lars Schmidt-Thieme (PAKDD (1), 2020)

HIDRA: Head Initialization across Dynamic targets for Robust Architectures. - Rafael Rêgo Drumond; Lukas Brinkmeyer; Josif Grabocka; Lars Schmidt-Thieme (SDM, 2020)

Scalable Pareto Front Approximation for Deep Multi-Objective Learning. - Michael Ruchte; Josif Grabocka (ICDM, 2021)

Few-Shot Bayesian Optimization with Deep Kernel Surrogates. - Martin Wistuba; Josif Grabocka (ICLR, 2021)

Well-tuned Simple Nets Excel on Tabular Datasets. - Arlind Kadra; Marius Lindauer; Frank Hutter; Josif Grabocka (NeurIPS, 2021)

HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. - Sebastian Pineda-Arango; Hadi S. Jomaa; Martin Wistuba; Josif Grabocka (NeurIPS Datasets and Benchmarks, 2021)

Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets. - Shayan Jawed; Hadi S. Jomaa; Lars Schmidt-Thieme; Josif Grabocka (ECML/PKDD (1), 2021)

A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning. - Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme (SIGIR, 2021)

Hyperparameter Optimization with Differentiable Metafeatures. - Hadi S. Jomaa; Lars Schmidt-Thieme; Josif Grabocka (CoRR, 2021)

Efficient Multi-Objective Optimization for Deep Learning. - Michael Ruchte; Josif Grabocka (CoRR, 2021)

Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data. - Arlind Kadra; Marius Lindauer; Frank Hutter; Josif Grabocka (CoRR, 2021)

Multi-task problems are not multi-objective. - Michael Ruchte; Josif Grabocka (CoRR, 2021)

Dataset2Vec: learning dataset meta-features. - Hadi S. Jomaa; Lars Schmidt-Thieme; Josif Grabocka (Data Min. Knowl. Discov., 2021)

Transformers Can Do Bayesian Inference. - Samuel Müller 005; Noah Hollmann; Sebastian Pineda-Arango; Josif Grabocka; Frank Hutter (ICLR, 2022)

Zero-shot AutoML with Pretrained Models. - Ekrem Öztürk; Fabio Ferreira; Hadi S. Jomaa; Lars Schmidt-Thieme; Josif Grabocka; Frank Hutter (ICML, 2022)

Supervising the Multi-Fidelity Race of Hyperparameter Configurations. - Martin Wistuba; Arlind Kadra; Josif Grabocka (NeurIPS, 2022)

Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning. - Martin Wistuba; Arlind Kadra; Josif Grabocka (CoRR, 2022)

Deep Ranking Ensembles for Hyperparameter Optimization. - Abdus Salam Khazi; Sebastian Pineda-Arango; Josif Grabocka (ICLR, 2023)

Gray-Box Gaussian Processes for Automated Reinforcement Learning. - Gresa Shala; André Biedenkapp; Frank Hutter; Josif Grabocka (ICLR, 2023)

Transfer NAS with Meta-learned Bayesian Surrogates. - Gresa Shala; Thomas Elsken; Frank Hutter; Josif Grabocka (ICLR, 2023)

Deep Pipeline Embeddings for AutoML. - Sebastian Pineda-Arango; Josif Grabocka (KDD, 2023)

Scaling Laws for Hyperparameter Optimization. - Arlind Kadra; Maciej Janowski; Martin Wistuba; Josif Grabocka (NeurIPS, 2023)

Deep Power Laws for Hyperparameter Optimization. - Arlind Kadra; Maciej Janowski; Martin Wistuba; Josif Grabocka (CoRR, 2023)

Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks. - Mofassir ul Islam Arif; Mohsan Jameel; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2023)

Breaking the Paradox of Explainable Deep Learning. - Arlind Kadra; Sebastian Pineda-Arango; Josif Grabocka (CoRR, 2023)

HPO-RL-Bench: A Zero-Cost Benchmark for HPO in Reinforcement Learning. - Gresa Shala; Sebastian Pineda-Arango; André Biedenkapp; Frank Hutter; Josif Grabocka (AutoML, 2024)

Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How. - Sebastian Pineda-Arango; Fabio Ferreira; Arlind Kadra; Frank Hutter; Josif Grabocka (ICLR, 2024)

Interpretable Mesomorphic Networks for Tabular Data. - Arlind Kadra; Sebastian Pineda-Arango; Josif Grabocka (NeurIPS, 2024)

Tabular Data: Is Attention All You Need? - Guri Zabërgja; Arlind Kadra; Josif Grabocka (CoRR, 2024)

Hierarchical Transformers are Efficient Meta-Reinforcement Learners. - Gresa Shala; André Biedenkapp; Josif Grabocka (CoRR, 2024)

Dynamic Post-Hoc Neural Ensemblers. - Sebastian Pineda-Arango; Maciej Janowski; Lennart Purucker; Arber Zela; Frank Hutter; Josif Grabocka (CoRR, 2024)

Lightweight Correlation-Aware Table Compression. - Mihail Stoian; Alexander van Renen; Jan Kobiolka; Ping-Lin Kuo; Josif Grabocka; Andreas Kipf (CoRR, 2024)

Ensembling Finetuned Language Models for Text Classification. - Sebastian Pineda-Arango; Maciej Janowski; Lennart Purucker; Arber Zela; Frank Hutter; Josif Grabocka (CoRR, 2024)

Warmstarting for Scaling Language Models. - Neeratyoy Mallik; Maciej Janowski; Johannes Hog; Herilalaina Rakotoarison; Aaron Klein; Josif Grabocka; Frank Hutter (CoRR, 2024)

Virtual: Compressing Data Lake Files. - Mihail Stoian; Alexander van Renen; Jan Kobiolka; Ping-Lin Kuo; Andreas Zimmerer; Josif Grabocka; Andreas Kipf (EDBT, 2025)

Efficient Cross-Episode Meta-RL. - Gresa Shala; André Biedenkapp; Pierre Krack; Florian Walter; Josif Grabocka (ICLR, 2025)

Multi-objective Differentiable Neural Architecture Search. - Rhea Sanjay Sukthanker; Arber Zela; Benedikt Staffler; Samuel Dooley; Josif Grabocka; Frank Hutter (ICLR, 2025)