Josif Grabocka

88 papers A* 19A 2B 3Journal 47Unranked 17

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	End-to-End Compression for Tabular Foundation Models. CoRR Guri Zabërgja, Rafiq Kamel, Arlind Kadra, Christian M. M. Frey, Josif Grabocka
2026	J	`jnl`	Learning to Order: Task Sequencing as In-Context Optimization. CoRR Jan Kobiolka, Christian M. M. Frey, Arlind Kadra, Gresa Shala, Josif Grabocka
2026	J	`jnl`	POP: Prior-fitted Optimizer Policies. CoRR Jan Kobiolka, Christian M. M. Frey, Gresa Shala, Arlind Kadra, Erind Bedalli, Josif Grabocka
2025	A*	`conf`	Efficient Cross-Episode Meta-RL. ICLR Gresa Shala, André Biedenkapp, Pierre Krack, Florian Walter, Josif Grabocka
2025	A*	`conf`	Multi-objective Differentiable Neural Architecture Search. ICLR Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Samuel Dooley, Josif Grabocka, Frank Hutter
2025	—	`conf`	Regularized Neural Ensemblers. AutoML Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka
2025	B	`conf`	Virtual: Compressing Data Lake Files. EDBT Mihail Stoian, Alexander van Renen, Jan Kobiolka, Ping-Lin Kuo, Andreas Zimmerer, Josif Grabocka, Andreas Kipf
2025	J	`jnl`	Zhyper: Factorized Hypernetworks for Conditioned LLM Fine-Tuning. CoRR M. H. I. Abdalla, Zhipin Wang, Christian Frey, Steffen Eger, Josif Grabocka
2024	J	`jnl`	Dynamic Post-Hoc Neural Ensemblers. CoRR Sebastian Pineda-Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka
2024	J	`jnl`	Ensembling Finetuned Language Models for Text Classification. CoRR Sebastian Pineda-Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka
2024	—	`conf`	HPO-RL-Bench: A Zero-Cost Benchmark for HPO in Reinforcement Learning. AutoML Gresa Shala, Sebastian Pineda-Arango, André Biedenkapp, Frank Hutter, Josif Grabocka
2024	J	`jnl`	Hierarchical Transformers are Efficient Meta-Reinforcement Learners. CoRR Gresa Shala, André Biedenkapp, Josif Grabocka
2024	A*	`conf`	Interpretable Mesomorphic Networks for Tabular Data. NeurIPS Arlind Kadra, Sebastian Pineda-Arango, Josif Grabocka
2024	J	`jnl`	Lightweight Correlation-Aware Table Compression. CoRR Mihail Stoian, Alexander van Renen, Jan Kobiolka, Ping-Lin Kuo, Josif Grabocka, Andreas Kipf
2024	J	`jnl`	Multi-objective Differentiable Neural Architecture Search. CoRR Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Samuel Dooley, Josif Grabocka, Frank Hutter
2024	A*	`conf`	Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How. ICLR Sebastian Pineda-Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka
2024	J	`jnl`	Tabular Data: Is Attention All You Need? CoRR Guri Zabërgja, Arlind Kadra, Josif Grabocka
2024	J	`jnl`	Warmstarting for Scaling Language Models. CoRR Neeratyoy Mallik, Maciej Janowski, Johannes Hog, Herilalaina Rakotoarison, Aaron Klein, Josif Grabocka, Frank Hutter
2023	J	`jnl`	Breaking the Paradox of Explainable Deep Learning. CoRR Arlind Kadra, Sebastian Pineda-Arango, Josif Grabocka
2023	A*	`conf`	Deep Pipeline Embeddings for AutoML. KDD Sebastian Pineda-Arango, Josif Grabocka
2023	J	`jnl`	Deep Pipeline Embeddings for AutoML. CoRR Sebastian Pineda-Arango, Josif Grabocka
2023	J	`jnl`	Deep Power Laws for Hyperparameter Optimization. CoRR Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka
2023	A*	`conf`	Deep Ranking Ensembles for Hyperparameter Optimization. ICLR Abdus Salam Khazi, Sebastian Pineda-Arango, Josif Grabocka
2023	J	`jnl`	Deep Ranking Ensembles for Hyperparameter Optimization. CoRR Abdus Salam Khazi, Sebastian Pineda-Arango, Josif Grabocka
2023	A*	`conf`	Gray-Box Gaussian Processes for Automated Reinforcement Learning. ICLR Gresa Shala, André Biedenkapp, Frank Hutter, Josif Grabocka
2023	J	`jnl`	Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks. CoRR Mofassir ul Islam Arif, Mohsan Jameel, Josif Grabocka, Lars Schmidt-Thieme
2023	J	`jnl`	Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How. CoRR Sebastian Pineda-Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka
2023	A*	`conf`	Scaling Laws for Hyperparameter Optimization. NeurIPS Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka
2023	A*	`conf`	Transfer NAS with Meta-learned Bayesian Surrogates. ICLR Gresa Shala, Thomas Elsken, Frank Hutter, Josif Grabocka
2022	J	`jnl`	Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning. CoRR Martin Wistuba, Arlind Kadra, Josif Grabocka
2022	A*	`conf`	Supervising the Multi-Fidelity Race of Hyperparameter Configurations. NeurIPS Martin Wistuba, Arlind Kadra, Josif Grabocka
2022	A*	`conf`	Transformers Can Do Bayesian Inference. ICLR Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter
2022	J	`jnl`	Zero-Shot AutoML with Pretrained Models. CoRR Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter
2022	A*	`conf`	Zero-shot AutoML with Pretrained Models. ICML Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter
2021	A*	`conf`	A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning. SIGIR Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme
2021	J	`jnl`	Dataset2Vec: learning dataset meta-features. Data Min. Knowl. Discov. Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka
2021	J	`jnl`	Efficient Multi-Objective Optimization for Deep Learning. CoRR Michael Ruchte, Josif Grabocka
2021	A*	`conf`	Few-Shot Bayesian Optimization with Deep Kernel Surrogates. ICLR Martin Wistuba, Josif Grabocka
2021	J	`jnl`	Few-Shot Bayesian Optimization with Deep Kernel Surrogates. CoRR Martin Wistuba, Josif Grabocka
2021	—	`conf`	HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. NeurIPS Datasets and Benchmarks Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka
2021	J	`jnl`	HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. CoRR Sebastian Pineda-Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka
2021	J	`jnl`	Hyperparameter Optimization with Differentiable Metafeatures. CoRR Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka
2021	—	`conf`	Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets. ECML/PKDD (1) Shayan Jawed, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka
2021	J	`jnl`	Multi-task problems are not multi-objective. CoRR Michael Ruchte, Josif Grabocka
2021	J	`jnl`	Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data. CoRR Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
2021	A*	`conf`	Scalable Pareto Front Approximation for Deep Multi-Objective Learning. ICDM Michael Ruchte, Josif Grabocka
2021	J	`jnl`	Transformers Can Do Bayesian Inference. CoRR Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter
2021	A*	`conf`	Well-tuned Simple Nets Excel on Tabular Datasets. NeurIPS Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
2020	A	`conf`	HIDRA: Head Initialization across Dynamic targets for Robust Architectures. SDM Rafael Rêgo Drumond, Lukas Brinkmeyer, Josif Grabocka, Lars Schmidt-Thieme
2020	—	`conf`	Phantom Embeddings: Using Embeddings Space for Model Regularization in Deep Neural Networks. LWDA Mofassir ul Islam Arif, Mohsan Jameel, Josif Grabocka, Lars Schmidt-Thieme
2020	—	`conf`	Self-supervised Learning for Semi-supervised Time Series Classification. PAKDD (1) Shayan Jawed, Josif Grabocka, Lars Schmidt-Thieme
2019	—	`conf`	A Deep Multi-task Approach for Residual Value Forecasting. ECML/PKDD (3) Ahmed Rashed, Shayan Jawed, Jens Rehberg, Josif Grabocka, Lars Schmidt-Thieme, Andre Hintsches
2019	B	`conf`	A Hybrid Convolutional Approach for Parking Availability Prediction. IJCNN Hadi Samer Jomaa, Josif Grabocka, Lars Schmidt-Thieme, Alexander Borek
2019	A	`conf`	Attribute-aware non-linear co-embeddings of graph features. RecSys Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme
2019	J	`jnl`	Chameleon: Learning Model Initializations Across Tasks With Different Schemas. CoRR Lukas Brinkmeyer, Rafael Rêgo Drumond, Randolf Scholz, Josif Grabocka, Lars Schmidt-Thieme
2019	J	`jnl`	Data-Driven Vehicle Trajectory Forecasting. CoRR Shayan Jawed, Eya Boumaiza, Josif Grabocka, Lars Schmidt-Thieme
2019	J	`jnl`	Dataset2Vec: Learning Dataset Meta-Features. CoRR Hadi S. Jomaa, Josif Grabocka, Lars Schmidt-Thieme
2019	—	`conf`	Gait Verification using Deep Learning with a Pairwise Loss. BIOSIG Vijaya Krishna Yalavarthi, Josif Grabocka, Hareesh Mandalapu, Lars Schmidt-Thieme
2019	J	`jnl`	HIDRA: Head Initialization across Dynamic targets for Robust Architectures. CoRR Rafael Rêgo Drumond, Lukas Brinkmeyer, Josif Grabocka, Lars Schmidt-Thieme
2019	J	`jnl`	Hyp-RL : Hyperparameter Optimization by Reinforcement Learning. CoRR Hadi S. Jomaa, Josif Grabocka, Lars Schmidt-Thieme
2019	J	`jnl`	In Hindsight: A Smooth Reward for Steady Exploration. CoRR Hadi S. Jomaa, Josif Grabocka, Lars Schmidt-Thieme
2019	J	`jnl`	Learning Surrogate Losses. CoRR Josif Grabocka, Randolf Scholz, Lars Schmidt-Thieme
2019	—	`conf`	Multi-Label Network Classification via Weighted Personalized Factorizations. ICAART (2) Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme
2019	J	`jnl`	Multi-Label Network Classification via Weighted Personalized Factorizations. CoRR Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme
2019	A*	`conf`	Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings. KDD Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme
2019	—	`conf`	Ring-Star: A Sparse Topology for Faster Model Averaging in Decentralized Parallel SGD. PKDD/ECML Workshops (1) Mohsan Jameel, Josif Grabocka, Mofassir ul Islam Arif, Lars Schmidt-Thieme
2019	—	`conf`	Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights. ICAART (Revised Selected Papers) Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme
2018	J	`jnl`	NeuralWarp: Time-Series Similarity with Warping Networks. CoRR Josif Grabocka, Lars Schmidt-Thieme
2017	J	`jnl`	Channel masking for multivariate time series shapelets. CoRR Dripta S. Raychaudhuri, Josif Grabocka, Lars Schmidt-Thieme
2017	—	`conf`	Personalized Deep Learning for Tag Recommendation. PAKDD (1) Hanh T. H. Nguyen, Martin Wistuba, Josif Grabocka, Lucas Rêgo Drumond, Lars Schmidt-Thieme
2016	J	`jnl`	Fast classification of univariate and multivariate time series through shapelet discovery. Knowl. Inf. Syst. Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
2016	J	`jnl`	Latent Time-Series Motifs. ACM Trans. Knowl. Discov. Data Josif Grabocka, Nicolas Schilling, Lars Schmidt-Thieme
2016	—	`conf`	Learning DTW-Shapelets for Time-Series Classification. CODS Mit Shah, Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme
2015	J	`jnl`	Learning Through Non-linearly Supervised Dimensionality Reduction. Trans. Large Scale Data Knowl. Centered Syst. Josif Grabocka, Lars Schmidt-Thieme
2015	J	`jnl`	Optimal Time-Series Motifs. CoRR Josif Grabocka, Nicolas Schilling, Lars Schmidt-Thieme
2015	J	`jnl`	Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials. IEEE Trans. Knowl. Data Eng. Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
2015	J	`jnl`	Scalable Discovery of Time-Series Shapelets. CoRR Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
2015	J	`jnl`	Ultra-Fast Shapelets for Time Series Classification. CoRR Martin Wistuba, Josif Grabocka, Lars Schmidt-Thieme
2014	J	`jnl`	Invariant time-series factorization. Data Min. Knowl. Discov. Josif Grabocka, Lars Schmidt-Thieme
2014	A*	`conf`	Learning time-series shapelets. KDD Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme
2014	—	`conf`	Realistic optimal policies for energy-efficient train driving. ITSC Josif Grabocka, Alexandros Dalkalitsis, Athanasios Lois, Evangelos Katsaros, Lars Schmidt-Thieme
2014	—	`conf`	Supervised Nonlinear Factorizations Excel In Semi-supervised Regression. PAKDD (1) Josif Grabocka, Erind Bedalli, Lars Schmidt-Thieme
2013	J	`jnl`	Invariant Factorization Of Time-Series. CoRR Josif Grabocka, Lars Schmidt-Thieme
2013	B	`conf`	Supervised Dimensionality Reduction via Nonlinear Target Estimation. DaWaK Josif Grabocka, Lucas Drumond, Lars Schmidt-Thieme
2013	J	`jnl`	Time-Series Classification Through Histograms of Symbolic Polynomials. CoRR Josif Grabocka, Martin Wistuba, Lars Schmidt-Thieme
2012	A*	`conf`	Classification of Sparse Time Series via Supervised Matrix Factorization. AAAI Josif Grabocka, Alexandros Nanopoulos, Lars Schmidt-Thieme
2012	—	`conf`	Efficient Classification of Long Time-Series. ICT Innovations Josif Grabocka, Erind Bedalli, Lars Schmidt-Thieme
2012	—	`conf`	Invariant Time-Series Classification. ECML/PKDD (2) Josif Grabocka, Alexandros Nanopoulos, Lars Schmidt-Thieme