Josif Grabocka

1.2k citations

17 papers · 451 indexed · 1 hit paper · h-index 7

Co-authors: Lars Schmidt-Thieme Martin Wistuba Nicolas Schilling Ahmed Nabih Zaki Rashed Mit Shah Frank Hutter Arber Zela Marius Lindauer
Topics: Time Series Analysis and Forecasting (7 papers)Music and Audio Processing (6 papers)Complex Systems and Time Series Analysis (4 papers)
Cited by: Signal Processing Artificial Intelligence Economics and Econometrics
Journals: IEEE Transactions on Knowledge and Data Engineering Data Mining and Knowledge Discovery Knowledge and Information Systems
Partner nations: Germany Switzerland India

In The Last Decade

Josif Grabocka

15 papers receiving 443 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Learning time-series shapelets

2014 275 citations Josif Grabocka, Nicolas Schilling et al. profile →

Peers

Countries citing papers authored by Josif Grabocka

Since Specialization

Citations

This map shows the geographic impact of Josif Grabocka's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Josif Grabocka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Josif Grabocka more than expected).

Fields of papers citing papers by Josif Grabocka

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Josif Grabocka. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Josif Grabocka. The network helps show where Josif Grabocka may publish in the future.

Co-authorship network of co-authors of Josif Grabocka

This figure shows the co-authorship network connecting the top 25 collaborators of Josif Grabocka. A scholar is included among the top collaborators of Josif Grabocka based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Josif Grabocka. Josif Grabocka is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

17 of 17 papers shown

#	Work	Indexed citations
1	Deep Pipeline Embeddings for AutoML 2023 ·(unknown),Josif Grabocka	0
2	A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning 2021 ·Ahmed Nabih Zaki Rashed,Josif Grabocka,Lars Schmidt-Thieme	5
3	Classification of Sparse Time Series via Supervised Matrix Factorization 2021 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Josif Grabocka,Αλέξανδρος Νανόπουλος,Lars Schmidt-Thieme	1
4	NASLib: A Modular and Flexible Neural Architecture Search Library 2021 ·(unknown),Arber Zela,(unknown),Josif Grabocka,Frank Hutter	3
5	Well-tuned Simple Nets Excel on Tabular Datasets 2021 ·arXiv (Cornell University) ·(unknown),Marius Lindauer,Frank Hutter,Josif Grabocka	1
6	Gait Verification using Deep Learning with a Pairwise Loss 2019 ·(unknown),Josif Grabocka,(unknown),Lars Schmidt-Thieme	1
7	Attribute-aware non-linear co-embeddings of graph features 2019 ·Ahmed Nabih Zaki Rashed,Josif Grabocka,Lars Schmidt-Thieme	24
8	A Hybrid Convolutional Approach for Parking Availability Prediction 2019 ·(unknown),Josif Grabocka,Lars Schmidt-Thieme,(unknown)	6
9	Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings 2019 ·Ahmed Nabih Zaki Rashed,Josif Grabocka,Lars Schmidt-Thieme	4
10	Multi-Label Network Classification via Weighted Personalized Factorizations 2019 ·Ahmed Nabih Zaki Rashed,Josif Grabocka,Lars Schmidt-Thieme	0
11	Latent Time-Series Motifs 2016 ·ACM Transactions on Knowledge Discovery from Data ·Josif Grabocka,Nicolas Schilling,Lars Schmidt-Thieme	22
12	Learning DTW-Shapelets for Time-Series Classification 2016 ·Mit Shah,Josif Grabocka,Nicolas Schilling,Martin Wistuba,Lars Schmidt-Thieme	33
13	Fast classification of univariate and multivariate time series through shapelet discovery 2015 ·Knowledge and Information Systems ·Josif Grabocka,Martin Wistuba,Lars Schmidt-Thieme	49
14	Invariant time-series factorization 2014 ·Data Mining and Knowledge Discovery ·Josif Grabocka,Lars Schmidt-Thieme	12
15	Realistic optimal policies for energy-efficient train driving 2014 ·Josif Grabocka,(unknown),(unknown),(unknown),Lars Schmidt-Thieme	6
16	Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials 2014 ·IEEE Transactions on Knowledge and Data Engineering ·Josif Grabocka,Martin Wistuba,Lars Schmidt-Thieme	9
17	Learning time-series shapeletsbreakdown → 2014 ·Josif Grabocka,Nicolas Schilling,Martin Wistuba,Lars Schmidt-Thieme	275

About Josif Grabocka

Josif Grabocka is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 17 papers that have together received 451 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (7 papers), Music and Audio Processing (6 papers) and Complex Systems and Time Series Analysis (4 papers). The work is most often cited by research in Signal Processing (336 citations), Artificial Intelligence (289 citations) and Economics and Econometrics (115 citations). Josif Grabocka has collaborated with scholars based in Germany, Switzerland and India. Frequent co-authors include Lars Schmidt-Thieme, Martin Wistuba, Nicolas Schilling, Ahmed Nabih Zaki Rashed, Mit Shah, Frank Hutter, Arber Zela, Marius Lindauer and Αλέξανδρος Νανόπουλος. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Data Mining and Knowledge Discovery and Knowledge and Information Systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact