Josif Grabocka

发表

Optimal Time-Series Motifs pdf

Lars Schmidt-Thieme, Josif Grabocka, Nicolas Schilling, 2015, ArXiv.

Classification of Sparse Time Series via Supervised Matrix Factorization

Lars Schmidt-Thieme, Alexandros Nanopoulos, Josif Grabocka, 2012, AAAI.

Personalized Deep Learning for Tag Recommendation

Lars Schmidt-Thieme, Josif Grabocka, Lucas Drumond, 2017, PAKDD.

Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings

Lars Schmidt-Thieme, Josif Grabocka, Ahmed Rashed, 2019, KDD.

Channel masking for multivariate time series shapelets pdf

Lars Schmidt-Thieme, Josif Grabocka, Dripta S. Raychaudhuri, 2017, ArXiv.

Learning DTW-Shapelets for Time-Series Classification

Lars Schmidt-Thieme, Josif Grabocka, Nicolas Schilling, 2016, CODS.

Supervised Dimensionality Reduction via Nonlinear Target Estimation

Lars Schmidt-Thieme, Josif Grabocka, Lucas Drumond, 2013, DaWaK.

Realistic optimal policies for energy-efficient train driving

Lars Schmidt-Thieme, Josif Grabocka, Athanasios Lois, 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

HIDRA: Head Initialization across Dynamic targets for Robust Architectures

Lars Schmidt-Thieme, Josif Grabocka, Rafael Rego Drumond, 2020, SDM.

Phantom Embeddings: Using Embeddings Space for Model Regularization in Deep Neural Networks

Lars Schmidt-Thieme, Josif Grabocka, Mohsan Jameel, 2020, LWDA.

Invariant time-series factorization

Lars Schmidt-Thieme, Josif Grabocka, L. Schmidt-Thieme, 2014, Data Mining and Knowledge Discovery.

Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights

Lars Schmidt-Thieme, Josif Grabocka, Ahmed Rashed, 2019, ICAART.

Data-Driven Vehicle Trajectory Forecasting pdf

Lars Schmidt-Thieme, Josif Grabocka, Shayan Jawed, 2019, ArXiv.

In Hindsight: A Smooth Reward for Steady Exploration pdf

Lars Schmidt-Thieme, Josif Grabocka, Hadi S. Jomaa, 2019, ArXiv.

Supervised Nonlinear Factorizations Excel In Semi-supervised Regression

Lars Schmidt-Thieme, Josif Grabocka, Erind Bedalli, 2014, PAKDD.

Learning time-series shapelets

Lars Schmidt-Thieme, Josif Grabocka, Nicolas Schilling, 2014, KDD.

Chameleon: Learning Model Initializations Across Tasks With Different Schemas pdf

Lars Schmidt-Thieme, Josif Grabocka, Randolf Scholz, 2019, ArXiv.

Latent Time-Series Motifs

Lars Schmidt-Thieme, Josif Grabocka, Nicolas Schilling, 2016, ACM Trans. Knowl. Discov. Data.

Dataset2Vec: learning dataset meta-features pdf

Lars Schmidt-Thieme, Josif Grabocka, Hadi S. Jomaa, 2019, Data Mining and Knowledge Discovery.

Self-supervised Learning for Semi-supervised Time Series Classification

Lars Schmidt-Thieme, Josif Grabocka, Shayan Jawed, 2020, PAKDD.

Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials

Lars Schmidt-Thieme, Josif Grabocka, Martin Wistuba, 2015, IEEE Transactions on Knowledge and Data Engineering.

Ultra-Fast Shapelets for Time Series Classification pdf

Lars Schmidt-Thieme, Josif Grabocka, Martin Wistuba, 2015, ArXiv.

A Hybrid Convolutional Approach for Parking Availability Prediction

Lars Schmidt-Thieme, Josif Grabocka, Alexander Borek, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

Few-Shot Bayesian Optimization with Deep Kernel Surrogates pdf

Josif Grabocka, Martin Wistuba, Josif Grabocka, 2021, ICLR.

Attribute-aware non-linear co-embeddings of graph features

Lars Schmidt-Thieme, Josif Grabocka, Ahmed Rashed, 2019, RecSys.

NeuralWarp: Time-Series Similarity with Warping Networks pdf

Lars Schmidt-Thieme, Josif Grabocka, L. Schmidt-Thieme, 2018, ArXiv.

Efficient Classification of Long Time-Series

Lars Schmidt-Thieme, Josif Grabocka, Erind Bedalli, 2012, ICT Innovations.

Hyp-RL : Hyperparameter Optimization by Reinforcement Learning pdf

Lars Schmidt-Thieme, Josif Grabocka, Hadi S. Jomaa, 2019, ArXiv.

Multi-Label Network Classification via Weighted Personalized Factorizations pdf

Lars Schmidt-Thieme, Josif Grabocka, Ahmed Rashed, 2019, ICAART.

Scalable Discovery of Time-Series Shapelets pdf

Lars Schmidt-Thieme, Josif Grabocka, Martin Wistuba, 2015, ArXiv.

Gait Verification using Deep Learning with a Pairwise Loss

Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi, Josif Grabocka, 2019, 2019 International Conference of the Biometrics Special Interest Group (BIOSIG).

Learning Through Non-linearly Supervised Dimensionality Reduction

Lars Schmidt-Thieme, Josif Grabocka, L. Schmidt-Thieme, 2015, Trans. Large Scale Data Knowl. Centered Syst..

Ring-Star: A Sparse Topology for Faster Model Averaging in Decentralized Parallel SGD

Lars Schmidt-Thieme, Josif Grabocka, Mohsan Jameel, 2019, PKDD/ECML Workshops.

Fast classification of univariate and multivariate time series through shapelet discovery

Lars Schmidt-Thieme, Josif Grabocka, Martin Wistuba, 2016, Knowledge and Information Systems.

NASLib: A Modular and Flexible Neural Architecture Search Library

Josif Grabocka, Frank Hutter, Arber Zela, 2020 .

A Deep Multi-task Approach for Residual Value Forecasting

Lars Schmidt-Thieme, Josif Grabocka, Shayan Jawed, 2019, ECML/PKDD.

Learning Surrogate Losses pdf

Lars Schmidt-Thieme, Josif Grabocka, Randolf Scholz, 2019, ArXiv.

Invariant Time-Series Classification

Lars Schmidt-Thieme, Alexandros Nanopoulos, Josif Grabocka, 2012, ECML/PKDD.

Well-tuned Simple Nets Excel on Tabular Datasets

Josif Grabocka, Marius Lindauer, Frank Hutter, 2021, NeurIPS.

Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data pdf

Josif Grabocka, Marius Lindauer, Frank Hutter, 2021, ArXiv.

HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML pdf

Josif Grabocka, Martin Wistuba, Hadi S. Jomaa, 2021, NeurIPS Datasets and Benchmarks.

Multi-task problems are not multi-objective pdf

Josif Grabocka, Michael Ruchte, Josif Grabocka, 2021, ArXiv.

Transformers Can Do Bayesian Inference pdf

Josif Grabocka, Frank Hutter, Sebastian Pineda Arango, 2021, ArXiv.

Efficient Multi-Objective Optimization for Deep Learning pdf

Josif Grabocka, Michael Ruchte, Josif Grabocka, 2021, ArXiv.

Multi-task Learning Curve Forecasting Across Hyperparameter Configurations and Datasets

Lars Schmidt-Thieme, Josif Grabocka, Shayan Jawed, 2021, ECML/PKDD.

Hyperparameter Optimization with Differentiable Metafeatures pdf

Lars Schmidt-Thieme, Josif Grabocka, Hadi S. Jomaa, 2021, ArXiv.

Scalable Pareto Front Approximation for Deep Multi-Objective Learning

Josif Grabocka, Michael Ruchte, Josif Grabocka, 2021, 2021 IEEE International Conference on Data Mining (ICDM).

A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning

Lars Schmidt-Thieme, Josif Grabocka, Ahmed Rashed, 2021, SIGIR.