Vijaya Krishna Yalavarthi

Randolf Scholz

Maximilian Stubbemann

Stefan Born

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows.

[BibT_eX]

[DOI]

Randolf Scholz

Stefan Born

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

Motif-aware Graph Neural Networks for Networked Time Series Imputation.

[BibT_eX]

[DOI]

Nourhan Ahmed

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting.

[BibT_eX]

[DOI]

CoRR, 2024

Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training.

[BibT_eX]

[DOI]

Thorben Werner

Diego Coello de Portugal

Maximilian Stubbemann

CoRR, 2024

Probabilistic Forecasting of Irregular Time Series via Conditional Flows.

[BibT_eX]

[DOI]

Randolf Scholz

Stefan Born

CoRR, 2024

Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting.

[BibT_eX]

[DOI]

Christian Klötergens

Maximilian Stubbemann

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

GraFITi: Graphs for Forecasting Irregularly Sampled Time Series.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Forecasting Irregularly Sampled Time Series using Graphs.

[BibT_eX]

[DOI]

CoRR, 2023

Forecasting Early with Meta Learning.

[BibT_eX]

[DOI]

Shayan Jawed

Kiran Madhusudhanan

Proceedings of the International Joint Conference on Neural Networks, 2023

Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Big Data, 2023

2022

Tripletformer for Probabilistic Interpolation of Asynchronous Time Series.

[BibT_eX]

[DOI]

CoRR, 2022

Open Set Recognition for Time Series Classification.

[BibT_eX]

[DOI]

Tolga Akar

Thorben Werner

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022

DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series.

[BibT_eX]

[DOI]

Proceedings of the 9th IEEE International Conference on Data Science and Advanced Analytics, 2022

2019

Gait Verification using Deep Learning with a Pairwise Loss.

[BibT_eX]

[DOI]

Josif Grabocka

Hareesh Mandalapu

Proceedings of the 2019 International Conference of the Biometrics Special Interest Group, 2019

2018

A Demonstration of PERC: Probabilistic Entity Resolution With Crowd Errors.

[BibT_eX]

[DOI]

Xiangyu Ke

Michelle Teo

Proc. VLDB Endow., 2018

Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach.

[BibT_eX]

[DOI]

CoRR, 2018

Steering Top-k Influencers in Dynamic Graphs via Local Updates.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017

Probabilistic Entity Resolution with Imperfect Crowd.

[BibT_eX]

[DOI]

Xiangyu Ke

CoRR, 2017

Select Your Questions Wisely: For Entity Resolution With Crowd Errors.

[BibT_eX]

[DOI]

Xiangyu Ke

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

2016

A Novel Incremental Class Learning Technique for Multi-class Classification.

[BibT_eX]

[DOI]

Meng Joo Er