| GraFITi: Graphs for Forecasting Irregularly Sampled Time Series VK Yalavarthi, K Madhusudhanan, R Scholz, N Ahmed, J Burchert, ... Proceedings of the AAAI Conference on Artificial Intelligence 38 (15), 16255 …, 2024 | 34 | 2024 |
| Steering top-k influencers in dynamic graphs via local updates VK Yalavarthi, A Khan 2018 IEEE International Conference on Big Data (Big Data), 576-583, 2018 | 21* | 2018 |
| Select Your Questions Wisely: For Entity Resolution With Crowd Errors VK Yalavarthi, X Ke, A Khan ACM on Conference on Information and Knowledge Management, 317-326, 2017 | 17* | 2017 |
| Tripletformer for probabilistic interpolation of irregularly sampled time series VK Yalavarthi, J Burchert, L Schmidt-Thieme 2023 IEEE International Conference on Big Data (BigData), 986-995, 2023 | 10* | 2023 |
| Open set recognition for time series classification T Akar, T Werner, VK Yalavarthi, L Schmidt-Thieme Pacific-Asia Conference on Knowledge Discovery and Data Mining, 354-366, 2022 | 8 | 2022 |
| DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series VK Yalavarthi, J Burchert, L Schmidt-Thieme 2022 IEEE 9th International Conference on Data Science and Advanced …, 2022 | 8 | 2022 |
| A demonstration of PERC: probabilistic entity resolution with crowd errors X Ke, M Teo, A Khan, VK Yalavarthi Proceedings of the VLDB Endowment 11 (12), 1922-1925, 2018 | 7 | 2018 |
| Are eeg sequences time series? eeg classification with time series models and joint subject training J Burchert, T Werner, VK Yalavarthi, DC de Portugal, M Stubbemann, ... arXiv preprint arXiv:2404.06966, 2024 | 5 | 2024 |
| A novel incremental class learning technique for multi-class classification MJ Er, VK Yalavarthi, N Wang, R Venkatesan International Symposium on Neural Networks, 474-481, 2016 | 4 | 2016 |
| Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows VK Yalavarthi, R Scholz, S Born, L Schmidt-Thieme Proceedings of the AAAI Conference on Artificial Intelligence 39 (20), 21877 …, 2025 | 3* | 2025 |
| Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting C Klötergens, VK Yalavarthi, M Stubbemann, L Schmidt-Thieme Joint European Conference on Machine Learning and Knowledge Discovery in …, 2024 | 3 | 2024 |
| Gait verification using deep learning with a pairwise loss VK Yalavarthi, J Grabocka, H Mandalapu, L Schmidt-Thieme 2019 International Conference of the Biometrics Special Interest Group …, 2019 | 2 | 2019 |
| Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs C Klötergens, VK Yalavarthi, R Scholz, M Stubbemann, S Born, ... The Thirteenth International Conference on Learning Representations, 2025 | 1 | 2025 |
| Forecasting Early with Meta Learning S Jawed, K Madhusudhanan, VK Yalavarthi, L Schmidt-Thieme 2023 International Joint Conference on Neural Networks (IJCNN), 1-8, 2023 | 1 | 2023 |
| TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression K Madhusudhanan, VK Yalavarthi, J Sonntag, M Stubbemann, ... arXiv preprint arXiv:2508.17056, 2025 | | 2025 |
| The Role of Active Learning in Modern Machine Learning T Werner, L Schmidt-Thieme, VK Yalavarthi arXiv preprint arXiv:2508.00586, 2025 | | 2025 |
| Motif-aware Graph Neural Networks for Networked Time Series Imputation N Ahmed, VK Yalavarthi, L Schmidt-Thieme Proceedings of the AAAI Conference on Artificial Intelligence 39 (11), 11409 …, 2025 | | 2025 |
| Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting V Krishna Yalavarthi, R Scholz, K Madhusudhanan, S Born, ... arXiv e-prints, arXiv: 2406.07246, 2024 | | 2024 |
| Open Set Recognition in Semantic Segmentation R Raghuraman, L Schmidt-Thieme, VK Yalavarthi, S Raafatnia | | 2020 |
| A hybrid machine learning technique for complex non-stationary classification problems VK Yalavarthi | | 2018 |
Lars Schmidt-ThiemeUniversity of Hildesheim, GermanyVerified email at ismll.uni-hildesheim.de
