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ORCIDVijaya Krishna Yalavarthi
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Journal Articles
- 2018
[j1]Xiangyu Ke
, Michelle Teo, Arijit Khan, Vijaya Krishna Yalavarthi:
A Demonstration of PERC: Probabilistic Entity Resolution With Crowd Errors. Proc. VLDB Endow. 11(12): 1922-1925 (2018)
Conference and Workshop Papers
- 2025
- [c13]Nourhan Ahmed, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme:
Motif-aware Graph Neural Networks for Networked Time Series Imputation. AAAI 2025: 11409-11417 - [c12]Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme:
Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows. AAAI 2025: 21877-21885 - [c11]Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz, Maximilian Stubbemann, Stefan Born, Lars Schmidt-Thieme:
Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs. ICLR 2025 - 2024
- [c10]Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Scholz, Nourhan Ahmed, Johannes Burchert, Shayan Jawed, Stefan Born, Lars Schmidt-Thieme:
GraFITi: Graphs for Forecasting Irregularly Sampled Time Series. AAAI 2024: 16255-16263 - [c9]Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme:
Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting. ECML/PKDD (4) 2024: 421-436 - 2023
- [c8]Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme:
Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series. IEEE Big Data 2023: 986-995 - [c7]Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme:
Forecasting Early with Meta Learning. IJCNN 2023: 1-8 - 2022
- [c6]Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme:
DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series. DSAA 2022: 1-10 - [c5]Tolga Akar, Thorben Werner, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme:
Open Set Recognition for Time Series Classification. PAKDD (2) 2022: 354-366 - 2019
- [c4]Vijaya Krishna Yalavarthi, Josif Grabocka, Hareesh Mandalapu, Lars Schmidt-Thieme:
Gait Verification using Deep Learning with a Pairwise Loss. BIOSIG 2019: 141-152 - 2018
- [c3]Vijaya Krishna Yalavarthi, Arijit Khan:
Steering Top-k Influencers in Dynamic Graphs via Local Updates. IEEE BigData 2018: 576-583 - 2017
- [c2]Vijaya Krishna Yalavarthi, Xiangyu Ke, Arijit Khan:
Select Your Questions Wisely: For Entity Resolution With Crowd Errors. CIKM 2017: 317-326 - 2016
- [c1]Meng Joo Er, Vijaya Krishna Yalavarthi, Ning Wang, Rajasekar Venkatesan:
A Novel Incremental Class Learning Technique for Multi-class Classification. ISNN 2016: 474-481
Informal and Other Publications
- 2025
- [i13]Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz, Maximilian Stubbemann, Stefan Born, Lars Schmidt-Thieme:
Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs. CoRR abs/2502.07489 (2025) - [i12]Thorben Werner, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi:
The Role of Active Learning in Modern Machine Learning. CoRR abs/2508.00586 (2025) - [i11]Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Jonas Sonntag, Maximilian Stubbemann, Lars Schmidt-Thieme:
TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression. CoRR abs/2508.17056 (2025) - 2024
- [i10]Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme:
Probabilistic Forecasting of Irregular Time Series via Conditional Flows. CoRR abs/2402.06293 (2024) - [i9]Johannes Burchert, Thorben Werner, Vijaya Krishna Yalavarthi, Diego Coello de Portugal, Maximilian Stubbemann, Lars Schmidt-Thieme:
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training. CoRR abs/2404.06966 (2024) - [i8]Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme:
Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting. CoRR abs/2405.03582 (2024) - [i7]Vijaya Krishna Yalavarthi, Randolf Scholz, Kiran Madhusudhanan, Stefan Born, Lars Schmidt-Thieme:
Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting. CoRR abs/2406.07246 (2024) - 2023
- [i6]Vijaya Krishna Yalavarthi, Kiran Madusudanan, Randolf Scholz, Nourhan Ahmed, Johannes Burchert, Shayan Jawed, Stefan Born, Lars Schmidt-Thieme:
Forecasting Irregularly Sampled Time Series using Graphs. CoRR abs/2305.12932 (2023) - [i5]Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme:
Forecasting Early with Meta Learning. CoRR abs/2307.09796 (2023) - 2022
- [i4]Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme:
DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series. CoRR abs/2208.11374 (2022) - [i3]Vijaya Krishna Yalavarthi, Johannes Burchert, Lars Schmidt-Thieme:
Tripletformer for Probabilistic Interpolation of Asynchronous Time Series. CoRR abs/2210.02091 (2022) - 2018
- [i2]Vijaya Krishna Yalavarthi, Arijit Khan:
Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach. CoRR abs/1802.00574 (2018) - 2017
- [i1]Vijaya Krishna Yalavarthi, Xiangyu Ke, Arijit Khan:
Probabilistic Entity Resolution with Imperfect Crowd. CoRR abs/1701.08288 (2017)
Coauthor Index
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