A Novel Incremental Class Learning Technique for Multi-class Classification. - Meng Joo Er; Vijaya Krishna Yalavarthi; Ning Wang 002; Rajasekar Venkatesan (ISNN, 2016)

Select Your Questions Wisely: For Entity Resolution With Crowd Errors. - Vijaya Krishna Yalavarthi; Xiangyu Ke; Arijit Khan 001 (CIKM, 2017)

Probabilistic Entity Resolution with Imperfect Crowd. - Vijaya Krishna Yalavarthi; Xiangyu Ke; Arijit Khan 001 (CoRR, 2017)

Steering Top-k Influencers in Dynamic Graphs via Local Updates. - Vijaya Krishna Yalavarthi; Arijit Khan 001 (IEEE BigData, 2018)

Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach. - Vijaya Krishna Yalavarthi; Arijit Khan 001 (CoRR, 2018)

A Demonstration of PERC: Probabilistic Entity Resolution With Crowd Errors. - Xiangyu Ke; Michelle Teo; Arijit Khan 001; Vijaya Krishna Yalavarthi (Proc. VLDB Endow., 2018)

Gait Verification using Deep Learning with a Pairwise Loss. - Vijaya Krishna Yalavarthi; Josif Grabocka; Hareesh Mandalapu; Lars Schmidt-Thieme (BIOSIG, 2019)

DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series. - Vijaya Krishna Yalavarthi; Johannes Burchert; Lars Schmidt-Thieme (DSAA, 2022)

Open Set Recognition for Time Series Classification. - Tolga Akar; Thorben Werner; Vijaya Krishna Yalavarthi; Lars Schmidt-Thieme (PAKDD (2), 2022)

Tripletformer for Probabilistic Interpolation of Asynchronous Time Series. - Vijaya Krishna Yalavarthi; Johannes Burchert; Lars Schmidt-Thieme (CoRR, 2022)

Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series. - Vijaya Krishna Yalavarthi; Johannes Burchert; Lars Schmidt-Thieme (IEEE Big Data, 2023)

Forecasting Early with Meta Learning. - Shayan Jawed; Kiran Madhusudhanan; Vijaya Krishna Yalavarthi; Lars Schmidt-Thieme (IJCNN, 2023)

Forecasting Irregularly Sampled Time Series using Graphs. - Vijaya Krishna Yalavarthi; Kiran Madusudanan; Randolf Scholz; Nourhan Ahmed; Johannes Burchert; Shayan Jawed; Stefan Born; Lars Schmidt-Thieme (CoRR, 2023)

GraFITi: Graphs for Forecasting Irregularly Sampled Time Series. - Vijaya Krishna Yalavarthi; Kiran Madhusudhanan; Randolf Scholz; Nourhan Ahmed; Johannes Burchert; Shayan Jawed; Stefan Born; Lars Schmidt-Thieme (AAAI, 2024)

Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting. - Christian Klötergens; Vijaya Krishna Yalavarthi; Maximilian Stubbemann; Lars Schmidt-Thieme (ECML/PKDD (4), 2024)

Probabilistic Forecasting of Irregular Time Series via Conditional Flows. - Vijaya Krishna Yalavarthi; Randolf Scholz; Stefan Born; Lars Schmidt-Thieme (CoRR, 2024)

Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training. - Johannes Burchert; Thorben Werner; Vijaya Krishna Yalavarthi; Diego Coello de Portugal; Maximilian Stubbemann; Lars Schmidt-Thieme (CoRR, 2024)

Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting. - Vijaya Krishna Yalavarthi; Randolf Scholz; Kiran Madhusudhanan; Stefan Born; Lars Schmidt-Thieme (CoRR, 2024)

Motif-aware Graph Neural Networks for Networked Time Series Imputation. - Nourhan Ahmed; Vijaya Krishna Yalavarthi; Lars Schmidt-Thieme (AAAI, 2025)

Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows. - Vijaya Krishna Yalavarthi; Randolf Scholz; Stefan Born; Lars Schmidt-Thieme (AAAI, 2025)

Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs. - Christian Klötergens; Vijaya Krishna Yalavarthi; Randolf Scholz; Maximilian Stubbemann; Stefan Born; Lars Schmidt-Thieme (ICLR, 2025)