Kiran Madhusudhanan

According to our database, Kiran Madhusudhanan authored at least 15 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased Is Time Series Forecasting?

Ibram Abdelmalak, Kiran Madhusudhanan, Jungmin Choi, Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2026

2025

Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?

Ibram Abdelmalak, Kiran Madhusudhanan, Jungmin Choi, Maximilian Stubbemann, Lars Schmidt-Thieme

CoRR, February, 2025

TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression.

Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Jonas Sonntag, Maximilian Stubbemann, Lars Schmidt-Thieme

Proceedings of the ECAI 2025 - 28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy, 2025

2024

Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecasting.

Dona Arabi, Jafar Bakhshaliyev, Ayse Coskuner, Kiran Madhusudhanan, Kami Serdar Uckardes

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

Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting.

Kiran Madhusudhanan, Shayan Jawed, Lars Schmidt-Thieme

CoRR, 2024

Hyperparameter Tuning MLP's for Probabilistic Time Series Forecasting.

Kiran Madhusudhanan, Shayan Jawed, Lars Schmidt-Thieme

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2024

ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing.

Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann, Lars Schmidt-Thieme

Proceedings of the IEEE International Conference on Big Data, 2024

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

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

2023

Forecasting Early with Meta Learning.

Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme

Proceedings of the International Joint Conference on Neural Networks, 2023

2022

Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series.

Nghia Duong-Trung, Stefan Born, Kiran Madhusudhanan, Randolf Scholz, Johannes Burchert, Danh Le Phuoc, Lars Schmidt-Thieme

CoRR, 2022

A.I. and Data-Driven Mobility at Volkswagen Financial Services AG.

Shayan Jawed, Mofassir ul Islam Arif, Ahmed Rashed, Kiran Madhusudhanan, Shereen Elsayed, Mohsan Jameel, Alexei Volk, Andre Hintsches, Marlies Kornfeld, Katrin Lange, Lars Schmidt-Thieme

CoRR, 2022

U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting.

Kiran Madhusudhanan, Johannes Burchert, Nghia Duong-Trung, Stefan Born, Lars Schmidt-Thieme

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

2021

Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting.

Kiran Madhusudhanan, Johannes Burchert, Nghia Duong-Trung, Stefan Born, Lars Schmidt-Thieme

CoRR, 2021

Multimodal Meta-Learning for Time Series Regression.

Sebastian Pineda-Arango, Felix Heinrich, Kiran Madhusudhanan, Lars Schmidt-Thieme

Proceedings of the Advanced Analytics and Learning on Temporal Data, 2021