Randolf Scholz

According to our database, Randolf Scholz authored at least 15 papers between 2019 and 2025.

Collaborative distances:

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

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

Proceedings of the Thirteenth International Conference on Learning Representations, 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

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.

Vijaya Krishna Yalavarthi, Randolf Scholz, Kiran Madhusudhanan, Stefan Born, Lars Schmidt-Thieme

CoRR, 2024

Probabilistic Forecasting of Irregular Time Series via Conditional Flows.

Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme

CoRR, 2024

A workflow management system for reproducible and interoperable high-throughput self-driving experiments.

Federico Mione, Lucas Kaspersetz, Martin F. Luna, Judit Aizpuru, Randolf Scholz, Maxim Borisyak, Annina Karolin Kemmer, Marie-Therese Schermeyer, Ernesto C. Martínez, Peter Neubauer, Mariano Nicolás Cruz Bournazou

Comput. Chem. Eng., 2024

Learning Set Embeddings for Fashion Compatibility Recommendation.

Indra Firmansyah, Randolf Scholz, Adrian Nahmendorff, Ngoc Son Le, Shereen Elsayed, Lars Schmidt-Thieme

Proceedings of the Workshop on Strategic and Utility-aware REcommendations (SURE 2024) co-located with 18th ACM Conference on Recommender Systems (RecSys 2024), 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 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

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

When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development.

Nghia Duong-Trung, Stefan Born, Jong Woo Kim, Marie-Therese Schermeyer, Katharina Paulick, Maxim Borisyak, Ernesto C. Martínez, Mariano Nicolás Cruz Bournazou, Thorben Werner, Randolf Scholz, Lars Schmidt-Thieme, Peter Neubauer

CoRR, 2022

2021

Deep Metric Learning for Ground Images.

Raaghav Radhakrishnan, Jan Fabian Schmid, Randolf Scholz, Lars Schmidt-Thieme

CoRR, 2021

Multi-script Handwritten Digit Recognition Using Multi-task Learning.

Mesay Samuel Gondere, Lars Schmidt-Thieme, Durga Prasad Sharma, Randolf Scholz

CoRR, 2021

2020

Improving Sample Efficiency with Normalized RBF Kernels.

Sebastian Pineda-Arango, David Obando-Paniagua, Alperen Dedeoglu, Philip Kurzendörfer, Friedemann Schestag, Randolf Scholz

CoRR, 2020

2019

Chameleon: Learning Model Initializations Across Tasks With Different Schemas.

Lukas Brinkmeyer, Rafael Rêgo Drumond, Randolf Scholz, Josif Grabocka, Lars Schmidt-Thieme

CoRR, 2019

Learning Surrogate Losses.

Josif Grabocka, Randolf Scholz, Lars Schmidt-Thieme

CoRR, 2019