Learning Surrogate Losses. - Josif Grabocka; Randolf Scholz; Lars Schmidt-Thieme (CoRR, 2019)

Chameleon: Learning Model Initializations Across Tasks With Different Schemas. - Lukas Brinkmeyer; Rafael Rêgo Drumond; Randolf Scholz; Josif Grabocka; Lars Schmidt-Thieme (CoRR, 2019)

Improving Sample Efficiency with Normalized RBF Kernels. - Sebastian Pineda-Arango; David Obando-Paniagua; Alperen Dedeoglu; Philip Kurzendörfer; Friedemann Schestag; Randolf Scholz (CoRR, 2020)

Multi-script Handwritten Digit Recognition Using Multi-task Learning. - Mesay Samuel Gondere; Lars Schmidt-Thieme; Durga Prasad Sharma; Randolf Scholz (CoRR, 2021)

Deep Metric Learning for Ground Images. - Raaghav Radhakrishnan; Jan Fabian Schmid; Randolf Scholz; Lars Schmidt-Thieme (CoRR, 2021)

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)

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)

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)

Learning Set Embeddings for Fashion Compatibility Recommendation. - Indra Firmansyah; Randolf Scholz; Adrian Nahmendorff; Ngoc Son Le; Shereen Elsayed; Lars Schmidt-Thieme (SURE@RecSys, 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)

Probabilistic Forecasting of Irregular Time Series via Conditional Flows. - Vijaya Krishna Yalavarthi; Randolf Scholz; Stefan Born; 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)

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)