Exploring the Influence of Data Aggregation in Parking Prediction. - Shereen Elsayed; Daniela Thyssens; Shabanaz Chamurally; Arslan Tariq; Hadi Samer Jomaa (DEXA Workshops, 2020)

Do We Really Need Deep Learning Models for Time Series Forecasting? - Shereen Elsayed; Daniela Thyssens; Ahmed Rashed; Lars Schmidt-Thieme; Hadi Samer Jomaa (CoRR, 2021)

Learning to Control Local Search for Combinatorial Optimization. - Jonas K. Falkner; Daniela Thyssens; Ahmad Bdeir; Lars Schmidt-Thieme (ECML/PKDD (5), 2022)

Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size. - Daniela Thyssens; Jonas K. Falkner; Lars Schmidt-Thieme (CoRR, 2022)

Large Neighborhood Search based on Neural Construction Heuristics. - Jonas K. Falkner; Daniela Thyssens; Lars Schmidt-Thieme (CoRR, 2022)

DeepStay: Stay Region Extraction from Location Trajectories Using Weak Supervision. - Christian Löwens; Daniela Thyssens; Emma Andersson; Christina Jenkins; Lars Schmidt-Thieme (ITSC, 2023)

Routing Arena: A Benchmark Suite for Neural Routing Solvers. - Daniela Thyssens; Tim Dernedde; Jonas K. Falkner; Lars Schmidt-Thieme (CoRR, 2023)

Graph2RETA: Graph Neural Networks for Pick-up and Delivery Route Prediction and Arrival Time Estimation. - Wilson Sentanoe; Sreyashi Saha; Yasas Diyanananda; Aqsa Manzoor; Buddhika Dasanayake; Daniela Thyssens (KI, 2024)

Moco: A Learnable Meta Optimizer for Combinatorial Optimization. - Tim Dernedde; Daniela Thyssens; Sören Dittrich; Maximilian Stubbemann; Lars Schmidt-Thieme (PAKDD (3), 2025)