Daniela Thyssens

14 papers Misc 1Journal 9Unranked 4
YearRankTypeTitle / Venue / Authors
2025 — conf
Moco: A Learnable Meta Optimizer for Combinatorial Optimization.
PAKDD (3)
Tim Dernedde, Daniela Thyssens, Sören Dittrich, Maximilian Stubbemann, Lars Schmidt-Thieme
2025 J jnl
On Distributional Dependent Performance of Classical and Neural Routing Solvers.
CoRR
Daniela Thyssens, Tim Dernedde, Wilson Sentanoe, Lars Schmidt-Thieme
2025 J jnl
Recurrent State Encoders for Efficient Neural Combinatorial Optimization.
CoRR
Tim Dernedde, Daniela Thyssens, Lars Schmidt-Thieme
2024 Misc conf
Graph2RETA: Graph Neural Networks for Pick-up and Delivery Route Prediction and Arrival Time Estimation.
KI
Wilson Sentanoe, Sreyashi Saha, Yasas Diyanananda, Aqsa Manzoor, Buddhika Dasanayake, Daniela Thyssens
2024 J jnl
Moco: A Learnable Meta Optimizer for Combinatorial Optimization.
CoRR
Tim Dernedde, Daniela Thyssens, Sören Dittrich, Maximilian Stubbemann, Lars Schmidt-Thieme
2023 — conf
DeepStay: Stay Region Extraction from Location Trajectories Using Weak Supervision.
ITSC
Christian Löwens, Daniela Thyssens, Emma Andersson, Christina Jenkins, Lars Schmidt-Thieme
2023 J jnl
DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision.
CoRR
Christian Löwens, Daniela Thyssens, Emma Andersson, Christina Jenkins, Lars Schmidt-Thieme
2023 J jnl
Routing Arena: A Benchmark Suite for Neural Routing Solvers.
CoRR
Daniela Thyssens, Tim Dernedde, Jonas K. Falkner, Lars Schmidt-Thieme
2022 J jnl
Large Neighborhood Search based on Neural Construction Heuristics.
CoRR
Jonas K. Falkner, Daniela Thyssens, Lars Schmidt-Thieme
2022 — conf
Learning to Control Local Search for Combinatorial Optimization.
ECML/PKDD (5)
Jonas K. Falkner, Daniela Thyssens, Ahmad Bdeir, Lars Schmidt-Thieme
2022 J jnl
Learning to Control Local Search for Combinatorial Optimization.
CoRR
Jonas K. Falkner, Daniela Thyssens, Ahmad Bdeir, Lars Schmidt-Thieme
2022 J jnl
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size.
CoRR
Daniela Thyssens, Jonas K. Falkner, Lars Schmidt-Thieme
2021 J jnl
Do We Really Need Deep Learning Models for Time Series Forecasting?
CoRR
Shereen Elsayed, Daniela Thyssens, Ahmed Rashed, Lars Schmidt-Thieme, Hadi Samer Jomaa
2020 — conf
Exploring the Influence of Data Aggregation in Parking Prediction.
DEXA Workshops
Shereen Elsayed, Daniela Thyssens, Shabanaz Chamurally, Arslan Tariq, Hadi Samer Jomaa