Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecasting
Fuente:
arXiv
Guardado en:
| Autores principales: | Tziorvas, Antonios, Tritsarolis, Andreas, Theodoridis, Yannis |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
MoDE-Boost: Boosting Shared Mobility Demand with Edge-Ready Prediction Models
por: Tziorvas, Antonios, et al.
Publicado: (2026)
por: Tziorvas, Antonios, et al.
Publicado: (2026)
On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning
por: Tritsarolis, Andreas, et al.
Publicado: (2026)
por: Tritsarolis, Andreas, et al.
Publicado: (2026)
On Vessel Location Forecasting and the Effect of Federated Learning
por: Tritsarolis, Andreas, et al.
Publicado: (2024)
por: Tritsarolis, Andreas, et al.
Publicado: (2024)
FLP-XR: Future Location Prediction on Extreme Scale Maritime Data in Real-time
por: Theodoropoulos, George S., et al.
Publicado: (2025)
por: Theodoropoulos, George S., et al.
Publicado: (2025)
Electric Vehicle Charging Load Forecasting: An Experimental Comparison of Machine Learning Methods
por: Kyriakopoulos, Iason, et al.
Publicado: (2025)
por: Kyriakopoulos, Iason, et al.
Publicado: (2025)
T-STAR: A Context-Aware Transformer Framework for Short-Term Probabilistic Demand Forecasting in Dock-Based Shared Micro-Mobility
por: Cheng, Jingyi, et al.
Publicado: (2026)
por: Cheng, Jingyi, et al.
Publicado: (2026)
Towards Data-driven Nitrogen Estimation in Wheat Fields using Multispectral Images
por: Tritsarolis, Andreas, et al.
Publicado: (2026)
por: Tritsarolis, Andreas, et al.
Publicado: (2026)
A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting
por: Park, Young-Jin, et al.
Publicado: (2024)
por: Park, Young-Jin, et al.
Publicado: (2024)
A Typed Tensor Language for Federated Learning
por: Mailis, Theofilos, et al.
Publicado: (2026)
por: Mailis, Theofilos, et al.
Publicado: (2026)
Multivariate Forecasting of Bitcoin Volatility with Gradient Boosting: Deterministic, Probabilistic, and Feature Importance Perspectives
por: Dudek, Grzegorz, et al.
Publicado: (2025)
por: Dudek, Grzegorz, et al.
Publicado: (2025)
Building Gradient Bridges: Label Leakage from Restricted Gradient Sharing in Federated Learning
por: Zhang, Rui, et al.
Publicado: (2024)
por: Zhang, Rui, et al.
Publicado: (2024)
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks
por: Li, Yi, et al.
Publicado: (2024)
por: Li, Yi, et al.
Publicado: (2024)
SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree
por: Fan, Tao, et al.
Publicado: (2021)
por: Fan, Tao, et al.
Publicado: (2021)
Forecasting Residential Heating and Electricity Demand with Scalable, High-Resolution, Open-Source Models
por: Lee, Stephen J., et al.
Publicado: (2025)
por: Lee, Stephen J., et al.
Publicado: (2025)
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning
por: Abourayya, Amr, et al.
Publicado: (2023)
por: Abourayya, Amr, et al.
Publicado: (2023)
Condensed Gradient Boosting
por: Emami, Seyedsaman, et al.
Publicado: (2022)
por: Emami, Seyedsaman, et al.
Publicado: (2022)
Federated Learning for Early Prediction of EV Charging Demand
por: Perifanis, Vasilis, et al.
Publicado: (2026)
por: Perifanis, Vasilis, et al.
Publicado: (2026)
Probabilistic Demand Forecasting with Graph Neural Networks
por: Kozodoi, Nikita, et al.
Publicado: (2024)
por: Kozodoi, Nikita, et al.
Publicado: (2024)
Deriving Hematological Disease Classes Using Fuzzy Logic and Expert Knowledge: A Comprehensive Machine Learning Approach with CBC Parameters
por: Ameen, Salem, et al.
Publicado: (2024)
por: Ameen, Salem, et al.
Publicado: (2024)
Spatial PDE-aware Selective State-space with Nested Memory for Mobile Traffic Grid Forecasting
por: Bettouche, Zineddine, et al.
Publicado: (2026)
por: Bettouche, Zineddine, et al.
Publicado: (2026)
ParamBoost: Gradient Boosted Piecewise Cubic Polynomials
por: Salvadé, Nicolas, et al.
Publicado: (2026)
por: Salvadé, Nicolas, et al.
Publicado: (2026)
Gradient Boosted Risk Scores
por: Georgantas, Costa, et al.
Publicado: (2026)
por: Georgantas, Costa, et al.
Publicado: (2026)
On the Convergence of Multicalibration Gradient Boosting
por: Haimovich, Daniel, et al.
Publicado: (2026)
por: Haimovich, Daniel, et al.
Publicado: (2026)
RieszBoost: Gradient Boosting for Riesz Regression
por: Lee, Kaitlyn J., et al.
Publicado: (2025)
por: Lee, Kaitlyn J., et al.
Publicado: (2025)
Advancing Heat Demand Forecasting with Attention Mechanisms: Opportunities and Challenges
por: Ramachandran, Adithya, et al.
Publicado: (2025)
por: Ramachandran, Adithya, et al.
Publicado: (2025)
Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations
por: Kamarthi, Harshavardhan, et al.
Publicado: (2026)
por: Kamarthi, Harshavardhan, et al.
Publicado: (2026)
Measuring Time Series Forecast Stability for Demand Planning
por: Klee, Steven, et al.
Publicado: (2025)
por: Klee, Steven, et al.
Publicado: (2025)
Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids
por: Banerjee, Roopkatha, et al.
Publicado: (2025)
por: Banerjee, Roopkatha, et al.
Publicado: (2025)
Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail
por: Henzel, Joanna, et al.
Publicado: (2020)
por: Henzel, Joanna, et al.
Publicado: (2020)
Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecasting
por: Turazza, Fabio, et al.
Publicado: (2026)
por: Turazza, Fabio, et al.
Publicado: (2026)
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting
por: Pavlidis, Nikolaos, et al.
Publicado: (2024)
por: Pavlidis, Nikolaos, et al.
Publicado: (2024)
Robust-Multi-Task Gradient Boosting
por: Emami, Seyedsaman, et al.
Publicado: (2025)
por: Emami, Seyedsaman, et al.
Publicado: (2025)
Gradient Boosted Filters For Signal Processing
por: Lopez, Jose A., et al.
Publicado: (2024)
por: Lopez, Jose A., et al.
Publicado: (2024)
Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning
por: Shankar, Aditya, et al.
Publicado: (2024)
por: Shankar, Aditya, et al.
Publicado: (2024)
Improving Forecasts for Heterogeneous Time Series by "Averaging", with Application to Food Demand Forecast
por: Neubauer, Lukas, et al.
Publicado: (2023)
por: Neubauer, Lukas, et al.
Publicado: (2023)
Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand
por: Smyl, Slawek, et al.
Publicado: (2024)
por: Smyl, Slawek, et al.
Publicado: (2024)
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates
por: Ma, Chenyang, et al.
Publicado: (2023)
por: Ma, Chenyang, et al.
Publicado: (2023)
CogScale: Scalable Benchmark for Sequence Processing
por: Bendi-Ouis, Yannis, et al.
Publicado: (2026)
por: Bendi-Ouis, Yannis, et al.
Publicado: (2026)
ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
por: Gong, Zirui, et al.
Publicado: (2026)
por: Gong, Zirui, et al.
Publicado: (2026)
Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing
por: Lu, Zhufeng, et al.
Publicado: (2025)
por: Lu, Zhufeng, et al.
Publicado: (2025)
Ejemplares similares
-
MoDE-Boost: Boosting Shared Mobility Demand with Edge-Ready Prediction Models
por: Tziorvas, Antonios, et al.
Publicado: (2026) -
On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning
por: Tritsarolis, Andreas, et al.
Publicado: (2026) -
On Vessel Location Forecasting and the Effect of Federated Learning
por: Tritsarolis, Andreas, et al.
Publicado: (2024) -
FLP-XR: Future Location Prediction on Extreme Scale Maritime Data in Real-time
por: Theodoropoulos, George S., et al.
Publicado: (2025) -
Electric Vehicle Charging Load Forecasting: An Experimental Comparison of Machine Learning Methods
por: Kyriakopoulos, Iason, et al.
Publicado: (2025)