Copertina

DISCO: An End-to-End Bandit Framework for Personalised Discount Allocation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Jason Shuo, Howson, Benjamin, Savva, Panayiota, Loh, Eleanor
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

DISCO: Diversifying Sample Condensation for Efficient Model Evaluation
di: Rubinstein, Alexander, et al.
Pubblicazione: (2025)

Agentic Personalisation of Cross-Channel Marketing Experiences
di: Abboud, Sami, et al.
Pubblicazione: (2025)

ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation
di: Bakhtyari, Mohammadreza, et al.
Pubblicazione: (2025)

An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems
di: Miao, Changhao, et al.
Pubblicazione: (2025)

Attack End-to-End Autonomous Driving through Module-Wise Noise
di: Wang, Lu, et al.
Pubblicazione: (2024)

Bridging the Divide: End-to-End Sequence-Graph Learning
di: Chen, Yuen, et al.
Pubblicazione: (2025)

Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning
di: Zhang, Kehao, et al.
Pubblicazione: (2026)

Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Study
di: Jeunen, Olivier, et al.
Pubblicazione: (2026)

Stratos: An End-to-End Distillation Pipeline for Customized LLMs under Distributed Cloud Environments
di: Dai, Ziming, et al.
Pubblicazione: (2025)

End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler
di: Blessing, Denis, et al.
Pubblicazione: (2025)

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
di: Pan, Jianming, et al.
Pubblicazione: (2024)

MASteer: Multi-Agent Adaptive Steer Strategy for End-to-End LLM Trustworthiness Repair
di: Li, Changqing, et al.
Pubblicazione: (2025)

Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
di: Zheng, Yinan, et al.
Pubblicazione: (2026)

End-to-End Probabilistic Framework for Learning with Hard Constraints
di: Utkarsh, Utkarsh, et al.
Pubblicazione: (2025)

AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models
di: Yao, Yifei, et al.
Pubblicazione: (2024)

Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning
di: Braun, Dan, et al.
Pubblicazione: (2024)

End-to-End Learning for Partially-Observed Time Series with PyPOTS
di: Du, Wenjie, et al.
Pubblicazione: (2026)

Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
di: Cini, Andrea, et al.
Pubblicazione: (2023)

LEAP: Learnable End-to-End Adaptive Pruning of Large Language Models
di: Mozaffari, Mohammad, et al.
Pubblicazione: (2026)

End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost
di: Tan, Qitao, et al.
Pubblicazione: (2025)

GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
di: Yu, Chengqing, et al.
Pubblicazione: (2024)

Learning to be Smooth: An End-to-End Differentiable Particle Smoother
di: Younis, Ali, et al.
Pubblicazione: (2025)

FinWorld: An All-in-One Open-Source Platform for End-to-End Financial AI Research and Deployment
di: Zhang, Wentao, et al.
Pubblicazione: (2025)

Sub-JEPA: Subspace Gaussian Regularization for Stable End-to-End World Models
di: Zhao, Kai, et al.
Pubblicazione: (2026)

Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines
di: Lambert, Katherine, et al.
Pubblicazione: (2026)

GPart: End-to-End Isometric Fine-Tuning via Global Parameter Partitioning
di: Mandica, Paolo, et al.
Pubblicazione: (2026)

RewriteNets: End-to-End Trainable String-Rewriting for Generative Sequence Modeling
di: Vejendla, Harshil
Pubblicazione: (2026)

EvaDrive: Evolutionary Adversarial Policy Optimization for End-to-End Autonomous Driving
di: Jiao, Siwen, et al.
Pubblicazione: (2025)

Conditional Rectified Flow-based End-to-End Rapid Seismic Inversion Method
di: Xu, Haofei, et al.
Pubblicazione: (2026)

DISCO-TAB: A Hierarchical Reinforcement Learning Framework for Privacy-Preserving Synthesis of Complex Clinical Data
di: Ilaty, Arshia, et al.
Pubblicazione: (2026)

ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers
di: Chen, Liangliang, et al.
Pubblicazione: (2024)

Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment
di: Ji, Yuhao, et al.
Pubblicazione: (2024)

Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups
di: You, Weiqiu, et al.
Pubblicazione: (2023)

End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation
di: Tripathi, Anurag, et al.
Pubblicazione: (2025)

An End-to-End Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drones
di: Zeng, Taihelong, et al.
Pubblicazione: (2025)

OptiProxy-NAS: Optimization Proxy based End-to-End Neural Architecture Search
di: Lyu, Bo, et al.
Pubblicazione: (2025)

Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents
di: Wang, Haochen, et al.
Pubblicazione: (2026)

PlatformX: An End-to-End Transferable Platform for Energy-Efficient Neural Architecture Search
di: Tu, Xiaolong, et al.
Pubblicazione: (2025)

Developing an End-to-End Framework for Predicting the Social Communication Severity Scores of Children with Autism Spectrum Disorder
di: Mun, Jihyun, et al.
Pubblicazione: (2024)

NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI
di: Yang, Hanchen, et al.
Pubblicazione: (2025)