Deep Generative Demand Learning for Newsvendor and Pricing
Fuente:
arXiv
Saved in:
| Main Authors: | Gong, Shijin, Liu, Huihang, Zhang, Xinyu |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning When to Restart: Nonstationary Newsvendor from Uncensored to Censored Demand
by: Chen, Xin, et al.
Published: (2025)
by: Chen, Xin, et al.
Published: (2025)
The Data-Driven Censored Newsvendor Problem
by: Hssaine, Chamsi, et al.
Published: (2024)
by: Hssaine, Chamsi, et al.
Published: (2024)
Closing the Gaps: Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems
by: Lyu, Jiameng, et al.
Published: (2024)
by: Lyu, Jiameng, et al.
Published: (2024)
From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
by: Besbes, Omar, et al.
Published: (2023)
by: Besbes, Omar, et al.
Published: (2023)
Deep Neural Newsvendor
by: Han, Jinhui, et al.
Published: (2023)
by: Han, Jinhui, et al.
Published: (2023)
Learning to Price with Resource Constraints: From Full Information to Machine-Learned Prices
by: Ao, Ruicheng, et al.
Published: (2025)
by: Ao, Ruicheng, et al.
Published: (2025)
Extensions of Robbins-Siegmund Theorem with Applications in Reinforcement Learning
by: Liu, Xinyu, et al.
Published: (2025)
by: Liu, Xinyu, et al.
Published: (2025)
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planning
by: van Twiller, Jaike, et al.
Published: (2025)
by: van Twiller, Jaike, et al.
Published: (2025)
Almost Sure Convergence Rates of Stochastic Approximation and Reinforcement Learning via a Poisson-Moreau Drift
by: Liu, Xinyu, et al.
Published: (2026)
by: Liu, Xinyu, et al.
Published: (2026)
Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning
by: Fan, Yingying, et al.
Published: (2026)
by: Fan, Yingying, et al.
Published: (2026)
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events
by: Taboga, Vincent, et al.
Published: (2023)
by: Taboga, Vincent, et al.
Published: (2023)
Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Qian, Xiaochi, et al.
Published: (2024)
by: Qian, Xiaochi, et al.
Published: (2024)
Online Learning and Optimization for Queues with Unknown Demand Curve and Service Distribution
by: Chen, Xinyun, et al.
Published: (2023)
by: Chen, Xinyun, et al.
Published: (2023)
Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
by: Jungel, Kai, et al.
Published: (2023)
by: Jungel, Kai, et al.
Published: (2023)
Learning to Price Bundles: A GCN Approach for Mixed Bundling
by: Ding, Liangyu, et al.
Published: (2025)
by: Ding, Liangyu, et al.
Published: (2025)
The New Era of Dynamic Pricing: Synergizing Supervised Learning and Quadratic Programming
by: Bramao, Gustavo, et al.
Published: (2024)
by: Bramao, Gustavo, et al.
Published: (2024)
Spatial Supply Repositioning with Censored Demand Data
by: Jiang, Hansheng, et al.
Published: (2025)
by: Jiang, Hansheng, et al.
Published: (2025)
Deep Generalized Schrödinger Bridges: From Image Generation to Solving Mean-Field Games
by: Liu, Guan-Horng, et al.
Published: (2024)
by: Liu, Guan-Horng, et al.
Published: (2024)
Semi-on-Demand Transit Feeders with Shared Autonomous Vehicles and Reinforcement-Learning-Based Zonal Dispatching Control
by: Ng, Max T. M., et al.
Published: (2025)
by: Ng, Max T. M., et al.
Published: (2025)
Fast Policy Learning for Linear Quadratic Control with Entropy Regularization
by: Guo, Xin, et al.
Published: (2023)
by: Guo, Xin, et al.
Published: (2023)
Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
by: Tran, Hoang, et al.
Published: (2024)
by: Tran, Hoang, et al.
Published: (2024)
Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework
by: Guan, Hongzhao, et al.
Published: (2026)
by: Guan, Hongzhao, et al.
Published: (2026)
Learning-Based Pricing and Matching for Two-Sided Queues
by: Yang, Zixian, et al.
Published: (2024)
by: Yang, Zixian, et al.
Published: (2024)
The Price of Adaptivity in Stochastic Convex Optimization
by: Carmon, Yair, et al.
Published: (2024)
by: Carmon, Yair, et al.
Published: (2024)
The Value of Information in Resource-Constrained Pricing
by: Ao, Ruicheng, et al.
Published: (2026)
by: Ao, Ruicheng, et al.
Published: (2026)
Newsvendor under Ambiguity and Misspecification
by: Liu, Feng, et al.
Published: (2024)
by: Liu, Feng, et al.
Published: (2024)
DLMMPR:Deep Learning-based Measurement Matrix for Phase Retrieval
by: Liu, Jing, et al.
Published: (2025)
by: Liu, Jing, et al.
Published: (2025)
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes
by: Zhang, Erica, et al.
Published: (2024)
by: Zhang, Erica, et al.
Published: (2024)
Enhancing Accuracy in Deep Learning Using Random Matrix Theory
by: Berlyand, Leonid, et al.
Published: (2023)
by: Berlyand, Leonid, et al.
Published: (2023)
AI2STOW: End-to-End Deep Reinforcement Learning to Construct Master Stowage Plans under Demand Uncertainty
by: Van Twiller, Jaike, et al.
Published: (2025)
by: Van Twiller, Jaike, et al.
Published: (2025)
Adaptive Moment Estimation Optimization Algorithm Using Projection Gradient for Deep Learning
by: Li, Yongqi, et al.
Published: (2025)
by: Li, Yongqi, et al.
Published: (2025)
Mathematical Foundations of Deep Learning
by: Ye, Xiaojing
Published: (2026)
by: Ye, Xiaojing
Published: (2026)
Revenue Optimization with Price-Sensitive and Interdependent Demand
by: Laasri, Julien, et al.
Published: (2025)
by: Laasri, Julien, et al.
Published: (2025)
A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints
by: Stradi, Francesco Emanuele, et al.
Published: (2024)
by: Stradi, Francesco Emanuele, et al.
Published: (2024)
Deep Learning Model Predictive Control for Deep Brain Stimulation in Parkinson's Disease
by: Steffen, Sebastian, et al.
Published: (2025)
by: Steffen, Sebastian, et al.
Published: (2025)
Learning to Stop: Deep Learning for Mean Field Optimal Stopping
by: Magnino, Lorenzo, et al.
Published: (2024)
by: Magnino, Lorenzo, et al.
Published: (2024)
LSTM-Based Forecasting and Analysis of EV Charging Demand in a Dense Urban Campus
by: Ressler, Zak, et al.
Published: (2025)
by: Ressler, Zak, et al.
Published: (2025)
Deep Learning for the Multiple Optimal Stopping Problem
by: Laurière, Mathieu, et al.
Published: (2025)
by: Laurière, Mathieu, et al.
Published: (2025)
Learning Exactly Linearizable Deep Dynamics Models
by: Moriyasu, Ryuta, et al.
Published: (2023)
by: Moriyasu, Ryuta, et al.
Published: (2023)
Deep Reinforcement Learning: A Convex Optimization Approach
by: Gattami, Ather
Published: (2024)
by: Gattami, Ather
Published: (2024)
Similar Items
-
Learning When to Restart: Nonstationary Newsvendor from Uncensored to Censored Demand
by: Chen, Xin, et al.
Published: (2025) -
The Data-Driven Censored Newsvendor Problem
by: Hssaine, Chamsi, et al.
Published: (2024) -
Closing the Gaps: Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems
by: Lyu, Jiameng, et al.
Published: (2024) -
From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
by: Besbes, Omar, et al.
Published: (2023) -
Deep Neural Newsvendor
by: Han, Jinhui, et al.
Published: (2023)