Cover Image

Private Optimal Inventory Policy Learning for Feature-based Newsvendor with Unknown Demand

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhao, Tuoyi, Zhou, Wen-xin, Wang, Lan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Deep Generative Demand Learning for Newsvendor and Pricing
by: Gong, Shijin, et al.
Published: (2024)

Learning When to Restart: Nonstationary Newsvendor from Uncensored to Censored Demand
by: Chen, Xin, et al.
Published: (2025)

A Conformal Approach to Feature-based Newsvendor under Model Misspecification
by: Cao, Junyu
Published: (2024)

Closing the Gaps: Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems
by: Lyu, Jiameng, et al.
Published: (2024)

Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions
by: Hu, Zihao, et al.
Published: (2026)

A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy
by: Lyu, Jiameng, et al.
Published: (2024)

Thompson Sampling for Repeated Newsvendor
by: Chen, Li, et al.
Published: (2025)

Learning an Inventory Control Policy with General Inventory Arrival Dynamics
by: Andaz, Sohrab, et al.
Published: (2023)

The Data-Driven Censored Newsvendor Problem
by: Hssaine, Chamsi, et al.
Published: (2024)

Supervised Learning for the (s,S) Inventory Model with General Interarrival Demands and General Lead Times
by: Sherzer, Eliran, et al.
Published: (2026)

Exploratory Machine Learning with Unknown Unknowns
by: Zhao, Peng, et al.
Published: (2020)

Understanding Private Learning From Feature Perspective
by: Ding, Meng, et al.
Published: (2025)

On Optimal Hyperparameters for Differentially Private Deep Transfer Learning
by: Rehn, Aki, et al.
Published: (2025)

On-Demand Sampling: Learning Optimally from Multiple Distributions
by: Haghtalab, Nika, et al.
Published: (2022)

VC Theory for Inventory Policies
by: Xie, Yaqi, et al.
Published: (2024)

Survey of Data-driven Newsvendor: Unified Analysis and Spectrum of Achievable Regrets
by: Chen, Zhuoxin, et al.
Published: (2024)

What is the Value of Censored Data? An Exact Analysis for the Data-driven Newsvendor
by: Kumar, Rachitesh, et al.
Published: (2026)

Adaptive Policy Learning Under Unknown Network Interference
by: Gleich, Aidan, et al.
Published: (2026)

Aggregating Data for Optimal and Private Learning
by: Agarwal, Sushant, et al.
Published: (2024)

Online Learning and Optimization for Queues with Unknown Demand Curve and Service Distribution
by: Chen, Xinyun, et al.
Published: (2023)

Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning
by: Yifru, Lunet, et al.
Published: (2024)

DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management
by: Xie, Yaqi, et al.
Published: (2026)

Optimal Kernel Quantile Learning with Random Features
by: Wang, Caixing, et al.
Published: (2024)

Grasp, See, and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior
by: Xu, Kechun, et al.
Published: (2024)

On the Sample Complexity of Differentially Private Policy Optimization
by: He, Yi, et al.
Published: (2025)

Differentially Private Policy Gradient
by: Rio, Alexandre, et al.
Published: (2025)

Private Estimation when Data and Privacy Demands are Correlated
by: Chaudhuri, Syomantak, et al.
Published: (2024)

Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization
by: Alvo, Matias, et al.
Published: (2023)

Nearly Optimal Differentially Private ReLU Regression
by: Ding, Meng, et al.
Published: (2025)

From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
by: Besbes, Omar, et al.
Published: (2023)

Can Transformers Learn Optimal Filtering for Unknown Systems?
by: Balim, Haldun, et al.
Published: (2023)

Learning Optimal Deterministic Policies with Stochastic Policy Gradients
by: Montenegro, Alessandro, et al.
Published: (2024)

Mind the Gap: Optimal and Equitable Encouragement Policies
by: Zhou, Angela
Published: (2023)

Locally Differentially Private Distributed Online Learning with Guaranteed Optimality
by: Chen, Ziqin, et al.
Published: (2023)

Learning Koopman-based Stability Certificates for Unknown Nonlinear Systems
by: Zhou, Ruikun, et al.
Published: (2024)

Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity
by: Li, Bo, et al.
Published: (2025)

Optimal Control of Nonlinear Systems with Unknown Dynamics
by: Hao, Wenjian, et al.
Published: (2023)

Near-Optimal Algorithms for Differentially Private Online Learning in a Stochastic Environment
by: Hu, Bingshan, et al.
Published: (2021)

Temperature as a Meta-Policy: Adaptive Temperature in LLM Reinforcement Learning
by: Dang, Haoran, et al.
Published: (2026)

Distribution-Centric Policy Optimization Dominates Exploration-Exploitation Trade-off
by: Li, Zhaochun, et al.
Published: (2026)