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Feature-Rich, Data-Private: A Sparse Learning Framework for the High-Dimensional Newsvendor
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Outgoing claims: the papers this work cites, builds on, or argues with.
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One‐Bit Compressed Sensing by Linear Programming
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Approximation by superpositions of a sigmoidal function
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Multilayer feedforward networks are universal approximators
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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Model Selection via Bayesian Information Criterion for Quantile Regression Models
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Minimax Optimal Procedures for Locally Private Estimation
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Smoothing Quantile Regressions
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Concentration Inequalities
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Local Privacy and Statistical Minimax Rates
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Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds
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Normalized Iterative Hard Thresholding: Guaranteed Stability and Performance
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On Sparse Linear Regression in the Local Differential Privacy Model
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Stagewise Weak Gradient Pursuits
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Regression Shrinkage and Selection Via the Lasso
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How Research in Production and Operations Management May Evolve in the Era of Big Data
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Neural Networks for Optimal Approximation of Smooth and Analytic Functions
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ℓ1-penalized quantile regression in high-dimensional sparse models
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Best subset selection via a modern optimization lens
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From Predictive to Prescriptive Analytics
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Privacy-Preserving Dynamic Personalized Pricing with Demand Learning
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Online Learning and Decision Making Under Generalized Linear Model with High-Dimensional Data
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Fast Rates for Contextual Linear Optimization
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Privacy-Preserving Personalized Revenue Management
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A Nonparametric Asymptotic Analysis of Inventory Planning with Censored Demand
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Feature-Based Inventory Control with Censored Demand
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Offline Feature-Based Pricing Under Censored Demand: A Causal Inference Approach
inferredcites →https://doi.org/10.1287/opre.2015.1422openalex-snapshot
The Data-Driven Newsvendor Problem: New Bounds and Insights
inferredcites →https://doi.org/10.1287/opre.2018.1757openalex-snapshot
The Big Data Newsvendor: Practical Insights from Machine Learning
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Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
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Differential Privacy in Personalized Pricing with Nonparametric Demand Models
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An Algorithmic Approach to Managing Supply Chain Data Security: The Differentially Private Newsvendor
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The Algorithmic Foundations of Differential Privacy
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Differentially private false discovery rate control
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