Provably near-optimal sampling-based policies for stochastic inventory control models
In this paper, we consider two fundamental inventory models, the single-period newsvendor
problem and its multiperiod extension, but under the assumption that the explicit demand …
problem and its multiperiod extension, but under the assumption that the explicit demand …
The framework of parametric and nonparametric operational data analytics
This paper introduces the general philosophy of the Operational Data Analytics (ODA)
framework for data‐based decision modeling. The fundamental development of this …
framework for data‐based decision modeling. The fundamental development of this …
Price of correlations in stochastic optimization
When decisions are made in the presence of high-dimensional stochastic data, handling
joint distribution of correlated random variables can present a formidable task, both in terms …
joint distribution of correlated random variables can present a formidable task, both in terms …
Sampling-based approximation algorithms for multistage stochastic optimization
Stochastic optimization problems provide a means to model uncertainty in the input data
where the uncertainty is modeled by a probability distribution over the possible realizations …
where the uncertainty is modeled by a probability distribution over the possible realizations …
Sampling-based approximation schemes for capacitated stochastic inventory control models
We study the classical multiperiod capacitated stochastic inventory control problems in a
data-driven setting. Instead of assuming full knowledge of the demand distributions, we …
data-driven setting. Instead of assuming full knowledge of the demand distributions, we …
Optimal online assignment with forecasts
Motivated by the allocation problem facing publishers in display advertising we formulate the
online assignment with forecast problem, a version of the online allocation problem where …
online assignment with forecast problem, a version of the online allocation problem where …
An approximation scheme for stochastic linear programming and its application to stochastic integer programs
Stochastic optimization problems attempt to model uncertainty in the data by assuming that
the input is specified by a probability distribution. We consider the well-studied paradigm of …
the input is specified by a probability distribution. We consider the well-studied paradigm of …
Approximation algorithms for budgeted learning problems
We present the first approximation algorithms for a large class of budgeted learning
problems. One classicexample of the above is the budgeted multi-armed bandit problem. In …
problems. One classicexample of the above is the budgeted multi-armed bandit problem. In …
Approximation algorithms for 2-stage stochastic optimization problems
Uncertainty is a facet of many decision environments and might arise for various reasons,
such as unpredictable information revealed in the future, or inherent fluctuations caused by …
such as unpredictable information revealed in the future, or inherent fluctuations caused by …
Beating greedy for stochastic bipartite matching
We consider the maximum bipartite matching problem in stochastic settings, namely the
query-commit and price-of-information models. In the query-commit model, an edge e …
query-commit and price-of-information models. In the query-commit model, an edge e …