Add to ORKGAbstract. Our goal is to build robust optimization problems for making decisions based on complex data from the past. In robust optimization (RO) generally, the goal is to create a policy for decision-making that is robust to our uncertainty about the future. In particular, we want our policy to best handle the the worst possible situation that could arise, out of an uncertainty set of possible situations. Classically, the uncertainty set is simply chosen by the user, or it might be estimated in overly simplistic ways with strong assumptions; whereas in this work, we learn the uncertainty set from data collected in the past. The past data are drawn randomly from an (unknown) possibly complicated high-dimensional distribution. We propose a n...
Recent advances in decision making have incorporated both risk and ambiguity in decision theory and ...
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Resear...
This paper studies the problem of constructing robust classifiers when the training is plagued with ...
The last decade witnessed an explosion in the availability of data for operations research applicati...
In order for reinforcement learning techniques to be useful in real-world decision making processes,...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach us...
This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach us...
Robust Optimization has traditionally taken a pessimistic, or worst-case viewpoint of uncertainty wh...
Optimization problems due to noisy data are usually solved us-ing stochastic programming or robust o...
We propose a general framework for machine learning based optimization under uncertainty. Our approa...
In robust optimization, the general aim is to find a solution that performs well over a set of possi...
Motivated by data-driven decision making and sampling problems, we investigate probabilistic interpr...
Decision making formulated as finding a strategy that maximizes a utility function depends critic...
It is fair to say that in many real world decision problems the underlying models cannot be accurate...
Recent advances in decision making have incorporated both risk and ambiguity in decision theory and ...
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Resear...
This paper studies the problem of constructing robust classifiers when the training is plagued with ...
The last decade witnessed an explosion in the availability of data for operations research applicati...
In order for reinforcement learning techniques to be useful in real-world decision making processes,...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach us...
This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach us...
Robust Optimization has traditionally taken a pessimistic, or worst-case viewpoint of uncertainty wh...
Optimization problems due to noisy data are usually solved us-ing stochastic programming or robust o...
We propose a general framework for machine learning based optimization under uncertainty. Our approa...
In robust optimization, the general aim is to find a solution that performs well over a set of possi...
Motivated by data-driven decision making and sampling problems, we investigate probabilistic interpr...
Decision making formulated as finding a strategy that maximizes a utility function depends critic...
It is fair to say that in many real world decision problems the underlying models cannot be accurate...
Recent advances in decision making have incorporated both risk and ambiguity in decision theory and ...
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Resear...
This paper studies the problem of constructing robust classifiers when the training is plagued with ...