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Found 5 papers, 2 papers with code
Smoothed $f$-Divergence Distributionally Robust Optimization
In data-driven optimization, sample average approximation (SAA) is known to suffer from the so-called optimizer's curse that causes an over-optimistic evaluation of the solution performance.
Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions
We define first a sensible yard stick with which to measure the quality of any data-driven formulation and subsequently seek to find an optimal such formulation.
Efficient Data-Driven Optimization with Noisy Data
Classical Kullback-Leibler or entropic distances are known to enjoy certain desirable statistical properties in the context of decision-making with noiseless data.
Exterior-point Optimization for Sparse and Low-rank Optimization
In this paper, we present the nonconvex exterior-point optimization solver NExOS -- a first-order algorithm tailored to sparse and low-rank optimization problems.
Optimization and Control
Optimal Learning for Structured Bandits
We propose a novel learning algorithm that we call "DUSA" whose regret matches the information-theoretic regret lower bound up to a constant factor and can handle a wide range of structural information.
