73 citations · 218 across the 15 of their papers we have counts for
12 papers · 1 filter
A stochastic subgradient method with optimal failure exponent
Bart P. G. van Parys
Fix a target accuracy , a gradient-noise level , and a horizon . We wish to design algorithms which minimize the probability of observing a suboptimality gap whi…
Globalized Adversarial Regret Optimization: Robust Decisions with Uncalibrated Predictions
Jannis Kurtz, Bart P. G. van Parys
Optimization problems routinely depend on uncertain parameters that must be predicted before a decision is made. Classical robust and regret formulations are designed to handle err…
Subgradient Methods for Nonsmooth Convex Functions with Adversarial Errors
Martijn Gösgens, Bart P. G. Van Parys
We consider minimizing nonsmooth convex functions with bounded subgradients. However, instead of directly observing a subgradient at every step , we assume th…
Smoothed -Divergence Distributionally Robust Optimization
Zhenyuan Liu, Bart P. G. Van Parys, Henry Lam
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 pe…
Mean Robust Optimization
Irina Wang, Cole Becker, Bart Van Parys +1
Robust optimization is a tractable and expressive technique for decision-making under uncertainty, but it can lead to overly conservative decisions when pessimistic assumptions are…
Efficient Data-Driven Optimization with Noisy Data
Bart P. G. Van Parys
Classical Kullback-Leibler or entropic distances are known to enjoy certain desirable statistical properties in the context of decision-making with noiseless data. However, in most…
A Pareto Dominance Principle for Data-Driven Optimization
Tobias Sutter, Bart P. G. Van Parys, Daniel Kuhn
We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that…
Bootstrap Robust Prescriptive Analytics
Dimitris Bertsimas, Bart Van Parys
We address the problem of prescribing an optimal decision in a framework where the cost function depends on uncertain problem parameters that need to be learned from data. Earlier…
Sparse Classification: a scalable discrete optimization perspective
Dimitris Bertsimas, Jean Pauphilet, Bart Van Parys
We formulate the sparse classification problem of samples with features as a binary convex optimization problem and propose a cutting-plane algorithm to solve it exactly. F…
Sparse Hierarchical Regression with Polynomials
Dimitris Bertsimas, Bart Van Parys
We present a novel method for exact hierarchical sparse polynomial regression. Our regressor is that degree polynomial which depends on at most inputs, counting at most $\e…
Sparse High-Dimensional Regression: Exact Scalable Algorithms and Phase Transitions
Dimitris Bertsimas, Bart Van Parys
We present a novel binary convex reformulation of the sparse regression problem that constitutes a new duality perspective. We devise a new cutting plane method and provide evidenc…
Security Constrained Optimal Power Flow with Distributionally Robust Chance Constraints
Line Roald, Frauke Oldewurtel, Bart Van Parys +1
The growing amount of fluctuating renewable infeeds and market liberalization increases uncertainty in power system operation. To capture the influence of fluctuations in operation…