82 citations · 168 across the 21 of their papers we have counts for
23 papers
Multi-Armed Bernoulli Bandits via Minimax Single-Arm Stopping
Huikang Liu, Zhengchao Wang, Daniel Kuhn +1
We develop an index policy for finite-horizon Bernoulli multi-armed bandits from minimax solutions to single-arm bandit (SAB) problems. Each SAB problem involves choosing between a…
A Shrinkage Path Heuristic for Wasserstein Distributionally Robust Optimization
Lingjun Meng, Ryan Cory-Wright, Wolfram Wiesemann
Wasserstein distributionally robust optimization (DRO) is a versatile and widely adopted framework for decision-making under uncertainty, yet its standard deterministic reformulati…
Efficient Algorithms for Robust Markov Decision Processes with -Rectangular Ambiguity Sets
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann
Robust Markov decision processes (MDPs) have attracted significant interest due to their ability to protect MDPs from poor out-of-sample performance in the presence of ambiguity. I…
Don't Look Back in Anger: Wasserstein Distributionally Robust Optimization with Nonstationary Data
Dominic S. T. Keehan, Edward J. Anderson, Wolfram Wiesemann
We study data-driven decision problems where historical observations are generated by a time-evolving distribution whose consecutive shifts are bounded in Wasserstein distance. We…
Distributionally Robust Optimization
Daniel Kuhn, Soroosh Shafiee, Wolfram Wiesemann
Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncer…
A Note on Piecewise Affine Decision Rules for Robust, Stochastic, and Data-Driven Optimization
Simon Thomä, Maximilian Schiffer, Wolfram Wiesemann
Multi-stage decision-making under uncertainty, where decisions are taken under sequentially revealing uncertain problem parameters, is often essential to faithfully model manageria…
An MILP-Based Solution Scheme for Factored and Robust Factored Markov Decision Processes
Huikang Liu, Wolfram Wiesemann, Man-Chung Yue
Factored Markov decision processes (MDPs) are a prominent paradigm within the artificial intelligence community for modeling and solving large-scale MDPs whose rewards and dynamics…
It's All in the Mix: Wasserstein Classification and Regression with Mixed Features
Reza Belbasi, Aras Selvi, Wolfram Wiesemann
Problem definition: A key challenge in supervised learning is data scarcity, which can cause prediction models to overfit to the training data and perform poorly out of sample. A c…
Streamlining Energy Transition Scenarios to Key Policy Decisions
Florian Joseph Baader, Stefano Moret, Wolfram Wiesemann +2
Uncertainties surrounding the energy transition often lead modelers to present large sets of scenarios that are challenging for policymakers to interpret and act upon. An alternati…
Differential Privacy via Distributionally Robust Optimization
Aras Selvi, Huikang Liu, Wolfram Wiesemann
In recent years, differential privacy has emerged as the de facto standard for sharing statistics of datasets while limiting the disclosure of private information about the involve…
On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls
Zhi Chen, Daniel Kuhn, Wolfram Wiesemann
Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given…
Wasserstein Logistic Regression with Mixed Features
Aras Selvi, Mohammad Reza Belbasi, Martin B Haugh +1
Recent work has leveraged the popular distributionally robust optimization paradigm to combat overfitting in classical logistic regression. While the resulting classification schem…
A Unified Theory of Robust and Distributionally Robust Optimization via the Primal-Worst-Equals-Dual-Best Principle
Jianzhe Zhen, Daniel Kuhn, Wolfram Wiesemann
Robust and distributionally robust optimization are modeling paradigms for decision-making under uncertainty where the uncertain parameters are only known to reside in an uncertain…
A Planner-Trader Decomposition for Multi-Market Hydro Scheduling
Kilian Schindler, Napat Rujeerapaiboon, Daniel Kuhn +1
Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout in Germany and the steady growth in photovoltaic capacity. Th…
Partial Policy Iteration for L1-Robust Markov Decision Processes
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann
Robust Markov decision processes (MDPs) allow to compute reliable solutions for dynamic decision problems whose evolution is modeled by rewards and partially-known transition proba…
On Linear Optimization over Wasserstein Balls
Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann
Wasserstein balls, which contain all probability measures within a pre-specified Wasserstein distance to a reference measure, have recently enjoyed wide popularity in the distribut…
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue +2
The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications.…
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue +2
A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these…
Data-Driven Chance Constrained Programs over Wasserstein Balls
Zhi Chen, Daniel Kuhn, Wolfram Wiesemann
We provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constr…
K-Adaptability in Two-Stage Mixed-Integer Robust Optimization
Anirudh Subramanyam, Chrysanthos E. Gounaris, Wolfram Wiesemann
We study two-stage robust optimization problems with mixed discrete-continuous decisions in both stages. Despite their broad range of applications, these problems pose two fundamen…
Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization
Napat Rujeerapaiboon, Kilian Schindler, Daniel Kuhn +1
Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters. To mitigate both short…
Scenario Reduction Revisited: Fundamental Limits and Guarantees
Napat Rujeerapaiboon, Kilian Schindler, Daniel Kuhn +1
The goal of scenario reduction is to approximate a given discrete distribution with another discrete distribution that has fewer atoms. We distinguish continuous scenario reduction…
Chebyshev Inequalities for Products of Random Variables
Napat Rujeerapaiboon, Daniel Kuhn, Wolfram Wiesemann
We derive sharp probability bounds on the tails of a product of symmetric non-negative random variables using only information about their first two moments. If the covariance matr…