14 citations · 135 across the 33 of their papers we have counts for
35 papers
Entropy Minimization for Optimization of Expensive, Unimodal Functions
Xiaohe Luo, Warren B. Powell
Maximization of an expensive, unimodal function under random observations has been an important problem in hyperparameter tuning. It features expensive function evaluations (which…
An Information-Collecting Drone Management Problem for Wildfire Mitigation
Lawrence Thul, Warren B Powell
We present a formal mathematical multi-agent modeling framework for autonomously combating a wildland fire with unmanned aerial vehicles. The problem is formulated as a collaborati…
The Information-Collecting Vehicle Routing Problem: Stochastic Optimization for Emergency Storm Response
Lina Al-Kanj, Warren B. Powell, Belgacem Bouzaiene-Ayari
We address the problem of mitigating damage to a power grid following a storm by managing a vehicle that has to be routed while simultaneously performing two tasks: learning about…
Stochastic Search for a Parametric Cost Function Approximation: Energy storage with rolling forecasts
Saeed Ghadimi, Warren B. Powell
Rolling forecasts have been almost overlooked in the renewable energy storage literature. In this paper, we provide a new approach for handling uncertainty not just in the accuracy…
The Parametric Cost Function Approximation: A new approach for multistage stochastic programming
Warren B Powell, Saeed Ghadimi
The most common approaches for solving multistage stochastic programming problems in the research literature have been to either use value functions ("dynamic programming") or scen…
Stochastic Optimization for Vaccine and Testing Kit Allocation for the COVID-19 Pandemic
Lawrence Thul, Warren Powell
The pandemic caused by the SARS-CoV-2 virus has exposed many flaws in the decision-making strategies used to distribute resources to combat global health crises. In this paper, we…
Optimal Learning for Sequential Decisions in Laboratory Experimentation
Kristopher Reyes, Warren B Powell
The process of discovery in the physical, biological and medical sciences can be painstakingly slow. Most experiments fail, and the time from initiation of research until a new adv…
On State Variables, Bandit Problems and POMDPs
Warren B Powell
State variables are easily the most subtle dimension of sequential decision problems. This is especially true in the context of active learning problems (bandit problems") where de…
Risk Directed Importance Sampling in Stochastic Dual Dynamic Programming with Hidden Markov Models for Grid Level Energy Storage
Joseph L. Durante, Juliana Nascimento, Warren B. Powell
Power systems that need to integrate renewables at a large scale must account for the high levels of uncertainty introduced by these power sources. This can be accomplished with a…
Reinforcement Learning via Parametric Cost Function Approximation for Multistage Stochastic Programming
Saeed Ghadimi, Raymond T. Perkins, Warren B. Powell
The most common approaches for solving stochastic resource allocation problems in the research literature is to either use value functions ("dynamic programming") or scenario trees…
From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions
Warren B Powell
There are over 15 distinct communities that work in the general area of sequential decisions and information, often referred to as decisions under uncertainty or stochastic optimiz…
Zeroth-order Stochastic Compositional Algorithms for Risk-Aware Learning
Dionysios S. Kalogerias, Warren B. Powell
We present , the first zeroth-order algorithm for (weakly-)convex mean-semideviation-based risk-aware learning, which is also the first three-level zerot…
Approximate Dynamic Programming for Planning a Ride-Sharing System using Autonomous Fleets of Electric Vehicles
Lina Al-Kanj, Juliana Nascimento, Warren B. Powell
Within a decade, almost every major auto company, along with fleet operators such as Uber, have announced plans to put autonomous vehicles on the road. At the same time, electric v…
Recursive Optimization of Convex Risk Measures: Mean-Semideviation Models
Dionysios S. Kalogerias, Warren B. Powell
We develop recursive, data-driven, stochastic subgradient methods for optimizing a new, versatile, and application-driven class of convex risk measures, termed here as mean-semidev…
Reinforcement Learning for Dynamic Bidding in Truckload Markets: an Application to Large-Scale Fleet Management with Advance Commitments
Yingfei Wang, Juliana Martins Do Nascimento, Warren Powell
Truckload brokerages, a $100 billion/year industry in the U.S., plays the critical role of matching shippers with carriers, often to move loads several days into the future. Broker…
Backward Approximate Dynamic Programming with Hidden Semi-Markov Stochastic Models in Energy Storage Optimization
Joseph L. Durante, Juliana Nascimento, Warren B. Powell
We consider an energy storage problem involving a wind farm with a forecasted power output, a stochastic load, an energy storage device, and a connection to the larger power grid w…
Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks
Yingfei Wang, Chu Wang, Warren Powell
We consider the problem of sequentially making decisions that are rewarded by "successes" and "failures" which can be predicted through an unknown relationship that depends on a pa…
MOLTE: a Modular Optimal Learning Testing Environment
Yingfei Wang, Warren Powell
We address the relative paucity of empirical testing of learning algorithms (of any type) by introducing a new public-domain, Modular, Optimal Learning Testing Environment (MOLTE)…
Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds
Daniel R. Jiang, Lina Al-Kanj, Warren B. Powell
Monte Carlo Tree Search (MCTS), most famously used in game-play artificial intelligence (e.g., the game of Go), is a well-known strategy for constructing approximate solutions to s…
Stochastic Optimization with Parametric Cost Function Approximations
Raymond T. Perkins, Warren B. Powell
A widely used heuristic for solving stochastic optimization problems is to use a deterministic rolling horizon procedure, which has been modified to handle uncertainty (e.g. buffer…
Optimal Learning for Stochastic Optimization with Nonlinear Parametric Belief Models
Xinyu He, Warren B. Powell
We consider the problem of estimating the expected value of information (the knowledge gradient) for Bayesian learning problems where the belief model is nonlinear in the parameter…
An optimal learning method for developing personalized treatment regimes
Yingfei Wang, Warren Powell
A treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across…
Finite-time Analysis for the Knowledge-Gradient Policy
Yingfei Wang, Warren Powell
We consider sequential decision problems in which we adaptively choose one of finitely many alternatives and observe a stochastic reward. We offer a new perspective of interpreting…
The Information-Collecting Vehicle Routing Problem: Stochastic Optimization for Emergency Storm Response
Lina Al-Kanj, Warren B. Powell, Belgacem Bouzaiene-Ayari
Utilities face the challenge of responding to power outages due to storms and ice damage, but most power grids are not equipped with sensors to pinpoint the precise location of the…