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Found 10 papers, 0 papers with code
Potential Energy Advantage of Quantum Economy
Energy cost is increasingly crucial in the modern computing industry with the wide deployment of large-scale machine learning models and language models.
Estimating and Incentivizing Imperfect-Knowledge Agents with Hidden Rewards
On top of the agent's learning, the principal trains a parallel algorithm and faces a trade-off between consistently estimating the agent's unknown rewards and maximizing their own utility by offering adaptive incentives to lead the agent.
Active Learning in the Predict-then-Optimize Framework: A Margin-Based Approach
We develop the first active learning method in the predict-then-optimize framework.
Repeated Principal-Agent Games with Unobserved Agent Rewards and Perfect-Knowledge Agents
Motivated by a number of real-world applications from domains like healthcare and sustainable transportation, in this paper we study a scenario of repeated principal-agent games within a multi-armed bandit (MAB) framework, where: the principal gives a different incentive for each bandit arm, the agent picks a bandit arm to maximize its own expected reward plus incentive, and the principal observes which arm is chosen and receives a reward (different than that of the agent) for the chosen arm.
Quantum Computing Methods for Supply Chain Management
We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management.
Policy-based Primal-Dual Methods for Convex Constrained Markov Decision Processes
We study convex Constrained Markov Decision Processes (CMDPs) in which the objective is concave and the constraints are convex in the state-action occupancy measure.
Integrated Conditional Estimation-Optimization
In contrast to the standard approach of first estimating the distribution of uncertain parameters and then optimizing the objective based on the estimation, we propose an integrated conditional estimation-optimization (ICEO) framework that estimates the underlying conditional distribution of the random parameter while considering the structure of the optimization problem.
Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function
A significant theoretical challenge in the nonlinear setting is that there is no explicit characterization of an optimal controller for a given set of cost and system parameters.
Urban Bike Lane Planning with Bike Trajectories: Models, Algorithms, and a Real-World Case Study
We formalize the bike lane planning problem in view of the cyclists' utility functions and derive an integer optimization model to maximize the utility.
Adjusting Rate of Spread Factors through Derivative-Free Optimization: A New Methodology to Improve the Performance of Forest Fire Simulators
In practical applications, it is common that wildfire simulators do not correctly predict the evolution of the fire scar.
