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Found 7 papers, 0 papers with code
VC Theory for Inventory Policies
Second, the number of parameters in a policy class may not be the correct measure of overfitting error: in fact, the class of policies defined by T time-varying base-stock levels exhibits a generalization error comparable to that of the two-parameter (s, S) policy class.
From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms
This class includes classical policies such as ERM, k-Nearest Neighbors and kernel-based policies.
Degeneracy is OK: Logarithmic Regret for Network Revenue Management with Indiscrete Distributions
We study the classical Network Revenue Management (NRM) problem with accept/reject decisions and $T$ IID arrivals.
Beyond IID: data-driven decision-making in heterogeneous environments
We then leverage this connection to quantify the performance that is achievable by Sample Average Approximation (SAA) as a function of the radius of the heterogeneity ball: for any integral probability metric, we derive bounds depending on the approximation parameter, a notion which quantifies how the interplay between the heterogeneity and the problem structure impacts the performance of SAA.
Fairness Maximization among Offline Agents in Online-Matching Markets
Examples of online and offline agents include keywords (online) and sponsors (offline) in Google Advertising; workers (online) and tasks (offline) in Amazon Mechanical Turk (AMT); riders (online) and drivers (offline when restricted to a short time window) in ridesharing.
The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification
We improve the effectiveness of propagation- and linear-optimization-based neural network verification algorithms with a new tightened convex relaxation for ReLU neurons.
Inventory Balancing with Online Learning
We overcome both the challenges of model uncertainty and customer heterogeneity by judiciously synthesizing two algorithmic frameworks from the literature: inventory balancing, which "reserves" a portion of each resource for high-reward customer types which could later arrive, and online learning, which shows how to "explore" the resource consumption distributions of each customer type under different actions.
