7 papers · 1 filter
Multi-Dimensional Matching
Irene Aldridge
We study a matching mechanism where agents and objects are described by features rather than complete rankings. A single spectral projection reduces the problem to a one-dimensiona…
Eigenvalue-Decomposition Cost Denoising for Predict-then-Optimize
Henry Aldridge-Krawciw, Irene Aldridge
Predict-then-optimize methods learn a mapping from features to unknown edge costs. They then solve the shortest-path problem on the predicted costs. We study a feature-free alterna…
Optimizing Regret
Irene Aldridge
Building on the identity that expected regret equals the covariance between costs and decisions, this paper develops a derivative theory of the covariance regret functional. We der…
Evaluating AI Investment Strategies
Irene Aldridge
We study the problem of auditing a black-box algorithmic decision-maker from observable inputs and outputs alone. Our main result is an exact decomposition: under precisely charact…
Regret Equals Covariance: A Closed-Form Characterization for Stochastic Optimization
Irene Aldridge
Quantifying regret typically involves repeatedly re-solving a stochastic optimization problem across simulated scenarios. Such an approach is costly: the cost scales with the numbe…
Scaling the Queue: Reinforcement Learning for Equitable Call Classification Capacity in NYC Municipal Complaint Systems
Irene Aldridge, Ellie Bae, Siddhesh Darak +25
Municipal 311 call centers and complaint intake systems face a structural mismatch between incoming volume and classification capacity. The staff and heuristics available to triage…
Fast TTC Computation
Irene Aldridge
This paper proposes a fast Markov Matrix-based methodology for computing Top Trading Cycles (TTC) that delivers O(1) computational speed, that is speed independent of the number of…