arXiv Machine Learning
Jun 10
Ambiguous Strategic Classification
arXiv:2606. 10137v1 Announce Type: new Abstract: A common assumption in strategic classification is that the classifier is public knowledge.
Robust Strategic Classification under Decision-Dependent Cost Uncertainty
arXiv:2606. 30136v1 Announce Type: new Abstract: Humans facing algorithmic decision systems have been found to ``game'' them by altering their input data (at a cost to them) in order to favorably change the algorithmic outcomes they receive (at a cost to the algorithm).
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arXiv Machine Learning
Jun 29
Non-Linear Strategic Classification Made Practical
arXiv:2606. 28204v1 Announce Type: cross Abstract: Algorithmic developments in Strategic Classification have been mostly limited to linear classifiers in settings where the best response has a closed-form solution or can be easily approximated.
arXiv Machine Learning
1d ago
Robust Non-Clairvoyant Scheduling with Classification Models
The paper tackles the single‑machine scheduling problem of minimizing total completion time in a non‑clairvoyant setting, where job processing times are unknown until completion. It introduces a robustness framework that uses a classification model’s confusion matrix to describe uncertainty as permutations within predicted classes, avoiding the computational challenges of traditional robust metrics. The authors present an optimal non‑adaptive strategy for three robust criteria and show that adaptive and randomized algorithms can outperform it when the confusion matrix has certain structural properties.
arXiv Machine Learning
Jun 2
Linear Strategic Classification with Endogenous Improvements
arXiv:2606. 01198v1 Announce Type: new Abstract: Strategic classification studies settings in which agents respond to a deployed classifier by modifying observable features at a cost.
arXiv AI
Jun 9
Beyond Rational Illusion: Behaviorally Realistic Strategic Classification
arXiv:2605. 19674v2 Announce Type: replace Abstract: Strategic classification(SC) studies the interaction between decision models and agents who strategically manipulate their features for favorable outcomes.
arXiv Machine Learning
Aug 10
Online Conformal Prediction Beyond Feedback
arXiv:2608. 07139v1 Announce Type: new Abstract: Uncertainty quantification is essential when deploying machine learning models in safety-critical applications.
arXiv AI
Jun 12
Strategic Decision Support for AI Agents
arXiv:2606. 12587v1 Announce Type: new Abstract: Traditionally, decision support studies how humans use machine learning models to make better decisions.
arXiv Machine Learning
Jun 11
Calibrating Decision Robustness via Inverse Conformal Risk Control
arXiv:2510. 07750v3 Announce Type: replace-cross Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions.
arXiv Machine Learning
Jun 5
Conformal Risk-Averse Decision Making with Action Conditional Guarantee
arXiv:2606. 05551v1 Announce Type: cross Abstract: Reliable decision making pipelines powered by machine learning models require uncertainty quantification (UQ) methods that come with explicit safety guarantees.
arXiv AI
Jun 2
Position Paper: Post-Solve Robustness in Decision Engines: Feasible Regions and Smoothness Under Perturbations
arXiv:2606. 00002v1 Announce Type: new Abstract: Mixed-Integer Linear Programming (MILP) decision engines routinely output nominally optimal plans for high-stakes industrial systems.
arXiv Machine Learning
Jun 19
Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning
arXiv:2606. 19883v1 Announce Type: new Abstract: We study a multi-agent multi-armed bandit problem in the competitive setup with two-sided matching markets under a human centric decision making model.
arXiv Machine Learning
Aug 11
Decision-Focused Learning in Network Interdiction Games
arXiv:2608. 09036v1 Announce Type: cross Abstract: We study decision-focused learning (DFL) in shortest-path network interdiction (SPNI) games, a Stackelberg game where an interdictor (leader) strengthens the networks' arcs against attacks, while an evader (follower) who is uncertain about costs of attacking network arcs relies on a machine-learned predictor to identify the shortest path.