Robust Assortment Optimization from Observational Data

arXiv:2602.10696 · stat.ML, cs.LG, math.OC, math.ST, stat.TH · Submitted 2026-02-11 · Read on arXiv

stat.ML, cs.LG, math.OC, math.ST, stat.TH

Submitted: 2026-02-11

Updated: 2026-08-28

Terminology

• Robustness — 19× in this paper · explained in 5 episodes
• Reinforcement Learning — 14× in this paper · explained in 4 episodes
• Sample Complexity — 12× in this paper · explained in 15 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Sample Efficiency — 6× in this paper · explained in 4 episodes
• Computational Tractability — 4× in this paper · explained in 2 episodes
• Distribution Shift — 4× in this paper · explained in 5 episodes
• Theoretical Guarantees — 4× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Causal Inference — 1× in this paper · explained in 4 episodes
• Contextual Bandits — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 9 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Q-learning — 1× in this paper · explained in 3 episodes
• Regret — 1× in this paper · explained in 3 episodes

Sources

• PASTA: A Unified Framework for Offline Assortment Learning
• Learning an Optimal Assortment Policy under Observational Data
• Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality
• Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
• Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms
• Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization
• Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes

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