Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Contextual Dynamic Pricing with Heterogeneous Buyers
Authors: Thodoris Lykouris, Sloan Nietert, Princewill Okoroafor, Chara Podimata, Julian Zimmert
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a contextual pricing algorithm based on optimistic posterior sampling with regret e O(K d T), which we prove to be tight in d and T up to logarithmic terms. Finally, we refine our analysis for the non-contextual pricing case, proposing a variance-aware zooming algorithm that achieves the optimal dependence on K . Our main algorithm achieves a regret bound of O(K d T), optimal up to a O( K ) factor and logarithmic terms. Our analysis bounds the disagreement coefficient by leveraging a novel decomposition lemma for aggregate demand functions with K breakpoints, thereby ensuring an efficient explorationexploitation tradeoff. |
| Researcher Affiliation | Collaboration | Thodoris Lykouris MIT EMAIL Sloan Nietert EPFL EMAIL Princewill Okoroafor Harvard University EMAIL Chara Podimata MIT EMAIL Julian Zimmert Google EMAIL |
| Pseudocode | Yes | ALGORITHM 1: OPS: Contextual Pricing with a Finite Model Class, ALGORITHM 2: Perturbed OPS (POPS) for Contextual Pricing with Infinite Model Class, ALGORITHM 3: GOPS: Generalized OPS for Contextual Pricing with Finite Model Class, ALGORITHM 4: Zoom V: Variance-Aware Zooming for Non-Contextual Pricing, ALGORITHM 5: Contextual Pricing with Ex-Post Type Identification |
| Open Source Code | No | The paper does not contain any explicit statements or links regarding the release of source code for the methodology described. |
| Open Datasets | No | The paper describes a theoretical framework for contextual dynamic pricing with heterogeneous buyers and does not mention the use of any specific datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, therefore no dataset splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or computational results that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup or implementation details that would list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and regret bounds, without detailing a specific experimental setup, hyperparameters, or training configurations. |