Feature-Based Online Bilateral Trade
Authors: Solenne Gaucher, Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Vianney Perchet
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We characterize the regret regimes of different settings, taking as a baseline the best context-dependent prices in hindsight. First, in the setting in which the learner has two-bit feedback and strong budget balance constraints, we propose an algorithm with O(log T) regret. Then, we study the same set-up with noisy valuations, providing a tight e O(T 2/3) regret upper bound. Finally, we show that loosening budget balance constraints allows the learner to operate under more restrictive feedback. |
| Researcher Affiliation | Collaboration | The authors are affiliated with 'CMAP, École polytechnique, IPP', 'Bocconi University', 'Politecnico di Milano' (academic institutions) and 'Criteo AI Lab' (an industry lab), indicating a collaboration between academia and industry. |
| Pseudocode | Yes | The paper contains several algorithm blocks, explicitly labeled as 'Algorithm 1 ELLIPSOIDPRICING FOR BILATERAL TRADE (EP-BT)', 'Algorithm 2 ESTIMATION SUBROUTINES', 'Algorithm 3 EXPLORE-OR-COMMIT FOR BILATERAL TRADE (EOC-BT)', and 'Algorithm 4 SCOUTING BANDIT WITH INFORMATION POOLING (SBIP)'. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code, links to repositories, or mentions of code in supplementary materials for the methodology described. |
| Open Datasets | No | The paper introduces a theoretical model: 'We introduce the feature-based online bilateral trade problem, in which the learner observes a feature vector xt Rd before posting prices for round t.' It does not mention the use of any specific publicly available datasets for experiments. |
| Dataset Splits | No | The paper is theoretical and focuses on algorithm design and regret analysis, not empirical experiments. Therefore, it does not provide any training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical, analyzing algorithms and their regret bounds. It does not describe empirical experiments or specify any hardware used for computations. |
| Software Dependencies | No | The paper is theoretical, providing algorithms and mathematical analysis. It does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical, focusing on the design and analysis of algorithms for feature-based online bilateral trade, including regret bounds. It does not describe any empirical experiments or provide details such as hyperparameters or system-level training settings. |