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 [1].
A Parametric Contextual Online Learning Theory of Brokerage
Authors: François Bachoc, Tommaso Cesari, Roberto Colomboni
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design algorithms for this problem and prove optimal theoretical regret guarantees under various standard assumptions. ... We prove a key structural result (Lemma 2.1) with two crucial consequences. ... We solve this challenging exploration-exploitation dilemma by proposing an algorithm (Algorithm 1) and prove its optimality by showing a Ld T ln T regret upper bound (Theorem 3.1) and a matching (up to a Ld T lower bound (Theorem 3.2). |
| Researcher Affiliation | Academia | 1IMT and IUF, Universit e Paul Sabatier, Toulouse, France 2EECS, University of Ottawa, Ottawa, Canada 3DEIB, Politecnico di Milano, Milano, Italy 4Department of CS, Universit a degli Studi di Milano, Milano, Italy. Correspondence to: Franc ois Bachoc <EMAIL>, Tommaso Cesari <EMAIL>, Roberto Colomboni <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 ... Algorithm 2 |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper focuses on a theoretical online learning problem and does not present experiments that utilize specific, publicly available datasets. It refers to abstract concepts like 'traders valuations' and 'contexts' without providing concrete dataset access information. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments involving specific datasets, therefore, no information regarding training/test/validation dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical, focusing on algorithm design, proofs, and regret analysis. It does not describe any experimental setup that would require specific hardware, and thus, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and describes algorithms and proofs without detailing their implementation. Therefore, it does not specify any software dependencies with version numbers needed for replication. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs, algorithms, and regret analysis. It does not include an experimental section detailing hyperparameters, training configurations, or system-level settings for any empirical evaluation. |