Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
An Online Learning Theory of Trading-Volume Maximization
Authors: Tommaso Cesari, Roberto Colomboni
ICLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 22,293 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,157 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We explore brokerage between traders in an online learning framework. ... If the traders valuations are revealed after each interaction (full-feedback), and the traders valuations cumulative distribution function (cdf) is continuous, we provide an algorithm achieving logarithmic regret and show its optimality up to constants. ... We complement our results by analyzing the implications of dropping the regularity assumptions on the unknown traders valuations cdf. |
| Researcher Affiliation | Academia | Tommaso Cesari EECS University of Ottawa Ottawa, Canada EMAIL Roberto Colomboni DEIB / Dept. of Computer Science Politecnico di Milano / University of Milan Milano, Italy EMAIL |
| Pseudocode | Yes | Algorithm 1: Follow the Empirical Median (FEM) Algorithm 2: Median Binary Search (MBS) Algorithm 3: Follow the Empirical Θ (FEΘ) |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper models traders valuations as an i.i.d. process with an unknown distribution. It does not mention the use of any specific real-world or synthetic dataset, nor does it provide any links or citations to publicly available datasets. |
| Dataset Splits | No | As no datasets are explicitly used or referenced for experiments, there is no discussion of training/test/validation splits. |
| Hardware Specification | No | This paper is theoretical in nature, focusing on algorithm design and regret analysis. It does not describe any experiments that would require specific hardware, and thus no hardware specifications are mentioned. |
| Software Dependencies | No | This paper presents theoretical algorithms and proofs. It does not mention any specific software packages, libraries, or programming languages with version numbers that would be used for implementation or experimentation. |
| Experiment Setup | No | The paper is purely theoretical, presenting algorithms and their regret analyses. There are no experimental setups, hyperparameters, or training configurations described, as no empirical evaluations are conducted. |