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..
Emulating the Expert: Inverse Optimization through Online Learning
Authors: Andreas Bärmann, Sebastian Pokutta, Oskar Schneider
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Preliminary Computational Tests. While a full computational study is beyond the scope of this paper and left for future work, we implemented a first preliminary version of our algorithm, and we report computational results for a few select problems. and In Table 1, we show the computational results for the Integer Knapsack Problem with n = 1000 items. |
| Researcher Affiliation | Academia | 1Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany 2Georgia Institute of Technology, Atlanta, USA. |
| Pseudocode | Yes | Algorithm 1 Online Objective Function Learning |
| Open Source Code | No | The paper states: 'We have implemented our framework using python and Gurobi 7.0.1 (Gurobi Optimization, Inc., 2016).' but does not provide any link or explicit statement about making their own code open source. |
| Open Datasets | No | The paper states: 'We generated random instances for our computational results, considering T = 1000 observations for a varying number of goods n 2 {100, 500, 1000}.' It describes the generation process but does not provide access to a public dataset. |
| Dataset Splits | No | The paper describes an online learning setting where observations are revealed over time, and it does not specify explicit training, validation, and test dataset splits in a traditional sense. |
| Hardware Specification | Yes | Our preliminary computational experiments have been obtained on a Mac Book Pro (2016) with an Intel Core i5 CPU with two 2.00 GHz cores. |
| Software Dependencies | Yes | We have implemented our framework using python and Gurobi 7.0.1 (Gurobi Optimization, Inc., 2016). |
| Experiment Setup | No | The paper describes how problem instances were generated (e.g., 'The customer s unknown utility vector is chosen at random as (arbitrary) integer numbers from the interval [1, 1000] from a uniform distribution'), and refers to 'T = 1000 observations', but does not specify explicit hyperparameters or system-level training settings for their algorithm, such as a fixed learning rate or optimizer configurations. |