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..
Inverse Optimization via Learning Feasible Regions
Authors: Ke Ren, Peyman Mohajerin Esfahani, Angelos Georghiou
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Synthetic experiments and two power system applications, including comparisons with state-of-the-art approaches, showcase and validate the proposed approach. |
| Researcher Affiliation | Collaboration | 1Amazon 2University of Toronto and Delft University of Technology 3University of Cyprus. Correspondence to: Ke Ren <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Gradient descent based algorithm for (10) using predictability loss |
| Open Source Code | No | No explicit statement or link indicating the release of source code for the methodology described in this paper was found. |
| Open Datasets | Yes | We apply our methods on an instance of a power system described in (Bampou & Kuhn, 2011). ... We further investigate the performance of the proposed approach using the larger IEEE 14-bus system (Leon et al., 2020). |
| Dataset Splits | Yes | We generate Ntrain = 100 data points for training and Ntest = 200 data points for testing, by generating signals uniformly at random from scost 1 [0.2, 1], scost 2 [0.2, 0.5], scost 3 [1, 2] and sdemand 1 [0.3, 1.5], sdemand 2 [0.36, 1.8], sdemand 3 [0.42, 2.1], sdemand 4 [0.48, 2.4] and sdemand 5 [0.54, 2.7], and solving problem (14) to obtain pairs {si, xi}N=100 i=1 . |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. It mentions Gurobi, which is a solver, not hardware. |
| Software Dependencies | Yes | We use the hypothesis (7) where Z is the unit simplex of dimension p {3, 6, 9} and solve problems (10) using the non-convex quadratic solver of Gurobi v11.0.3 with a time limit of 1800 seconds. |
| Experiment Setup | Yes | We use the hypothesis (7) where Z is the unit simplex of dimension p {3, 6, 9} using the Adaptive Smoothing Algorithm 2 with a limit of 3000 iterations. ... The initial values are ϵ1 = ϵ2 = 1, and every time the change in the values of PN i=1 γs1 2 and PN i=1 γs2 2 are less than 0.01/10(log2(ϵ1)+1), we multiply the parameters ϵ1 and ϵ2 by 2. |