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..
Procurement Auctions via Approximately Optimal Submodular Optimization
Authors: Yuan Deng, Amin Karbasi, Vahab Mirrokni, Renato Paes Leme, Grigoris Velegkas, Song Zuo
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate the welfare performance and running time trade-offs of different mechanisms on a publicly available coverage problem. Our instances are constructed using a bipartite graph from SNAP (https://snap.stanford.edu/ data/wiki-Vote.html). ... We generate 10000 random instances per (n, s). ... Figure 1 shows the running time comparison of different methods and Figure 2 compares the welfare outcomes. |
| Researcher Affiliation | Collaboration | 1Google Research 2Yale University. Correspondence to: Yuan Deng <EMAIL>, Grigoris Velegkas <EMAIL>, Song Zuo <EMAIL >. |
| Pseudocode | Yes | Algorithm 1 A meta algorithm A = (G) for submodular optimization Algorithm 2 A feasible mechanism construction for a given meta algorithm A Algorithm 3 A meta algorithm Ao = (G) for online submodular optimization Algorithm 4 Descending auction with a demand oracle D and a step size of Δ Algorithm 5 Distorted greedy algorithm (Harshaw et al., 2019) Algorithm 6 Stochastic distorted greedy algorithm (Harshaw et al., 2019) Algorithm 7 Noisy distorted greedy algorithm Algorithm 8 A posted-price mechanism construction for a given meta algorithm Ao Algorithm 9 Descending auction construction for a given meta algorithm Ao and a step size of Δ Algorithm 10 A faster implementation of Algorithm 1 when G has a diminishing-return structure Algorithm 11 A faster implementation of Algorithm 2 when G has a diminishing-return structure |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | Our instances are constructed using a bipartite graph from SNAP (https://snap.stanford.edu/ data/wiki-Vote.html). |
| Dataset Splits | No | The paper describes generating '10000 random instances per (n, s)' by randomly sampling subsets and scaling costs. It does not provide specific training/test/validation splits for a machine learning model, as the evaluation is on different mechanisms over generated instances. |
| Hardware Specification | No | The paper does not mention specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments or computations. |
| Software Dependencies | No | The paper mentions solving the optimization problem using a 'mixed integer program (MIP)' but does not specify the solver or its version number. |
| Experiment Setup | Yes | To create instances with varying sizes and value-to-cost ratios, for each pair (n, s), we randomly sample subsets of the sets of size n and scale their costs using Îș U[s, s2]. We generate 10000 random instances per (n, s). |