Reproducibility Index

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 with Predictions: Improved Frugality for Facility Location

Authors: Eric Balkanski, Nicholas DeFilippis, Vasilis Gkatzelis, Xizhi Tan

NeurIPS 2025 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical The paper does not include experiments. All results are entirely theoretical.
Researcher Affiliation Academia Eric Balkanski Columbia University EMAIL Nicholas De Filippis New York University EMAIL Vasilis Gkatzelis Drexel University, Archimedes, Athena R.C., Greece EMAIL Xizhi Tan Stanford University EMAIL
Pseudocode Yes Auction 1: PREDICTEDLIMITS Input: Set of clients U, set of facilities L, parameter Ο΅, predicted opening costs Λ†oβ„“for all β„“ L Output: Selected facility subset S and threshold payments for each facility β„“ S Auction 2: ERRORTOLERANT Input: Clients U, facilities L, predicted costs Λ†oβ„“for all β„“ L, parameter Ο΅ 2, error-tolerance Ξ» 0 Output: Chosen facilities S and threshold payments for each β„“ S
Open Source Code No The paper does not include any experiments, nor does it use on data or code.
Open Datasets No The paper does not include any experiments, nor does it use on data or code.
Dataset Splits No The paper does not include experiments.
Hardware Specification No The paper does not include experiments.
Software Dependencies No The paper does not include experiments.
Experiment Setup No The paper does not include experiments.