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.
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 | Input Tokens: 27,678 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,223 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 | 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 Λoβfor 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 Λoβfor 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. |