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..
Learning Algorithms for Second-Price Auctions with Reserve
Authors: Mehryar Mohri, Andres Munoz Medina
JMLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present novel algorithms for solving this problem and report the results of several experiments in both synthetic and real-world data demonstrating their effectiveness. |
| Researcher Affiliation | Academia | Mehryar Mohri and Andr es Mu noz Medina Courant Institute of Mathematical Sciences 251 Mercer Street New York, NY, 10012 |
| Pseudocode | Yes | Algorithm 1 Sorting |
| Open Source Code | No | The paper discusses the methodology and presents experimental results but does not provide any explicit links to source code or statements about its public release for the work described. |
| Open Datasets | Yes | We were able to procure an e Bay data set consisting of approximately 70,000 secondprice auctions of collector sport cards. The full data set can be accessed using the following URL: http: //cims.nyu.edu/ munoz/data. |
| Dataset Splits | Yes | We randomly sampled 2,000 examples for training, 2,000 examples for validation and 2,000 examples for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions the use of 'off-the-shelf QP solvers' for quadratic-programming but does not specify any particular software or version numbers. |
| Experiment Setup | Yes | For all our experiments, the parameters λ, γ and α were selected respectively from the sets {2i|i [ 5, 5]}, {0.1, 0.01, 0.001}, and {0.1, 0.2, . . . , 0.9} via validation over a set consisting of the same number of examples as the training set. Our algorithm was initialized using the best solution of the convex surrogate optimization problem. |