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 [1].
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. |