Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers
Authors: Mehryar Mohri, Andres Munoz
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also report the results of empirical evaluations comparing our algorithm with the previous state of the art and show a consistent exponential improvement in several different scenarios. 6 Empirical results In this section, we present the result of simulations comparing the monotone algorithm and our algorithm PFSr. |
| Researcher Affiliation | Collaboration | Mehryar Mohri Courant Institute and Google Research 251 Mercer Street New York, NY 10012 mohri@cims.nyu.edu Andres Mu noz Medina Courant Institute 251 Mercer Street New York, NY 10012 munoz@cims.nyu.edu |
| Pseudocode | Yes | Algorithm 1 Family of monotone algorithms. Algorithm 2 Definition of Ar. |
| Open Source Code | No | The paper does not provide an explicit statement or link regarding the public release of the source code for the described methodology. |
| Open Datasets | No | The paper describes generating data for simulations (e.g., 'given a buyer s valuation v, a discrete set of false valuations bv were selected out of the set {.03, .06, . . . , v}') but does not provide access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The experiments were carried out as follows: given a buyer s valuation v, a discrete set of false valuations bv were selected out of the set {.03, .06, . . . , v}. Both algorithms were run against a buyer making the seller believe her valuation is bv instead of v. The value of bv achieving the best utility for the buyer was chosen and the regret for both algorithms is reported in Figure 2. We considered two sets of experiments. First, the value of parameter γ was left unknown to both algorithms and the value of r was set to log(T). ... For the second set of experiments, we allowed access to the parameter γ to both algorithms. The value of r was chosen optimally based on the results of Theorem 1 and the parameter β of monotone was set to 1 1/ p TTγ to ensure regret in O( p T). |