No-Regret Learning in Partially-Informed Auctions
Authors: Wenshuo Guo, Michael Jordan, Ellen Vitercik
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formalize this problem as an online learning task where the goal is to have low regret with respect to a myopic oracle that has perfect knowledge of the distribution over items and the seller s masking function. When the distribution over items is known to the buyer and the mask is a Sim Hash function mapping Rd to {0, 1}ℓ, our algorithm has regret O((Tdℓ) 1/2). In a fully agnostic setting when the mask is an arbitrary function mapping to a set of size n and the prices are stochastic, our algorithm has regret O((Tn) 1/2). |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering & Computer Sciences, University of California, Berkeley, USA 2Department of Statistics, University of California, Berkeley, USA 3Department of Management Science & Engineering and Department of Computer Science, Stanford University, USA. |
| Pseudocode | Yes | Algorithm 1 Explore-then-Commit (Known Distribution); Algorithm 2 Exp4.VC with an unknown distribution |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public availability of its source code. |
| Open Datasets | No | This paper is theoretical and does not involve experimental training on a specific, publicly available dataset. |
| Dataset Splits | No | This is a theoretical paper and does not describe experimental validation involving dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. No hardware specifications were found. |
| Software Dependencies | No | The paper refers to specific algorithms (e.g., Exp4.VC, Lovász & Vempala's Integration Algorithm) but does not list any software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not provide details about an experimental setup, hyperparameters, or training configurations. |