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..
No-Regret Learning in Partially-Informed Auctions
Authors: Wenshuo Guo, Michael Jordan, Ellen Vitercik
ICML 2022 | Venue PDF | 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. |