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..
Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders
Authors: Alexey Drutsa
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a novel algorithm that has strategic regret upper bound of O(log log T) for worst-case valuations. This pricing is based on our novel transformation that upgrades an algorithm designed for the setup with a single buyer to the multi-buyer case. We provide theoretical guarantees on the ability of a transformed algorithm to learn the valuation of a strategic buyer, which has uncertainty about the future due to the presence of rivals. ... We cannot directly apply the optimal RPPA algorithms (Drutsa, 2017b; 2018), because our bidders have incomplete information in the game, while the proofs of optimality of these algorithms strongly rely on complete information. |
| Researcher Affiliation | Collaboration | Alexey Drutsa 1 2 1Yandex, Moscow, Russia 2Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Moscow, Russia. |
| Pseudocode | Yes | Algorithm 1 Pseudo-code of a div-transformation div M(A1, sr) of a RPPA algorithm A1 ARPPA. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | This paper is theoretical and does not describe experiments performed on a dataset, thus no information on dataset availability is provided. |
| Dataset Splits | No | This paper is theoretical and does not describe experiments with data. Therefore, it does not provide training/validation/test dataset splits. |
| Hardware Specification | No | This paper focuses on theoretical algorithm design and analysis. It does not describe any computational experiments or specify hardware used. |
| Software Dependencies | No | This paper describes a theoretical algorithm and provides pseudocode (Algorithm 1) but does not mention any specific software dependencies or version numbers required for implementation or experimentation. |
| Experiment Setup | No | This paper is theoretical and does not describe empirical experiments with specific hyperparameter values or system-level training settings. |