Revenue Optimization against Strategic Buyers

Authors: Mehryar Mohri, Andres Munoz

NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We present a revenue optimization algorithm for posted-price auctions when facing a buyer with random valuations who seeks to optimize his γ-discounted surplus. In order to analyze this problem we introduce the notion of -strategic buyer, a more natural notion of strategic behavior than what has been considered in the past. We improve upon the previous state-of-the-art and achieve an optimal regret bound in O(log T + 1/ log(1/γ)) when the seller selects prices from a finite set and provide a regret bound in e O(T + T 1/4/ log(1/γ)) when the prices offered are selected out of the interval [0, 1].
Researcher Affiliation Collaboration Mehryar Mohri Courant Institute of Mathematical Sciences 251 Mercer Street New York, NY, 10012 Andr es Mu noz Medina Google Research 111 8th Avenue New York, NY, 10011
Pseudocode No The paper describes algorithms and mathematical formulations but does not include structured pseudocode blocks or clearly labeled algorithm figures.
Open Source Code No The paper does not provide any concrete access to source code or explicitly state that code is made available.
Open Datasets No The paper is theoretical and does not mention specific public datasets used for training or provide access information for any dataset.
Dataset Splits No The paper is theoretical and does not mention validation dataset splits.
Hardware Specification No The paper is theoretical and does not describe any specific hardware used for experiments.
Software Dependencies No The paper is theoretical and does not mention specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations.