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Arsenii Vanunts

dblp:220/3335 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 100%
Artificial intelligence
1 paper
Learning theory · 50% Reinforcement learning · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › auction theory
posted-price auctions
0.412019
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer · NeurIPS 2019
Algorithmic game theory and mechanism design
revenue maximization
0.412019
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer · NeurIPS 2019
Machine learning › Learning theory
online learning
0.112019
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer · NeurIPS 2019
Machine learning › Reinforcement learning
regret minimization
0.112019
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer · NeurIPS 2019

Methods — techniques the papers use, named apart from their topics

myerson pricing · 0.8multidimensional optimization · 0.8
YearPublicationVenuePosition
2019 Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer
abstract
We study revenue optimization pricing algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation. When the participants non-equally discount their cumulative utilities, we show that the optimal constant pricing (which offers the Myerson price) is no longer optimal. In the case of more patient seller, we propose a novel multidimensional optimization functional --- a generalization of the one used to determine Myerson's price. This functional allows to find the optimal algorithm and to boost revenue of the optimal static pricing by an efficient low-dimensional approximation. Numerical experiments are provided to support our results.
Arsenii Vanunts, Alexey Drutsa
NeurIPS1