A scalable neural network for DSIC affine maximizer auction design
Automated auction design aims to find empirically high-revenue mechanisms through
machine learning. Existing works on multi item auction scenarios can be roughly divided into …
machine learning. Existing works on multi item auction scenarios can be roughly divided into …
Neural auction: End-to-end learning of auction mechanisms for e-commerce advertising
In e-commerce advertising, it is crucial to jointly consider various performance metrics, eg,
user experience, advertiser utility, and platform revenue. Traditional auction mechanisms …
user experience, advertiser utility, and platform revenue. Traditional auction mechanisms …
On the convergence of no-regret learning dynamics in time-varying games
I Anagnostides, I Panageas… - Advances in Neural …, 2024 - proceedings.neurips.cc
Most of the literature on learning in games has focused on the restrictive setting where the
underlying repeated game does not change over time. Much less is known about the …
underlying repeated game does not change over time. Much less is known about the …
Optimal-er auctions through attention
D Ivanov, I Safiulin, I Filippov… - Advances in Neural …, 2022 - proceedings.neurips.cc
RegretNet is a recent breakthrough in the automated design of revenue-maximizing
auctions. It combines the flexibility of deep learning with the regret-based approach to relax …
auctions. It combines the flexibility of deep learning with the regret-based approach to relax …
Encoding human behavior in information design through deep learning
We initiate the study of $\textit {behavioral information design} $ through deep learning. In
information design, a $\textit {sender} $ aims to persuade a $\textit {receiver} $ to take certain …
information design, a $\textit {sender} $ aims to persuade a $\textit {receiver} $ to take certain …
A context-integrated transformer-based neural network for auction design
One of the central problems in auction design is developing an incentive-compatible
mechanism that maximizes the auctioneer's expected revenue. While theoretical …
mechanism that maximizes the auctioneer's expected revenue. While theoretical …
Learning in repeated auctions
Online auctions are one of the most fundamental facets of the modern economy and power
an industry generating hundreds of billions of dollars a year in revenue. Auction theory has …
an industry generating hundreds of billions of dollars a year in revenue. Auction theory has …
Computing optimal equilibria and mechanisms via learning in zero-sum extensive-form games
We introduce a new approach for computing optimal equilibria via learning in games. It
applies to extensive-form settings with any number of players, including mechanism design …
applies to extensive-form settings with any number of players, including mechanism design …
Differentiable economics for randomized affine maximizer auctions
A recent approach to automated mechanism design, differentiable economics, represents
auctions by rich function approximators and optimizes their performance by gradient …
auctions by rich function approximators and optimizes their performance by gradient …
A permutation-equivariant neural network architecture for auction design
Designing an incentive compatible auction that maximizes expected revenue is a central
problem in Auction Design. Theoretical approaches to the problem have hit some limits in …
problem in Auction Design. Theoretical approaches to the problem have hit some limits in …