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David Parkes

8 accepted papers

2024

Strategic Recommendation: Revenue Optimal Matching for Online Platforms (Student Abstract)

AAAI 2024technical

We consider a platform in a two-sided market with unit-supply sellers and unit-demand buyers. Each buyer can transact with a subset of sellers it knows off platform and another seller that the platform recommends. Given the choice of sellers, transactions and prices form a competitive equilibrium. T…

Cited by 0SourcePDFScholar
2023

Reinforcement Learning with Stepwise Fairness Constraints

AISTATS 2023poster

AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to automated decision making. Moreover, many settings are dynamic, with populations responding to sequential decision policies. We introduce the study o…

Cited by 15SourcePDFScholar
2021

Learning Representations by Humans, for Humans

ICML 2021spotlight

When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with improving machine accuracy. Here we propose a framework to directly support human decision-making, in which the role of machi…

Cited by 39SourcePDFScholar
2021

Reinforcement Learning of Sequential Price Mechanisms

AAAI 2021technical

We introduce the use of reinforcement learning for indirect mechanisms, working with the existing class of sequential price mechanisms, which generalizes both serial dictatorship and posted price mechanisms and essentially characterizes all strongly obviously strategyproof mechanisms. Learning an op…

2019

Optimal Auctions through Deep Learning

ICML 2019oral

Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30-40 years of intense research the problem remains unsolved for seemingly simple multi-bidder, multi-item setti…