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Jalal Kazempour

dblp:184/9701 · also S. Jalal Kazempour · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0002-5050-6611ORCID · verified

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

Artificial intelligence and machine learning · 1 · 1 since 2021

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.

Artificial intelligence
1 paper
Robot manipulation · 100%
Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › robot design
mechanism design
0.812024
Bayesian Regression Markets · J. Mach. Learn. Res. 2024
Algorithmic game theory and mechanism design
incentive mechanism
0.812024
Bayesian Regression Markets · J. Mach. Learn. Res. 2024

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

regression markets · 1.5bayesian framework · 1.5
YearPublicationVenuePosition
2024 Bayesian Regression Markets
abstract
Although machine learning tasks are highly sensitive to the quality of input data, relevant datasets can often be challenging for firms to acquire, especially when held privately by a variety of owners. For instance, if these owners are competitors in a downstream market, they may be reluctant to share information. Focusing on supervised learning for regression tasks, we develop a regression market to provide a monetary incentive for data sharing. Our mechanism adopts a Bayesian framework, allowing us to consider a more general class of regression tasks. We present a thorough exploration of the market properties, and show that similar proposals in literature expose the market agents to sizeable financial risks, which can be mitigated in our setup.
Thomas Falconer, Jalal Kazempour, Pierre Pinson
J. Mach. Learn. Res.2