Devesh Parekh

dblp:51/5502 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0003-5098-3164ORCID · verified

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

Databases, data management, data science and information retrieval · 2 · 1 since 2021Artificial 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
Reinforcement learning · 50% Planning, search and constraint satisfaction · 50%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 50% Computational finance and economics · 50%
Databases, data mining, and information retrieval
2 papers
Recommender systems · 85% Distributed and cloud data management · 15%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
bandit
0.812024
Practical Bandits: An Industry Perspective · WSDM 2024
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
decision making under uncertainty
0.812024
Practical Bandits: An Industry Perspective · WSDM 2024
Computational social science and digital humanities › marketing
advertising
0.212024
Practical Bandits: An Industry Perspective · WSDM 2024
Computational finance and economics › market design
auction design
0.212024
Practical Bandits: An Industry Perspective · WSDM 2024
Recommender systems
online recommendation
0.212024
Practical Bandits: An Industry Perspective · WSDM 2024
Distributed systems
distributed coordination and fault tolerance
0.012003
GridDB: A Database Interface to the Grid · SIGMOD Conference 2003
Distributed systems
grid computing
0.012003
GridDB: A Database Interface to the Grid · SIGMOD Conference 2003

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

exploration-exploitation trade-off · 2.3
YearPublicationVenuePosition
2024 Practical Bandits: An Industry Perspective
abstract
The bandit paradigm provides a unified modeling framework for problems that require decision-making under uncertainty. Because many business metrics can be viewed as rewards (a.k.a. utilities) that result from actions, bandit algorithms have seen a large and growing interest from industrial applications, such as search, recommendation and advertising. Indeed, with the bandit lens comes the promise of direct optimisation for the metrics we care about.
Bram van den Akker, Olivier Jeunen, Ying Li 0124, Ben London 0001, Zahra Nazari, Devesh Parekh
WSDM6
2003 GridDB: A Database Interface to the Grid
abstract
No abstract available.
David T. Liu, Michael J. Franklin, Devesh Parekh
SIGMOD Conference3