Theoretical models for learning from multiple, heterogenous and strategic agents

Padmanabhan, D (2017) Theoretical models for learning from multiple, heterogenous and strategic agents. In: 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017, 8 - 12 May 2017, Sao Paulo, pp. 1847-1848.

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Abstract

With the advent of internet enabled hand-held mobile devices, there is a proliferation of user generated data. Often there is a wealth of useful knowledge embedded within this data and machine learning techniques can be used to extract the information. However, as much of this data is user generated, it suffers from subjectivity. Any machine learning techniques used in this context should address the subjectivity in a principled way. We broadly study three problems in the context of learning from multiple agents, (1) Multi-label classification (2) Active Linear Regression (3) Sponsored Search Auctions.

Item Type:	Conference Paper
Publication:	Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Publisher:	International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Additional Information:	The copyright for this article belongs to International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Keywords:	Classification (of information); Learning algorithms; Learning systems; Multi agent systems, Machine learning techniques; Multi label classification; Multiple agents; Sponsored search auctions; User-generated, Autonomous agents
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	25 Jul 2022 07:08
Last Modified:	25 Jul 2022 07:08
URI:	https://eprints.iisc.ac.in/id/eprint/74685

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