Steven D. Whitehead

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11ranked-venue papers

9as first author

0since 2021 · last 1997

—ORCID · none

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

Artificial intelligence and machine learning · 8 · 7 first-authorComputer networks · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

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 6 papers		Reinforcement learning · 36% Learning theory · 28% Representation and self-supervised learning · 16%
Databases, data mining, and information retrieval 1 paper		Information retrieval · 100%
Computer networks 1 paper		Internet architecture and protocols · 77% Network optimization and economics · 23%

Topics — the 11 heaviest of 13, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Machine learning › Reinforcement learning › markov decision process non-markov decision process	1	1995	Reinforcement Learning of Non-Markov Decision Processes · Artif. Intell. 1995
Machine learning › Learning theory online learning	1	1993	Online Learning with Random Representations · ICML 1993
Machine learning › Representation and self-supervised learning random representations	1	1993	Online Learning with Random Representations · ICML 1993
Machine learning › Learning theory computational complexity	1	1991	A Complexity Analysis of Cooperative Mechanisms in Reinforcement Learning · AAAI 1991
Machine learning › Reinforcement learning › value-based reinforcement learning q-learning	1	1991	Complexity and Cooperation in Q-Learning · ML 1991
Robotics › Robot navigation and mapping active perception	1	1990	Active Perception and Reinforcement Learning · ML 1990
Robotics › Motion planning and robot control › robot control architecture reactive system	1	1989	A Role for Anticipation in Reactive Systems that Learn · ML 1989
Information retrieval retrieval strategies	1	1997	GTE SuperPages: Using IR Techniques for Searching Complex Objects · VLDB 1997
Internet architecture and protocols integrated services	1	1984	Transmission Facility Sharing for Integrated Services · INFOCOM 1984
Machine learning › Reinforcement learning exploration	1	1990	Active Perception and Reinforcement Learning · ML 1990
Network optimization and economics resource allocation	1	1984	Transmission Facility Sharing for Integrated Services · INFOCOM 1984

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

reinforcement learning · 0.0q-learning · 0.0anticipatory learning · 0.0

Year	Publication	Venue	Position
1997	GTE SuperPages: Using IR Techniques for Searching Complex Objects Steven D. Whitehead, Himanshu Sinha, Michael Murphy	VLDB	1
1995	Reinforcement Learning of Non-Markov Decision Processes Steven D. Whitehead, Long Ji Lin	Artif. Intell.	1
1995	Auto-FAQ: An Experiment in Cyberspace Leveraging Steven D. Whitehead	Comput. Networks ISDN Syst.	1
1993	Online Learning with Random Representations Richard S. Sutton, Steven D. Whitehead	ICML	2
1991	A Complexity Analysis of Cooperative Mechanisms in Reinforcement Learning Steven D. Whitehead	AAAI	1
1991	Complexity and Cooperation in Q-Learning Steven D. Whitehead	ML	1
1991	Learning to Perceive and Act by Trial and Error Steven D. Whitehead, Dana H. Ballard	Mach. Learn.	1
1990	Active Perception and Reinforcement Learning Steven D. Whitehead, Dana H. Ballard	ML	1
1990	Active Perception and Reinforcement Learning abstract This paper considers adaptive control architectures that integrate active sensorimotor systems with decision systems based on reinforcement learning. One unavoidable consequence of active perception is that the agent's internal representation often confounds external world states. We call this phenomenon perceptual aliasing and show that it destabilizes existing reinforcement learning algorithms with respect to the optimal decision policy. A new decision system that overcomes these difficulties is described. The system incorporates a perceptual subcycle within the overall decision cycle and uses a modified learning algorithm to suppress the effects of perceptual aliasing. The result is a control architecture that learns not only how to solve a task but also where to focus its attention in order to collect necessary sensory information. Steven D. Whitehead, Dana H. Ballard	Neural Comput.	1
1989	A Role for Anticipation in Reactive Systems that Learn Steven D. Whitehead, Dana H. Ballard	ML	1
1984	Transmission Facility Sharing for Integrated Services John N. Daigle, Steven D. Whitehead	INFOCOM	2