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Christophe Papazian

dblp:87/3746 · DBLP profile ↗
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5ranked-venue papers
2as first author
0since 2021 · last 2004
—ORCID · none

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

Theory of computation · 4 · 2 first-authorArtificial intelligence and machine learning · 1

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.

Theoretical computer science
3 papers
Computational complexity · 48% Automata and formal languages · 26% Computational geometry · 26%
Artificial intelligence
1 paper
Learning theory · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational complexity
inductive inference		 0.012003
On the intrinsic complexity of learning recursive functions · Inf. Comput. 2003
Automata and formal languages
graph automata		 0.012002
Hyperbolic Recognition by Graph Automata · ICALP 2002
Computational geometry › metric geometry
hyperbolic geometry		 0.012002
Hyperbolic Recognition by Graph Automata · ICALP 2002
Machine learning › Learning theory
inductive inference		 0.011999
On the Intrinsic Complexity of Learning Recursive Functions · COLT 1999

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

reducibility · 0.0inductive inference · 0.0
YearPublicationVenuePosition
2004 Leader election in plane cellular automata, only with left-right global convention
Codrin M. Nichitiu, Christophe Papazian, Eric Rémila
 Theor. Comput. Sci.2
2003 On the intrinsic complexity of learning recursive functions
Sanjay Jain 0001, Efim B. Kinber, Christophe Papazian, Carl H. Smith 0001, Rolf Wiehagen
 Inf. Comput.3
2002 Hyperbolic Recognition by Graph Automata
Christophe Papazian, Eric Rémila
 ICALP1
2001 Linear Time Recognizer for Subsets of Z2
Christophe Papazian, Eric Rémila
 FCT1
1999 On the Intrinsic Complexity of Learning Recursive Functions
abstract
The intrinsic complexity of learning compares the difficulty of learning classes of objects by using some reducibility notion.For several types of learning recursive functions (learning in the limit, learning with a bounded number of mind changes, learning with anomalies), both generic complete classes are exhibited and necessary and sufficient conditions for completeness are derived.Informally, a class is complete iff both its topological structure is highly complex while its algorithmic structure is surprisingly easy.Counter-intuitively, some self-describing classes turn out to be complete.
Efim B. Kinber, Christophe Papazian, Carl H. Smith 0001, Rolf Wiehagen
 COLT2