Demo Data from SIGIR 2026 / reviewers in the wild / expert
Susanne Kaufmann
dblp:80/5985
· DBLP profile ↗
7ranked-venue papers
4as first author
0since 2021 · last 2001
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 5 · 3 first-authorArtificial intelligence and machine learning · 2 · 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
1 paper |
Learning theory · 100% | |
| Theoretical computer science
1 paper |
Automata and formal languages · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory
online learning |
0.0 | 1 | 1997 | Resource Bounded Next Value and Explanatory Identification: Learning Automata, Patterns and Polynomials On-Line · COLT 1997 |
Automata and formal languages › grammatical inference
learning automata |
0.0 | 1 | 1997 | Resource Bounded Next Value and Explanatory Identification: Learning Automata, Patterns and Polynomials On-Line · COLT 1997 |
Methods — techniques the papers use, named apart from their topics
next value learning · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Predictive learning models for concept drift
John Case, Sanjay Jain 0001, Susanne Kaufmann, Arun Sharma 0001, Frank Stephan 0001 |
Theor. Comput. Sci. | 3 |
| 2001 | Robust learning with infinite additional information
Susanne Kaufmann, Frank Stephan 0001 |
Theor. Comput. Sci. | 1 |
| 1998 | Predictive Learning Models for Concept Drift
John Case, Sanjay Jain 0001, Susanne Kaufmann, Arun Sharma 0001, Frank Stephan 0001 |
ALT | 3 |
| 1997 | Resource Bounded Next Value and Explanatory Identification: Learning Automata, Patterns and Polynomials On-Line
Susanne Kaufmann, Frank Stephan 0001 |
COLT | 1 |
| 1997 | Learning Recursive Functions from Approximations
John Case, Susanne Kaufmann, Efim B. Kinber, Martin Kummer |
J. Comput. Syst. Sci. | 2 |
| 1996 | On a Quantitative Notion of UniformityabstractOne topic arising in recent research on “Bounded Query Classes” is to consider quantitative aspects of recursion theory, and in particular various notions of parameterized recursive approximations of sets. An important question is, for which values of the parameters – depending on the type of approximation – the approximated set is necessarily recursive. Beigel's Nonspeedup Theorem, Rummer's Cardinality Theorem and Trakhtenbrot's Theorem provide answers using nonuniform constructions. This paper investigates to which extend these constructions can be made uniform. Beigel's Nonspeedup Theorem is equivalent to the statement that every branch of a recursively enumerable tree of bounded width is recursive. There is no algorithm which computes a branch from the index of the tree, but there are nontrivial positive results by weakening the requirements as follows: For some fixed number k, an algorithm is wanted which, given an index of a tree, outputs a list of k programs such that at least one of them computes a branch of the tree up to finitely many errors. What is the least k for which this works? In this paper it is shown that, for recursively enumerable trees of width at most n, the least possible k is 2n−1. For trees arising from Trakhtenbrot's Theorem with parameters m, n, the optimal value is k = n − m + 1. In addition, several other, related classes of trees are investigated. Susanne Kaufmann, Martin Kummer |
Fundam. Informaticae | 1 |
| 1995 | On a Quantitative Notion of Uniformity
Susanne Kaufmann, Martin Kummer |
MFCS | 1 |