EDBT 2026 Demo / reviewers in the wild / expert
Laurence Bisht
dblp:68/6518
· DBLP profile ↗
3ranked-venue papers
3as first author
0since 2021 · last 2008
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 2 · 2 first-authorArtificial intelligence and machine learning · 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.
| Theoretical computer science
1 paper |
Computational complexity · 100% | |
| Artificial intelligence
1 paper |
Learning theory · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › computational learning theory
exact learning |
0.1 | 1 | 2006 | On Optimal Learning Algorithms for Multiplicity Automata · COLT 2006 |
Computational complexity
learning theory |
0.0 | 1 | 2004 | Learning with Errors in Answers to Membership Queries · FOCS 2004 |
Computational complexity › lattice problems
learning with errors |
0.0 | 1 | 2004 | Learning with Errors in Answers to Membership Queries · FOCS 2004 |
Computational complexity › learning theory › exact learning
membership query learning |
0.0 | 1 | 2004 | Learning with Errors in Answers to Membership Queries · FOCS 2004 |
Methods — techniques the papers use, named apart from their topics
query learning · 0.1equivalence queries · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Learning with errors in answers to membership queries
Laurence Bisht, Nader H. Bshouty, Lawrance Khoury |
J. Comput. Syst. Sci. | 1 |
| 2006 | On Optimal Learning Algorithms for Multiplicity Automata
Laurence Bisht, Nader H. Bshouty, Hanna Mazzawi |
COLT | 1 |
| 2004 | Learning with Errors in Answers to Membership QueriesabstractWe study the learning models defined by Angluin et al. (1997): learning with equivalence and limited membership queries and learning with equivalence and malicious membership queries. We show that if a class of concepts that is closed under projection is learnable in polynomial time using equivalence and (standard) membership queries then it is learnable in polynomial time in the above models. This closes the open problems by Angluin et al. (1997). Our algorithm can also handle errors in the equivalence queries. Laurence Bisht, Nader H. Bshouty, Lawrance Khoury |
FOCS | 1 |