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Alex Buna-Marginean

dblp:359/3835 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0005-0750-445XORCID · reported

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

Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021

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 · 67% Automata and formal languages · 33%
Software engineering, system software, and programming languages
1 paper
Program synthesis and code generation · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational complexity › learning theory
exact learning
0.812024
On Learning Polynomial Recursive Programs · Proc. ACM Program. Lang. 2024
Computational complexity
learning theory
0.812024
On Learning Polynomial Recursive Programs · Proc. ACM Program. Lang. 2024
Automata and formal languages
weighted automata
0.812024
On Learning Polynomial Recursive Programs · Proc. ACM Program. Lang. 2024

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

polynomial-time learning · 1.5MAT learning model · 1.5
YearPublicationVenuePosition
2024 On Learning Polynomial Recursive Programs
abstract
We introduce the class of P-finite automata. These are a generalisation of weighted automata, in which the weights of transitions can depend polynomially on the length of the input word. P-finite automata can also be viewed as simple tail-recursive programs in which the arguments of recursive calls can non-linearly refer to a variable that counts the number of recursive calls. The nomenclature is motivated by the fact that over a unary alphabet P-finite automata compute so-called P-finite sequences, that is, sequences that satisfy a linear recurrence with polynomial coefficients. Our main result shows that P-finite automata can be learned in polynomial time in Angluin’s MAT exact learning model. This generalises the classical results that deterministic finite automata and weighted automata over a field are respectively polynomial-time learnable in the MAT model.
Alex Buna-Marginean, Vincent Cheval, Mahsa Shirmohammadi, James Worrell 0001
Proc. ACM Program. Lang.1