EDBT 2026 Demo / reviewers in the wild / expert
A. Maylar Baksho
dblp:99/6026
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1990
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 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
1 paper |
Mathematical optimization · 70% Information theory · 30% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › signal processing
adaptive filtering |
0.0 | 1 | 1990 | Sign-sign LMS convergence with independent stochastic inputs · IEEE Trans. Inf. Theory 1990 |
Mathematical optimization › stochastic optimization › stochastic approximation
least mean squares |
0.0 | 1 | 1990 | Sign-sign LMS convergence with independent stochastic inputs · IEEE Trans. Inf. Theory 1990 |
Mathematical optimization
statistical estimation |
0.0 | 1 | 1990 | Sign-sign LMS convergence with independent stochastic inputs · IEEE Trans. Inf. Theory 1990 |
Mathematical optimization › control theory
system identification |
0.0 | 1 | 1990 | Sign-sign LMS convergence with independent stochastic inputs · IEEE Trans. Inf. Theory 1990 |
Methods — techniques the papers use, named apart from their topics
stochastic approximation · 0.0sign-sign LMS · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1990 | Sign-sign LMS convergence with independent stochastic inputsabstractThe sign-sign adaptive least-mean-square (LMS) identifier filter is a computationally efficient variant of the LMS identifier filter. It involves the introduction of signum functions in the traditional LMS update term. Consideration is given to global convergence of parameter estimates offered by this algorithm, to a ball with radius proportional to the algorithm step size for white input sequences, specially from Gaussian and uniform distributions.> Soura Dasgupta, C. Richard Johnson Jr., A. Maylar Baksho |
IEEE Trans. Inf. Theory | 3 |