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researcher

A. Montanari

50 papers hereh-index 8127.1k citations316 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author3
  • first author18
  • middle author14
  • last author15

Across the 50 of 50 papers where every author was matched, so the position is known.

fields
  • cs.IT17
  • cond-mat.stat-mech8
  • cond-mat.dis-nn6
  • math.PR5
  • cs.LG3
  • cs.DM2
same name
  • A. Montanari — 54 papers
  • A. Montanari — 8 papers
  • A. Montanari — 6 papers
  • A. Montanari — 4 papers, h 121
  • A. Montanari — 3 papers, h 13
  • A. Montanari — 3 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20002013
most citedGibbs States and the Set of Solutions of Random Constraint Satisfaction Problems

553 citations · 1.4k across the 29 of their papers we have counts for

collaborators
Showing 2011Show all

4 papers · 1 filter

cs.IT2011★ 6 cited

Optimal coding for the deletion channel with small deletion probability

Yashodhan Kanoria, Andrea Montanari

The deletion channel is the simplest point-to-point communication channel that models lack of synchronization. Input bits are deleted independently with probability d, and when the…

cs.MA2011

Subexponential convergence for information aggregation on regular trees

Yashodhan Kanoria, Andrea Montanari

We consider the decentralized binary hypothesis testing problem on trees of bounded degree and increasing depth. For a regular tree of depth t and branching factor k>=2, we assume…

cs.IT2011★ 14 cited

Compressed Sensing over ℓp​-balls: Minimax Mean Square Error

David Donoho, Iain Johnstone, Arian Maleki +1

We consider the compressed sensing problem, where the object $x_0 \in \bR^N$ is to be recovered from incomplete measurements y=Ax0​+z; here the sensing matrix A is an $n \t…

cs.IT2011

Information Theoretic Limits on Learning Stochastic Differential Equations

José Bento, Morteza Ibrahimi, Andrea Montanari

Consider the problem of learning the drift coefficient of a stochastic differential equation from a sample path. In this paper, we assume that the drift is parametrized by a high d…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.