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How Robust are Reconstruction Thresholds for Community Detection?

How Robust are Reconstruction Thresholds for Community Detection?

4 November 2015
Ankur Moitra
William Perry
Alexander S. Wein
ArXivPDFHTML

Papers citing "How Robust are Reconstruction Thresholds for Community Detection?"

18 / 18 papers shown
Title
Adversarially-Robust Inference on Trees via Belief Propagation
Adversarially-Robust Inference on Trees via Belief Propagation
Samuel B. Hopkins
Anqi Li
42
0
0
31 Mar 2024
Top-$K$ ranking with a monotone adversary
Top-KKK ranking with a monotone adversary
Yuepeng Yang
Antares Chen
Lorenzo Orecchia
Cong Ma
37
1
0
12 Feb 2024
Do algorithms and barriers for sparse principal component analysis
  extend to other structured settings?
Do algorithms and barriers for sparse principal component analysis extend to other structured settings?
Guanyi Wang
Mengqi Lou
A. Pananjady
CML
40
1
0
25 Jul 2023
Differentially-Private Hierarchical Clustering with Provable
  Approximation Guarantees
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees
Jacob Imola
Alessandro Epasto
Mohammad Mahdian
Vincent Cohen-Addad
Vahab Mirrokni
29
4
0
31 Jan 2023
Private estimation algorithms for stochastic block models and mixture
  models
Private estimation algorithms for stochastic block models and mixture models
Hongjie Chen
Vincent Cohen-Addad
Tommaso dÓrsi
Alessandro Epasto
Jacob Imola
David Steurer
Stefan Tiegel
FedML
43
20
0
11 Jan 2023
Minimax Rates for Robust Community Detection
Minimax Rates for Robust Community Detection
Allen Liu
Ankur Moitra
22
13
0
25 Jul 2022
Semi-Supervised Clustering of Sparse Graphs: Crossing the
  Information-Theoretic Threshold
Semi-Supervised Clustering of Sparse Graphs: Crossing the Information-Theoretic Threshold
Jun Sheng
Thomas Strohmer
30
0
0
24 May 2022
Robust Estimation for Random Graphs
Robust Estimation for Random Graphs
Jayadev Acharya
Ayush Jain
Gautam Kamath
A. Suresh
Huanyu Zhang
30
8
0
09 Nov 2021
Estimating Principal Components under Adversarial Perturbations
Estimating Principal Components under Adversarial Perturbations
Pranjal Awasthi
Xue Chen
Aravindan Vijayaraghavan
AAML
17
2
0
31 May 2020
Reducibility and Statistical-Computational Gaps from Secret Leakage
Reducibility and Statistical-Computational Gaps from Secret Leakage
Matthew Brennan
Guy Bresler
35
86
0
16 May 2020
Improper Learning for Non-Stochastic Control
Improper Learning for Non-Stochastic Control
Max Simchowitz
Karan Singh
Elad Hazan
16
153
0
25 Jan 2020
Minimax Rates and Efficient Algorithms for Noisy Sorting
Minimax Rates and Efficient Algorithms for Noisy Sorting
Cheng Mao
Jonathan Niles-Weed
Philippe Rigollet
30
40
0
28 Oct 2017
Exponential error rates of SDP for block models: Beyond Grothendieck's
  inequality
Exponential error rates of SDP for block models: Beyond Grothendieck's inequality
Yingjie Fei
Yudong Chen
30
35
0
23 May 2017
Does robustness imply tractability? A lower bound for planted clique in
  the semi-random model
Does robustness imply tractability? A lower bound for planted clique in the semi-random model
Jacob Steinhardt
11
17
0
17 Apr 2017
Exact recovery in the Ising blockmodel
Exact recovery in the Ising blockmodel
Quentin Berthet
Philippe Rigollet
P. Srivastava
TPM
25
44
0
12 Dec 2016
Learning from Untrusted Data
Learning from Untrusted Data
Moses Charikar
Jacob Steinhardt
Gregory Valiant
FedML
OOD
21
292
0
07 Nov 2016
Detection in the stochastic block model with multiple clusters: proof of
  the achievability conjectures, acyclic BP, and the information-computation
  gap
Detection in the stochastic block model with multiple clusters: proof of the achievability conjectures, acyclic BP, and the information-computation gap
Emmanuel Abbe
Colin Sandon
22
119
0
30 Dec 2015
A semidefinite program for unbalanced multisection in the stochastic
  block model
A semidefinite program for unbalanced multisection in the stochastic block model
William Perry
Alexander S. Wein
27
49
0
20 Jul 2015
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