ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1808.07226
  4. Cited By
Mean-field approximation, convex hierarchies, and the optimality of
  correlation rounding: a unified perspective
v1v2 (latest)

Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective

22 August 2018
Vishesh Jain
Frederic Koehler
Andrej Risteski
ArXiv (abs)PDFHTML

Papers citing "Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective"

11 / 11 papers shown
Title
Optimal Inference Schedules for Masked Diffusion Models
Optimal Inference Schedules for Masked Diffusion Models
Sitan Chen
Kevin Cong
Jerry Li
DiffM
164
0
0
06 Nov 2025
Variational Inference on the Boolean Hypercube with the Quantum Entropy
Variational Inference on the Boolean Hypercube with the Quantum EntropyInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Eliot Beyler
Francis Bach
141
0
0
17 Feb 2025
Adaptive and oblivious statistical adversaries are equivalent
Adaptive and oblivious statistical adversaries are equivalentSymposium on the Theory of Computing (STOC), 2024
Guy Blanc
Gregory Valiant
AAML
126
5
0
17 Oct 2024
On Naive Mean-Field Approximation for high-dimensional canonical GLMs
On Naive Mean-Field Approximation for high-dimensional canonical GLMs
Soumendu Sundar Mukherjee
Jiaze Qiu
Subhabrata Sen
117
1
0
21 Jun 2024
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein spaceAnnual Conference Computational Learning Theory (COLT), 2023
Yiheng Jiang
Sinho Chewi
Aram-Alexandre Pooladian
430
14
0
05 Dec 2023
Metric Embeddings Beyond Bi-Lipschitz Distortion via Sherali-Adams
Metric Embeddings Beyond Bi-Lipschitz Distortion via Sherali-AdamsAnnual Conference Computational Learning Theory (COLT), 2023
Ainesh Bakshi
Vincent Cohen-Addad
Samuel B. Hopkins
Rajesh Jayaram
Silvio Lattanzi
71
1
0
29 Nov 2023
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational
  Methods
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational MethodsAnnual Conference Computational Learning Theory (COLT), 2022
Frederic Koehler
Holden Lee
Andrej Risteski
162
22
0
17 Feb 2022
Variational Inference in high-dimensional linear regression
Variational Inference in high-dimensional linear regressionJournal of machine learning research (JMLR), 2021
Soumendu Sundar Mukherjee
S. Sen
BDL
97
26
0
25 Apr 2021
The Complexity of Adversarially Robust Proper Learning of Halfspaces
  with Agnostic Noise
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic NoiseNeural Information Processing Systems (NeurIPS), 2020
Ilias Diakonikolas
D. Kane
Pasin Manurangsi
119
22
0
30 Jul 2020
Fast Convergence of Belief Propagation to Global Optima: Beyond
  Correlation Decay
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation DecayNeural Information Processing Systems (NeurIPS), 2019
Frederic Koehler
46
13
0
24 May 2019
Computational Hardness of Certifying Bounds on Constrained PCA Problems
Computational Hardness of Certifying Bounds on Constrained PCA Problems
Afonso S. Bandeira
Dmitriy Kunisky
Alexander S. Wein
166
71
0
19 Feb 2019
1