Andrea Montanari
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| title | conference | date |
|---|---|---|
| Exact Asymptotics and Universality for Gradient Flows and Empirical Risk Minimizers | Deep Learning Theory Symposium 2021 | 2021-12-03 |
Paper with Michael Celentano and Chen Chen proves that in proportional high‑dim limits one coordinate trajectory of high‑dimensional gradient flow has a… | ||
| Tractability And Overparametrization In Some Simple Models | Algorithmic Advances for Statistical Inference with Combinatorial Structure | 2021-10-11 |
Proves precise large-|kappa| asymptotics for when negative‑margin linear classifiers exist versus when they’re findable: the satisfiability threshold δ_S… | ||
| An Instability in Variational Methods for Learning Topic Models | Optimization, Statistics and Uncertainty | 2017-11-28 |
Variational mean-field inference on low-rank generative models produces a spurious informative fixed point between the information-theoretic spectral threshold… | ||
| Analysis of Algorithms on Dense Matrices using Approximate Message Passing | Random Instances and Phase Transitions (Simons Institute, 2016) | 2016-05-02 |
A specific algorithmic claim: iterating a centered, rescaled dense random matrix with a component-wise nonlinearity plus an explicit Onsager reaction term… | ||
| Local Algorithms for Two Problems on Locally Tree-Like Graphs | Approximate Counting, Markov Chains and Phase Transitions | 2016-02-22 |
Andrea Montanari, Stanford University
Approximate Counting, Markov Chains and Phase Transitions
https://simons.berkeley.edu/talks/andrea-montanari-02-22-2016 | ||
| Where the Hard Things Are | Counting Complexity and Phase Transitions Boot Camp | 2016-01-26 |
Random K-SAT exhibits a sharp satisfiability threshold ~4.267 for k=3 and a geometry where solutions break into exponentially many well-separated clusters then… | ||
| Statistical Estimation on Graphs | Coding: From Practice to Theory | 2015-02-19 |
Random LDPC code (rate 1/2, degree 3/6) shows a sharp gap between the local decoder threshold (belief propagation) and the information-theoretic threshold… | ||
| A Statistical Model for Tensor Principal Component Analysis | Spectral Algorithms: From Theory to Practice 2014 | 2014-10-27 |
Model: symmetric spiked tensor X = beta v0^{\otimes K} + Z with Gaussian rotation‑invariant noise; the statistical threshold is constant: MLE succeeds once… | ||
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