research
∙
12/20/2022
Random linear estimation with rotationally-invariant designs: Asymptotics at high temperature
We study estimation in the linear model y=Aβ^⋆+ϵ, in a Bayesian setting ...
share
research
∙
12/20/2022
HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes
Bayesian optimization (BO), while proved highly effective for many black...
research
∙
06/27/2022
Universality of Approximate Message Passing algorithms and tensor networks
Approximate Message Passing (AMP) algorithms provide a valuable tool for...
research
∙
05/04/2022
Rates of estimation for high-dimensional multi-reference alignment
We study the continuous multi-reference alignment model of estimating a ...
research
∙
04/01/2022
Differential Liquidity Provision in Uniswap v3 and Implications for Contract Design
Decentralized exchanges (DEXs) provide a means for users to trade pairs ...
research
∙
10/05/2021
Approximate Message Passing for orthogonally invariant ensembles: Multivariate non-linearities and spectral initialization
We study a class of Approximate Message Passing (AMP) algorithms for sym...
research
∙
07/02/2021
Maximum likelihood for high-noise group orbit estimation and single-particle cryo-EM
Motivated by applications to single-particle cryo-electron microscopy (c...
research
∙
06/21/2021
Local convexity of the TAP free energy and AMP convergence for Z2-synchronization
We study mean-field variational Bayesian inference using the TAP approac...
research
∙
05/06/2021
The replica-symmetric free energy for Ising spin glasses with orthogonally invariant couplings
We study the mean-field Ising spin glass model with external field, wher...
research
∙
12/21/2020
Empirical Bayes PCA in high dimensions
When the dimension of data is comparable to or larger than the number of...
research
∙
08/27/2020
Approximate Message Passing algorithms for rotationally invariant matrices
Approximate Message Passing (AMP) algorithms have seen widespread use ac...
research
∙
05/31/2020
Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs
We study estimation of a gradient-sparse parameter vector θ^* ∈ℝ^p, havi...
research
∙
03/31/2020
Likelihood landscape and maximum likelihood estimation for the discrete orbit recovery model
We study the non-convex optimization landscape for maximum likelihood es...
research
∙
07/20/2019
Spectral Graph Matching and Regularized Quadratic Relaxations II: Erdős-Rényi Graphs and Universality
We analyze a new spectral graph matching algorithm, GRAph Matching by Pa...
research
∙
07/20/2019
Spectral Graph Matching and Regularized Quadratic Relaxations I: The Gaussian Model
Graph matching aims at finding the vertex correspondence between two unl...
research
∙
07/19/2019
Surfing: Iterative optimization over incrementally trained deep networks
We investigate a sequential optimization procedure to minimize the empir...
research
∙
05/15/2019
Iterative Alpha Expansion for estimating gradient-sparse signals from linear measurements
We consider estimating a piecewise-constant image, or a gradient-sparse ...
research
∙
03/22/2019
Principal components in linear mixed models with general bulk
We study the outlier eigenvalues and eigenvectors in variance components...
research
∙
03/04/2019
Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space
In this paper we propose a hybrid architecture of actor-critic algorithm...
research
∙
08/23/2018
TAP free energy, spin glasses, and variational inference
We consider the Sherrington-Kirkpatrick model of spin glasses with ferro...
research
∙
06/25/2018
Spiked covariances and principal components analysis in high-dimensional random effects models
We study principal components analyses in multivariate random and mixed ...
research
∙
10/17/2016
