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Found 14 papers, 1 papers with code
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
In the transfer learning paradigm models learn useful representations (or features) during a data-rich pretraining stage, and then use the pretrained representation to improve model performance on data-scarce downstream tasks.
Fundamental limits of community detection from multi-view data: multi-layer, dynamic and partially labeled block models
Multi-view data arises frequently in modern network analysis e. g. relations of multiple types among individuals in social network analysis, longitudinal measurements of interactions among observational units, annotated networks with noisy partial labeling of vertices etc.
A Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regression
We study empirical Bayes estimation in high-dimensional linear regression.
Bayes optimal learning in high-dimensional linear regression with network side information
Supervised learning problems with side information in the form of a network arise frequently in applications in genomics, proteomics and neuroscience.
Sparse Signal Detection in Heteroscedastic Gaussian Sequence Models: Sharp Minimax Rates
Given a heterogeneous Gaussian sequence model with unknown mean $\theta \in \mathbb R^d$ and known covariance matrix $\Sigma = \operatorname{diag}(\sigma_1^2,\dots, \sigma_d^2)$, we study the signal detection problem against sparse alternatives, for known sparsity $s$.
A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond
In recent times, inference for the ATE in the presence of high-dimensional covariates has been extensively studied.
High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models
We provide a "path-wise" characterization of the overlap between the output of the Langevin algorithm and the planted signal.
The TAP free energy for high-dimensional linear regression
We derive a variational representation for the log-normalizing constant of the posterior distribution in Bayesian linear regression with a uniform spherical prior and an i. i. d.
Variational Inference in high-dimensional linear regression
Using the nascent theory of non-linear large deviations (Chatterjee and Dembo, 2016), we derive sufficient conditions for the leading-order correctness of the naive mean-field approximation to the log-normalizing constant of the posterior distribution.
Contextual Stochastic Block Model: Sharp Thresholds and Contiguity
We study community detection in the contextual stochastic block model arXiv:1807. 09596 [cs. SI], arXiv:1607. 02675 [stat. ME].
Long ties accelerate noisy threshold-based contagions
To model the trade-off between long and short edges we analyze the rate of spread over networks that are the union of circular lattices and random graphs on $n$ nodes.
Social and Information Networks Probability Physics and Society 91D30, 05C80
Contextual Stochastic Block Models
We provide the first information theoretic tight analysis for inference of latent community structure given a sparse graph along with high dimensional node covariates, correlated with the same latent communities.
LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming
The objective is to optimize a novel QoE metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches.
Networking and Internet Architecture Multimedia
Tracking Dynamic Sources of Malicious Activity at Internet Scale
We formulate and address the problem of discovering dynamic malicious regions on the Internet.
