no code implementations • 18 Apr 2024 • Yufan Li, Subhabrata Sen, Ben Adlam
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.
no code implementations • 16 Jan 2024 • Xiaodong Yang, Buyu Lin, Subhabrata Sen
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.
no code implementations • 28 Sep 2023 • Sumit Mukherjee, Bodhisattva Sen, Subhabrata Sen
We study empirical Bayes estimation in high-dimensional linear regression.
no code implementations • 9 Jun 2023 • Sagnik Nandy, Subhabrata Sen
Supervised learning problems with side information in the form of a network arise frequently in applications in genomics, proteomics and neuroscience.
no code implementations • 15 Nov 2022 • Julien Chhor, Rajarshi Mukherjee, Subhabrata Sen
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 $s$.
no code implementations • 20 May 2022 • Kuanhao Jiang, Rajarshi Mukherjee, Subhabrata Sen, Pragya Sur
In recent times, inference for the ATE in the presence of high-dimensional covariates has been extensively studied.
no code implementations • 9 Apr 2022 • Tengyuan Liang, Subhabrata Sen, Pragya Sur
We provide a "path-wise" characterization of the overlap between the output of the Langevin algorithm and the planted signal.
no code implementations • 14 Mar 2022 • Jiaze Qiu, Subhabrata Sen
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.
no code implementations • 25 Apr 2021 • Sumit Mukherjee, Subhabrata Sen
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.
no code implementations • 15 Nov 2020 • Chen Lu, Subhabrata Sen
We study community detection in the contextual stochastic block model arXiv:1807. 09596 [cs. SI], arXiv:1607. 02675 [stat. ME].
1 code implementation • 8 Oct 2018 • Dean Eckles, Elchanan Mossel, M. Amin Rahimian, Subhabrata Sen
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
no code implementations • NeurIPS 2018 • Yash Deshpande, Andrea Montanari, Elchanan Mossel, Subhabrata Sen
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.
no code implementations • 30 Apr 2018 • Anis Elgabli, Vaneet Aggarwal, Shuai Hao, Feng Qian, Subhabrata Sen
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
no code implementations • NeurIPS 2009 • Shobha Venkataraman, Avrim Blum, Dawn Song, Subhabrata Sen, Oliver Spatscheck
We formulate and address the problem of discovering dynamic malicious regions on the Internet.