31 citations · 59 across the 37 of their papers we have counts for
43 papers
Fundamental Limits of Query-Based Subgraph Detection
Wasim Huleihel
The planted subgraph detection problem asks whether a random graph contains a hidden structured subgraph. In the classical formulation, the entire adjacency matrix is observed and…
Recovery of Planted Subgraphs
Wasim Huleihel
Understanding the fundamental limits of recovering planted subgraphs in random graphs is a central challenge in high-dimensional statistics and theoretical computer science. While…
Testing for a Hidden Geometry in Random Graphs
Amit Silber, Mor Oren-Loberman, Wasim Huleihel
We study the problem of detecting a faint geometric signal hidden in an otherwise random graph. Formally, we consider a hypothesis testing problem in which, under the null, the obs…
Inhomogeneous Submatrix Detection
Mor Oren-Loberman, Dvir Jerbi, Tamir Bendory +1
In this paper, we study the problem of detecting multiple hidden submatrices in a large Gaussian random matrix when the planted signal is inhomogeneous across entries. Under the nu…
Robust Detection of Planted Subgraphs in Semi-Random Models
Dor Elimelech, Wasim Huleihel
Detection of planted subgraphs in Erdös-Rényi random graphs has been extensively studied, leading to a rich body of results characterizing both statistical and computational thresh…
Expectation-maximization for low-SNR multi-reference alignment
Amnon Balanov, Wasim Huleihel, Tamir Bendory
We study the multi-reference alignment (MRA) problem of recovering a signal from noisy observations acted on by unknown random circular shifts. While the information-theoretic limi…
Detecting Arbitrary Planted Subgraphs in Random Graphs
Dor Elimelech, Wasim Huleihel
The problems of detecting and recovering planted structures/subgraphs in Erdős-Rényi random graphs, have received significant attention over the past three decades, leading to many…
AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning
Yehonathan Refael, Jonathan Svirsky, Boris Shustin +2
Training and fine-tuning large language models (LLMs) come with challenges related to memory and computational requirements due to the increasing size of the model weights and the…
Testing Dependency of Weighted Random Graphs
Mor Oren, Vered Paslev, Wasim Huleihel
In this paper, we study the task of detecting the edge dependency between two weighted random graphs. We formulate this task as a simple hypothesis testing problem, where under the…
Sequential Classification of Misinformation
Daniel Toma, Wasim Huleihel
In recent years there have been a growing interest in online auditing of information flow over social networks with the goal of monitoring undesirable effects, such as, misinformat…
Confirmation Bias in Gaussian Mixture Models
Amnon Balanov, Tamir Bendory, Wasim Huleihel
Confirmation bias, the tendency to interpret information in a way that aligns with one's preconceptions, can profoundly impact scientific research, leading to conclusions that refl…
Einstein from Noise: Statistical Analysis
Amnon Balanov, Wasim Huleihel, Tamir Bendory
``Einstein from noise" (EfN) is a prominent example of the model bias phenomenon: systematic errors in the statistical model that lead to spurious but consistent estimates. In the…
Detection of Correlated Random Vectors
Dor Elimelech, Wasim Huleihel
In this paper, we investigate the problem of deciding whether two standard normal random vectors and are correlated or n…
Testing Dependency of Unlabeled Databases
Vered Paslev, Wasim Huleihel
In this paper, we investigate the problem of deciding whether two random databases and are statistical…
Online Auditing of Information Flow
Mor Oren-Loberman, Vered Azar, Wasim Huleihel
Modern social media platforms play an important role in facilitating rapid dissemination of information through their massive user networks. Fake news, misinformation, and unverifi…
Deep Learning-Aided Subspace-Based DOA Recovery for Sparse Arrays
Yoav Amiel, Dor H. Shmuel, Nir Shlezinger +1
Sparse arrays enable resolving more direction of arrivals (DoAs) than antenna elements using non-uniform arrays. This is typically achieved by reconstructing the covariance of a vi…
Detection and Recovery of Hidden Submatrices
Marom Dadon, Wasim Huleihel, Tamir Bendory
In this paper, we study the problems of detection and recovery of hidden submatrices with elevated means inside a large Gaussian random matrix. We consider two different structures…
Planted Bipartite Graph Detection
Asaf Rotenberg, Wasim Huleihel, Ofer Shayevitz
We consider the task of detecting a hidden bipartite subgraph in a given random graph. This is formulated as a hypothesis testing problem, under the null hypothesis, the graph is a…
Phase Transitions in the Detection of Correlated Databases
Dor Elimelech, Wasim Huleihel
We study the problem of detecting the correlation between two Gaussian databases and , each composed of users with…
Learning k-Level Structured Sparse Neural Networks Using Group Envelope Regularization
Yehonathan Refael, Iftach Arbel, Wasim Huleihel
The extensive need for computational resources poses a significant obstacle to deploying large-scale Deep Neural Networks (DNN) on devices with constrained resources. At the same t…
Mathematical Framework for Online Social Media Auditing
Wasim Huleihel, Yehonathan Refael
Social media platforms (SMPs) leverage algorithmic filtering (AF) as a means of selecting the content that constitutes a user's feed with the aim of maximizing their rewards. Selec…
Optimal Reference for DNA Synthesis
Ohad Elishco, Wasim Huleihel
In the recent years, DNA has emerged as a potentially viable storage technology. DNA synthesis, which refers to the task of writing the data into DNA, is perhaps the most costly pa…
Inferring Hidden Structures in Random Graphs
Wasim Huleihel
We study the two inference problems of detecting and recovering an isolated community of \emph{general} structure planted in a random graph. The detection problem is formalized as…
Random Subgraph Detection Using Queries
Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal
The planted densest subgraph detection problem refers to the task of testing whether in a given (random) graph there is a subgraph that is unusually dense. Specifically, we observe…