Search Results for author:
Found 15 papers, 1 papers with code
Detection of Correlated Random Vectors
In this paper, we investigate the problem of deciding whether two standard normal random vectors $\mathsf{X}\in\mathbb{R}^{n}$ and $\mathsf{Y}\in\mathbb{R}^{n}$ are correlated or not.
Testing Dependency of Unlabeled Databases
In this paper, we investigate the problem of deciding whether two random databases $\mathsf{X}\in\mathcal{X}^{n\times d}$ and $\mathsf{Y}\in\mathcal{Y}^{n\times d}$ are statistically dependent or not.
Online Auditing of Information Flow
To that end, we first construct an offline algorithm for learning the probabilistic information spreading model, and then apply our optimal detection algorithm.
Deep Learning-Aided Subspace-Based DOA Recovery for Sparse Arrays
By doing so, we learn to cope with coherent sources and miscalibrated sparse arrays, while preserving the interpretability and the suitability of model-based subspace DoA estimators.
Planted Bipartite Graph Detection
To provide an evidence for this statistical computational gap, we prove computational lower bounds based on the low-degree conjecture, and show that the class of low-degree polynomials algorithms fail in the conjecturally hard region.
Phase Transitions in the Detection of Correlated Databases
We study the problem of detecting the correlation between two Gaussian databases $\mathsf{X}\in\mathbb{R}^{n\times d}$ and $\mathsf{Y}^{n\times d}$, each composed of $n$ users with $d$ features.
Learning k-Level Sparse Neural Networks Using a New Generalized Weighted Group Sparse Envelope Regularization
We propose an efficient method to learn both unstructured and structured sparse neural networks during training, utilizing a novel generalization of the sparse envelope function (SEF) used as a regularizer, termed {\itshape{weighted group sparse envelope function}} (WGSEF).
Mathematical Framework for Online Social Media Auditing
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.
Inferring Hidden Structures in Random Graphs
The detection problem is formalized as a hypothesis testing problem, where under the null hypothesis, the graph is a realization of an Erd\H{o}s-R\'{e}nyi random graph $\mathcal{G}(n, q)$ with edge density $q\in(0, 1)$; under the alternative, there is an unknown structure $\Gamma_k$ on $k$ nodes, planted in $\mathcal{G}(n, q)$, such that it appears as an \emph{induced subgraph}.
Random Subgraph Detection Using Queries
Under the alternative, there is a subgraph on $k$ vertices with edge probability $p>q$.
Fuzzy Clustering with Similarity Queries
In particular, we provide algorithms for fuzzy clustering in this setting that asks $O(\mathsf{poly}(k)\log n)$ similarity queries and run with polynomial-time-complexity, where $n$ is the number of items.
Learning User Preferences in Non-Stationary Environments
One of the main surprising observations in our experiments is the fact our algorithm outperforms other static algorithms even when preferences do not change over time.
Sharp Thresholds of the Information Cascade Fragility Under a Mismatched Model
We analyze a sequential decision making model in which decision makers (or, players) take their decisions based on their own private information as well as the actions of previous decision makers.
Same-Cluster Querying for Overlapping Clusters
In this paper, we look at the more practical scenario of overlapping clusters, and provide upper bounds (with algorithms) on the sufficient number of queries.
Universality of Computational Lower Bounds for Submatrix Detection
In the general submatrix detection problem, the task is to detect the presence of a small $k \times k$ submatrix with entries sampled from a distribution $\mathcal{P}$ in an $n \times n$ matrix of samples from $\mathcal{Q}$.
