arXiv research

AMP with Gaussian initialization shows weak-recovery threshold for phase retrieval.

Community detection is considered for a stochastic block model graph of n vertices, with K vertices in the planted community, edge probability p for pairs of vertices both in the community, and edge probability q for other pairs of vertices. The main focus of the paper is on weak recovery of the community based on the …

The stochastic block model (SBM) is a random graph model with different group of vertices connecting differently. It is widely employed as a canonical model to study clustering and community detection, and provides a fertile ground to study the information-theoretic and computational tradeoffs that arise in combinatori…

New method detects communities in complex hypergraphs, matching theoretical limits.

QAOA matches classical tensor power iteration in spiked tensor model recovery.

Sharp thresholds and contiguity for community detection in contextual SBM.

Nuclear norm minimization (NNM) has recently gained significant attention for its use in rank minimization problems. Similar to compressed sensing, using null space characterizations, recovery thresholds for NNM have been studied in \cite{arxiv,Recht_Xu_Hassibi}. However simulations show that the thresholds are far fro…

New algorithm detects communities even with corrupted data, reaching Kesten-Stigum threshold.

Study community detection in multi-view data with various types of information.

RBM learns in high dimensions via AMP and GD, reaching optimal weak recovery.

New Bethe-Hessian method improves community detection in sparse networks.

Phase retrieval requires at least d+o(d) measurements to recover signals with high probability.

The study examines how side information quality and quantity affect community recovery in graphs.

Study reveals efficient recovery of multi-modal signals via Bayesian methods and sequential learning.

Optimal spectral estimators and AMP combine for efficient weak recovery in orthogonally invariant GLMs.

Study spectral estimators for multi-index models to recover low-dimensional signal subspaces.

Paper uses EXIT analysis for community detection with side information.

Study shows overparametrization can shift and bend loss landscapes, affecting signal recovery.

The study analyzes how neural reward models learn features for policy optimization in a Gaussian single-index model.

Efficient algorithm for robust recovery in stochastic block models.

We study the problem of recovering a hidden community of cardinality $K$ from an $n \times n$ symmetric data matrix $A$, where for distinct indices $i,j$, $A_{ij} \sim P$ if $i, j$ both belong to the community and $A_{ij} \sim Q$ otherwise, for two known probability distributions $P$ and $Q$ depending on $n$. If $P={\r…

Study sets limits for detecting a subhypergraph in uniform hypergraphs.

Optimal iterative thresholding algorithms improve upon hard and soft thresholding.

Developed a new thresholding method that connects soft and hard thresholding.

Flat minima lead to better generalization in low-rank matrix recovery models.

Paper proposes a method to identify optimal threshold for stock market networks.

We study the community detection and recovery problem in partially-labeled stochastic block models (SBM). We develop a fast linearized message-passing algorithm to reconstruct labels for SBM (with $n$ nodes, $k$ blocks, $p,q$ intra and inter block connectivity) when $δ$ proportion of node labels are revealed. The signa…

Efficient private algorithms for estimating block models and mixture models.

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

A new SSL method uses instance-dependent thresholds to improve accuracy.

Study stability thresholds of big line bundles, proving bounds and generalizing results.

Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.

In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here we composed an empirical wavelet from the SETAR (Self-Exciting Threshold Autoregressive) model and identified the thres…

The article examines different thresholding methods for improving PAM algorithm in cancer classification.

In this paper we consider the cluster estimation problem under the Stochastic Block Model. We show that the semidefinite programming (SDP) formulation for this problem achieves an error rate that decays exponentially in the signal-to-noise ratio. The error bound implies weak recovery in the sparse graph regime with bou…

In the work of Ammann, Dahl and Humbert it has turned out that the Yamabe invariant on closed manifolds is a bordism invariant below a certain threshold constant. A similar result holds for a spinorial analogon. These threshold constants are characterized through Yamabe-type equations on products of spheres with rescal…

Proposes a conservative LR estimator for infrequent data near a frequency threshold.

New method trains neural networks with threshold activation functions efficiently.

Optimizes threshold selection for variance estimation in financial models.

Noise makes learning linear thresholds hard, but algorithms can still learn near-optimal thresholds.

Bayesian framework proves thresholds for multi-graph alignment feasibility.

Paper develops DLTF to learn optimized dictionaries for efficient thresholded feature recovery.

Polynomial neural networks explore thresholds for maximum expressiveness.

Paper introduces threshold invariant fairness to ensure equitable predictions across different groups.

Study evaluates thresholds for removing noise from DNN weights using random matrix theory.

Modeling firm default with a variable threshold based on management decisions.

FILTER model uses fusion penalized logistic threshold regression for high-dimensional data with unknown cut points.

The paper considers an investment timing problem appearing in real options theory. Present values from an investment project are modeled by general diffusion process. We prove necessary and sufficient conditions under which an optimal investment time is induced by threshold strategy. We study also the conditions of opt…