On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

Trend · papers per month

count % of corpus

4 results for “BIHT”

On the Role of Normalization in Binary Iterative Hard Thresholding for 1-bit Compressed Sensingcs.IT

This paper resolves BIHT convergence, showing normalization is not necessary in noiseless settings but crucial for robustness.

problem Analyzing convergence and robustness of BIHT for 1-bit compressed sensing.

method Characterizes BIHT convergence and robustness, proving necessity of normalization for robustness under sign corruptions.

result Per-iteration normalization is not necessary for optimal recovery in noiseless settings but is crucial for robustness under sign corruptions.

BIHT 1-bit compressed sensing convergence analysis robustness normalization

2026-07-17abs ↗pdf ↗

Robust 1-bit Compressed Sensing with Iterative Hard Thresholdingcs.IT

Paper analyzes BIHT for noisy 1-bit CS, improving results with up to τ-fraction of incorrect measurements.

problem Estimating sparse vectors from noisy sign measurements in 1-bit compressed sensing.

method Binary Iterative Hard Thresholding (BIHT) algorithm, using Gaussian matrices and high-dimensional geometry analysis.

result BIHT provides estimates within ε+τ error with τ-fraction of incorrect measurements, maintaining universality of measurements.

1-bit compressed sensing BIHT algorithm iterative hard thresholding sign measurements noisy setting

2023-10-12abs ↗pdf ↗

Binary Iterative Hard Thresholding Converges with Optimal Number of Measurements for 1-Bit Compressed Sensingcs.IT

Binary Iterative Hard Thresholding converges with optimal number of 1-bit measurements.

problem Recovering sparse signals from 1-bit compressed measurements.

method Binary Iterative Hard Thresholding (BIHT) algorithm.

result BIHT converges with only O(k/ε) measurements, optimal for recovery.

compressed sensing 1-bit quantization sparse recovery gradient descent nonconvex optimization

2022-07-07abs ↗pdf ↗

Learning sparse generalized linear models with binary outcomes via iterative hard thresholdingmath.ST

New algorithm learns sparse GLMs for binary outcomes efficiently.

problem Sparse modeling of binary outcomes in high-dimensional data.

method Iterative hard thresholding algorithm (BIHT) for sparse GLMs.

result BIHT achieves statistical optimality for logistic regression.

sparse modeling binary outcomes generalized linear models iterative hard thresholding statistical optimality

2025-02-25abs ↗pdf ↗