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.

169,051 papers · 148 categories

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4 results for “block-random”

A block-random algorithm for learning on distributed, heterogeneous datacs.LG

New algorithm learns from distributed, heterogeneous data without shuffling.

problem Efficiently learning from distributed, heterogeneous data in exascale simulations.

method Block-random gradient descent algorithm for distributed, heterogeneous data.

result Algorithm enables in situ learning without pre-shuffling data.

distributed learning heterogeneous data block-random SGD exascale

2019-02-28abs ↗pdf ↗

Block Randomized Optimization for Adaptive Hypergraph Learningcs.SI

Two randomized algorithms improve hypergraph learning accuracy and efficiency.

problem Efficiently learning and tagging images in hypergraphs.

method Block randomized SVD and conjugate gradient method.

result Both methods achieve high accuracy and reduce computational requirements.

hypergraph learning randomized SVD conjugate gradient image tagging F1 score

2019-08-22abs ↗pdf ↗

On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomizationecon.EM

The paper proposes a method to estimate treatment effects using CAR designs with additional covariates.

problem Estimating distributional treatment effects in CAR designs with additional covariates.

method Flexible distribution regression framework that incorporates additional covariates using machine learning methods.

result The proposed estimator attains the semiparametric efficiency bound for distributional treatment effects under CAR.

distributional treatment effects covariate-adaptive randomization flexible regression machine learning asymptotic distribution

2025-06-06abs ↗pdf ↗

Feature Selection for Data Integration with Mixed Multi-view Datastat.ME

Develops a feature selection method for multi-view data with mixed types.

problem Challenges in feature selection for high-dimensional multi-view data with mixed data types.

method Block Randomized Adaptive Iterative Lasso (B-RAIL) combining randomized Lasso, adaptive weighting, and stability selection.

result Demonstrates effectiveness of B-RAIL in identifying biomarkers and novel candidates for ovarian cancer.

feature selection multi-view data mixed data types sparse regression graph selection

2019-03-27abs ↗pdf ↗