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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

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5 results for “Minimum-trace”
Identifiability of the minimum-trace directed acyclic graph and hill climbing algorithms without strict local optima under weakly increasing error variancesstat.CO

New proof shows how to identify DAGs with weakly increasing errors.

problem Identifying the true DAG in models with weakly increasing error variances.
method Minimum-trace DAG method and hill climbing algorithm with R2R neighborhood.
result Hill climbing algorithm without strict local optima under weakly increasing error variances.
DAG identificationGaussian linear modelsMinimum-tracehill climbingweakly increasing error
2025-08-07abs ↗pdf ↗
On Minimum Trace Factor Analysis -- An Old Song Sung to a New Tunestat.ML

Paper relaxes factor analysis for noisy data, improving robustness.

problem Challenges in finding robust low dimensional approximations for data with heteroskedastic noise.
method Introduces a relaxed version of Minimum Trace Factor Analysis (MTFA) as a convex optimization method.
result Effective at not overfitting to heteroskedastic perturbations and addressing common issues in factor analysis.
factor analysisheteroskedastic noiserobustnessconvex optimizationdimensionality reduction
2024-02-04abs ↗pdf ↗
Latent Factor Analysis of Gaussian Distributions under Graphical Constraintscs.IT

We analyze the structure of covariance matrices under graph constraints.

problem Analyzing the structure of covariance matrices under graph constraints.
method We explore the algebraic structure of the solution space of convex optimization problem Constrained Minimum Trace Factor Analysis (CMTFA) under a latent star topology.
result CMTFA can have either a rank 1 or a rank n-1 solution, with conditions for both.
Latent Factor AnalysisGaussian DistributionsGraphical ConstraintsMinimum Trace Factor AnalysisStar Topology
2020-01-08abs ↗pdf ↗
Optimal Combination Forecasts on Retail Multi-Dimensional Sales Datastat.ML

Time series data in the retail world are particularly rich in terms of dimensionality, and these dimensions can be aggregated in groups or hierarchies. Valuable information is nested in these complex structures, which helps to predict the aggregated time series data. From a portfolio of brands under HUUB's monitoring, …

2019-03-22abs ↗pdf ↗
Model selection in reconciling hierarchical time seriescs.LG

Paper proposes a new method for selecting the best hierarchical forecasting approach.

problem Selecting the best method for reconciling base forecasts in hierarchical time series.
method Conditional hierarchical forecasting using machine learning and time series features.
result Conditional hierarchical forecasting leads to significantly more accurate forecasts, especially at lower levels.
time series forecastinghierarchical dataforecast reconciliationmachine learningcoherence
2020-10-21abs ↗pdf ↗