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.

168,695 papers · 148 categories

Trend · papers per month

count % of corpus

4 results for “cross-pollination”

Criteria for Classifying Forecasting Methodsstat.ML

The authors argue against the classification of forecasting methods as machine learning or statistical.

problem The classification of forecasting methods as machine learning or statistical limits insights into their appropriateness and effectiveness.

method Alternative characteristics of forecasting methods are proposed to draw meaningful conclusions.

result The distinction between machine learning and statistical forecasting methods is not fundamental.

forecasting methods machine learning statistical methods cross-pollination appropriateness

2022-12-07abs ↗pdf ↗

Review of Clustering Methods for Functional Datastat.ME

Review of clustering methods for functional data across various fields.

problem Identify heterogeneous morphological patterns in continuous functions.

method Comprehensive review and systematic taxonomy of existing methods.

result Proposes a new taxonomy linking functional data clustering to conventional multivariate methods.

functional data clustering methods taxonomy heterogeneous patterns multivariate analysis

2022-10-03abs ↗pdf ↗

Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Designstat.ML

GBS uses machine learning to design products based on consumer preferences.

problem Designing products to meet consumer preferences.

method GBS is a discrete choice experiment that uses machine learning to adaptively construct paired comparison questions.

result GBS outperforms existing methods in accuracy and sample efficiency.

discrete choice experiment machine learning consumer preferences product design adaptive design

2023-10-18abs ↗pdf ↗

Learning in Implicit Generative Modelsstat.ML

Generative adversarial networks (GANs) provide an algorithmic framework for constructing generative models with several appealing properties: they do not require a likelihood function to be specified, only a generating procedure; they provide samples that are sharp and compelling; and they allow us to harness our knowl…

2016-10-11abs ↗pdf ↗