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

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9 results for “PMM”

Posterior Mean Matching: Generative Modeling through Online Bayesian Inferencecs.LG

PMM uses Bayesian inference to generate data from noisy approximations.

problem Creating flexible generative models for various data types.

method Bayesian inference and conjugate pairs of distributions.

result PMM achieves performance competitive with existing generative models.

Bayesian inference Generative modeling Online learning Conjugate pairs Flexible models

2024-12-17abs ↗pdf ↗

Evaluating tree-based imputation methods as an alternative to MICE PMM for drawing inference in empirical studiesstat.AP

Study compares tree-based imputation methods to MICE PMM for missing data.

problem Missing data in empirical studies.

method Various imputation methods including MICE PMM, RF, missRanger, and MIXGBoost.

result Tree-based imputations, especially RF and missRanger with PMM, perform better.

imputation methods tree-based MICE PMM Random Forest missing data

2024-01-17abs ↗pdf ↗

A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problemsmath.OC

In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for these problems. Our key idea for making the proposed PMM to be efficient is to develop a sparse semismooth Newton method to solve the corresponding subproblem…

2019-03-27abs ↗pdf ↗

Bayesian Parametric Matrix Models: Principled Uncertainty Quantification for Spectral Learningcs.LG

Bayesian parametric matrix models provide uncertainty quantification for spectral learning.

problem Uncertainty quantification in spectral learning for safety-critical applications.

method Bayesian parametric matrix models (B-PMMs) that extend PMMs to provide uncertainty estimates.

result B-PMMs achieve exceptional uncertainty calibration (ECE < 0.05) while maintaining favorable scaling.

Bayesian Parametric Matrix Models Spectral Learning Uncertainty Quantification Machine Learning

2025-09-15abs ↗pdf ↗

Regenerativity of Viterbi process for pairwise Markov modelscs.IT

The Viterbi process can be extended indefinitely in a pairwise Markov model.

problem Estimating hidden chains in pairwise Markov models.

method Construction of barriers to ensure Viterbi path goes through states.

result The Viterbi process is regenerative in the PMM.

Viterbi process Pairwise Markov model Regeneration Markov chain Hidden Markov model

2021-03-15abs ↗pdf ↗

Iterative Reweighted Framework Based Algorithms for Sparse Linear Regression with Generalized Elastic Net Penaltystat.ML

Paper develops algorithms for sparse linear regression with generalized elastic net penalty.

problem Sparse linear regression with robust penalty for high-dimensional data.

method Iterative Reweighted Framework based on ADMM and PMM with SNN.

result Efficient algorithms provide superior performance in both simulated and real data.

sparse linear regression elastic net penalty generalized penalty ADMM PMM

2024-11-22abs ↗pdf ↗

Improving Capital Efficiency and Impermanent Loss: Multi-Token Proactive Market Makerq-fin.TR

This research improves capital efficiency and impermanent loss in cryptocurrency markets using multi-token trading pools.

problem Poor impermanent loss and capital efficiency in automated market makers.

method Analysis and construction of a multi-token token proactive market maker (MPMM).

result MPMM shows better impermanent loss and capital efficiency than comparable market makers.

impermanent loss capital efficiency automated market makers multi-token pools market making

2023-08-17abs ↗pdf ↗

Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimationstat.ME

A new method for sampling on manifolds reduces density estimation errors.

problem Sampling on implicitly defined manifolds in various applications.

method Polynomial-Maximization Moment (PMM) estimator replacing local k-nearest-neighbour density estimate.

result Reduces density estimation errors by 22--36% on asymmetric gamma and boundary-spacing regimes.

sampling manifolds density estimation polynomial-maximization k-nearest-neighbour

2026-05-19abs ↗pdf ↗

An Efficient Learning Method to Connect Observablesnucl-th

A new emulator connects observables directly from data.

problem Constructing fast and accurate surrogate models for robust predictions.

method Introduces Multiparameter Eigenvalue Problem (MEP) emulator trained with Eigenvector Continuation (EC) and Parametric Matrix Model (PMM) data.

result The MEP emulator can make predictions directly from observables to observables.

surrogate models observables Eigenvector Continuation Parametric Matrix Model robust predictions

2025-03-03abs ↗pdf ↗