Research paper / RESOURCE PROFILE

Online Self-Concordant and Relatively Smooth Minimization, With Applic

Publication: Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum States. Published Oct 3, 2022.

Research & Papers
arxiv.orgPublished Oct 3, 2022
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Overview

Publication: Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum States. Published Oct 3, 2022.

Publication information

Publication title
Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum States
Authors
Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li
Published
Oct 3, 2022
Last publication revision
Sep 21, 2023
Subject areas
stat.ML, cs.LG, math.OC, q-fin.PM, quant-ph
Publication source
arxiv.org · abs/2210.00997v3 ↗

Source: arXiv API · observed Oct 11, 2026.

Source links & provenance

https://arxiv.org/abs/2210.00997 ↗

Discovery references

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Screenshot unavailableResource information and source links remain available. DeepIntoStreams/TSFM_Finance — reviewed source website screenshot
RepositoryChecked capture

DeepIntoStreams/TSFM_Finance

github.com

the official implementation of the paper “Re(visiting) Time Series Foundation Models in Finance”.

GitHub stars
31
Language
Jupyter Notebook
Code license
Apache-2.0
foundation-modeltime-seriesresearch-lab
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