📄

Paper

Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training

by Adhiraj Chattopadhyay arxiv/2604.14206

Free2AITools Nexus Index

36.1

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0

P: Popularity 0

R: Recency 71

Q: Quality 60

Tech Context

Vital Performance —

This paper proposes a machine learning assisted portfolio optimization framework designed for low data environments and regime uncertainty. We construct a teacher student learning pipeline in which a Conditional Value at Risk (CVaR) optimizer generates supervisory labels, and neural models (Bayesian and deterministic) are trained using both real and synthetically augmented data. The synthetic data is generated using a factor based model with t copula residuals, enabling training beyond the li...

Source →

- Citations

Paper Information Summary
Entity Passport
Registry ID	2604.14206
License	arXiv
Provider	arxiv

📜

Cite this paper

Academic & Research Attribution

BibTeX

@misc{arxiv_2604_14206,
  author = {Adhiraj Chattopadhyay},
  title = {Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2604.14206}},
  note = {Accessed via Free2AITools.}
}

APA Style

Adhiraj Chattopadhyay. (2026). Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training [Paper]. Free2AITools. https://arxiv.org/abs/2604.14206

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0

Popularity (P) 0

Recency (R) 71

Quality (Q) 60

💬 Index Insight

FNI V2.0 for Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training: Authority (A:0), Popularity (P:0), Recency (R:71), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"This paper proposes a machine learning assisted portfolio optimization framework designed for low data environments and regime uncertainty. We construct a teacher student learning pipeline in which a Conditional Value at Risk (CVaR) optimizer generates supervisory labels, and neural models (Bayesian and deterministic) are trained using both real and synthetically augmented data. The synthetic data is generated using a factor based model with t copula residuals, enabling training beyond the li..."

❝ Cite Node

@article{Chattopadhyay2026Portfolio,
  title={Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training},
  author={Adhiraj Chattopadhyay},
  journal={arXiv preprint arXiv:2604.14206},
  year={2026}
}

👥 Collaborating Minds

Adhiraj Chattopadhyay

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📅1970Published

⏱️71RecencyFNI pillar

✅60QualityFNI pillar

🗂️cs.LGField

📄 Data Source: arXiv ↗

🔄 Updated daily

Source summary: Based on arXiv metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: 2604.14206
slug: 2604.14206
source: arxiv
author: Adhiraj Chattopadhyay
license: arXiv
tags: arxiv:cs.LG, arxiv:q-fin.PM, arxiv:stat.ML

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: 0
forks: 0

Data indexed from public sources. Updated daily.