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.

168,878 papers · 148 categories

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11 results for “Optuna”

Optuna: A Next-generation Hyperparameter Optimization Frameworkcs.LG

Optuna introduces a new hyperparameter optimization framework.

problem Efficient hyperparameter optimization for machine learning models.

method Define-by-run API, efficient implementation, versatile architecture.

result Optuna is the first software meeting next-generation hyperparameter optimization criteria.

hyperparameter optimization define-by-run efficient implementation versatile architecture Optuna

2019-07-25abs ↗pdf ↗

EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methodsq-fin.CP

IUS framework predicts EUR/USD exchange rate with improved accuracy.

problem Accurate forecasting of EUR/USD exchange rate.

method Combines large language models for sentiment analysis, deep learning for forecasting, and feature selection.

result Optuna-optimized Bi-LSTM model reduces MAE and RMSE by 10.69% and 9.56% respectively.

EUR/USD exchange rate forecasting information fusion large language models deep learning

2024-08-23abs ↗pdf ↗

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddingscs.LG

PyKEEN 1.0 simplifies KGE model creation and optimization.

problem Training and evaluating knowledge graph embeddings (KGEs).

method Composes KGEMs with various interaction models, training approaches, and loss functions. Implements automatic memory optimization and extensive HPO functionalities.

result PyKEEN 1.0 streamlines KGE model creation and optimization.

knowledge graph embeddings knowledge graph embedding models training approaches loss functions

2020-07-28abs ↗pdf ↗

A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluationcs.CL

Study compares DSPy teleprompter algorithms for aligning LLM evaluations with human annotations.

problem Aligning LLM evaluation metrics with human annotations.

method Comparative analysis of five teleprompter algorithms within the DSPy framework.

result Certain teleprompters outperform others in detecting hallucinations.

DSPy teleprompter LLM alignment hallucination

2024-12-19abs ↗pdf ↗

Integrating the implied regularity into implied volatility models: A study on free arbitrage modelq-fin.CP

Study integrates implied Hurst exponent into IV models for better market efficiency.

problem Capturing market efficiency in IV models based on moneyness.

method Developed an IV model integrating implied Hurst exponent H, optimizing across multiple indexes.

result Model outperforms SABR and fSABR in accuracy, capturing IV-H dynamics.

implied volatility Hurst exponent market efficiency financial markets options pricing

2025-02-11abs ↗pdf ↗

mlr3mbo: Bayesian Optimization in Rstat.ML

mlr3mbo is a modular R toolbox for Bayesian optimization.

problem Efficiently solving optimization problems with multiple objectives and constraints.

method Bayesian optimization with support for multi-objective, multi-point proposals, parallelization, and custom algorithms.

result mlr3mbo performs competitively with state-of-the-art optimizers and robustly handles various optimization regimes.

Bayesian optimization R multi-objective parallelization coordinate descent

2026-03-31abs ↗pdf ↗

News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Modelsq-fin.ST

The paper compares advanced deep learning models for Indian stock price forecasting.

problem Complexity of stock price forecasting due to numerous influencing factors.

method Utilizes historical data from national banks in India, combines deep learning models and sentiment analysis.

result Achieved higher accuracy in stock price forecasting compared to traditional methods.

stock price forecasting deep learning sentiment analysis Indian markets natural language processing

2024-10-14abs ↗pdf ↗

XGBoost Forecasting of NEPSE Index Log Returns with Walk Forward Validationcs.LG

XGBoost predicts NEPSE Index log returns with low error and high directional accuracy.

problem Forecasting daily log-returns in the NEPSE Index with high accuracy.

method XGBoost machine learning, feature engineering, hyperparameter optimization, walk-forward validation.

result Optimal XGBoost configuration achieves lowest log-return RMSE and MAE.

XGBoost NEPSE Index log returns walk-forward validation feature engineering

2026-01-13abs ↗pdf ↗

Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approachcs.LG

Study compares AI models for stock price prediction using financial news.

problem Predicting stock price movements using financial news.

method Used FinBERT, GPT-4, and Logistic Regression for sentiment analysis and prediction.

result Logistic Regression outperformed FinBERT and GPT-4, achieving 81.83% accuracy.

AI models stock price prediction sentiment analysis financial news Logistic Regression

2024-12-07abs ↗pdf ↗

ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimizationcs.LG

ALMAB-DC optimizes expensive black-box experiments using active learning and distributed computing.

problem Efficiently optimizing expensive, gradient-free objectives in computational statistics and machine learning.

method Combines active learning, multi-armed bandits, and distributed asynchronous computing.

result Achieves lower simple regret and superior performance in various tasks compared to non-ALMAB baselines.

active learning multi-armed bandits distributed computing sequential design black-box optimization

2026-03-22abs ↗pdf ↗

Mamba Outpaces Reformer in Stock Prediction with Sentiments from Top Ten LLMsq-fin.ST

Mamba outperforms Reformer in minute-level stock prediction using LLM sentiment scores.

problem Improving minute-level stock market prediction accuracy in volatile markets.

method Combining sentiment scores from top LLMs with stock price data, training Mamba and Reformer models.

result Mamba achieved lower error rates across all tested LLMs, especially with LLaMA 3.3--70B.

stock prediction LLM sentiment analysis minute-level forecasting financial time series large language models

2025-09-14abs ↗pdf ↗