The paper examines MAPE's use in regression models and its implications.
problem The use of Mean Absolute Percentage Error (MAPE) as a quality measure for regression models.
method Proves the existence of an optimal MAPE model, shows universal consistency of Empirical Risk Minimization based on MAPE, and demonstrates the equivalence of MAPE model selection to weighted MAE regression.
result Finding the best model under MAPE is equivalent to weighted MAE regression, and this strategy is applied to kernel regression.
We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression. We show that universal consistency of Empirical Risk Minimiza…
We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression. We also show that, under some asumptions, universal consisten…
The paper explores MAE as a loss function for DNN vector-to-vector regression, proving its advantages over MSE.
problem Improving loss function for deep neural network based vector-to-vector regression.
method Presenting performance bounds and new properties of MAE, deriving generalized upper bounds, and interpreting MAE as a Laplacian distribution.
result MAE is a more suitable loss function than MSE for DNN based vector-to-vector regression, especially when errors follow a Laplacian distribution.
The paper bounds the mean absolute error in DNN vector-to-vector regression.
problem Bounding the mean absolute error in deep neural network based vector-to-vector regression.
method Error decomposition techniques in statistical learning theory and non-convex optimization theory were used to derive upper bounds for approximation, estimation, and optimization errors.
result Theoretical upper bounds for mean absolute error in DNN vector-to-vector regression were derived and validated experimentally.
New insights into MAE show it treats examples unequally and IMAE improves this.
problem Noise-robust learning in deep learning models.
method Analysis of MAE's noise-robustness and proposing IMAE to improve it.
result IMAE improves MAE's fitting ability while preserving its noise-robustness.
Paper proposes a new metric to evaluate survival models, especially for censored data.
problem Challenges in evaluating survival prediction models due to censored data.
method Developed a novel approach to estimate Mean Absolute Error (MAE) for survival datasets with censored data.
result The proposed MAE metric using pseudo-observations accurately ranks model performance and closely matches true MAE.
Machine learning predicts Bitcoin price with high accuracy.
problem Uncertainty in Bitcoin price prediction for investors.
method Used machine learning techniques with technical indicators.
result Stacking ensemble model with random forest and GLM is optimal.
Study compares machine learning algorithms for predicting SST in the Great Barrier Reef.
problem Predicting sea surface temperature in the Great Barrier Reef region.
method Ridge regression, LASSO, Random Forest, and Extreme Gradient Boosting (XGBoost) algorithms were evaluated.
result XGBoost significantly outperforms other algorithms in terms of predictive accuracy and Kullback-Leibler Divergence.
This paper models yearly exchange rates between USD/KZT, EUR/KZT and SGD/KZT, and compares the actual data with developed forecasts using time series analysis over the period from 2006 to 2014. The official yearly data of National Bank of the Republic of Kazakhstan is used for present study. The main goal of this paper…
Study compares MoE and RNN models for stock price prediction across volatility profiles.
problem Improving stock price prediction accuracy across different volatility levels.
method Dynamic Mixture of Experts model combining RNN and linear models, adjusting weights through a gating network.
result MoE model outperforms individual models in reducing prediction errors.
Proposes a new loss function for learning with noisy labels.
problem Improving model learnability with noisy labels.
method Uses generalized Jensen-Shannon divergence as a noise-robust loss function.
result Shows state-of-the-art results on noisy data.
Predicting survival in PUBG game with machine learning models.
problem Predicting the final placement in the game PUBG.
method Applied multiple machine learning models including LightGBM Regression, MultiLayer Perceptron, M5P, and Random Forest to predict winPlacePerc.
result Achieved an MAE of 0.02047, 0.065, 0.0592, and 0.0634 with different models.
Models predict probabilities of causation from limited data.
problem Estimating probabilities of causation requires unreliable or impractical experimental and observational data.
method Proposed Exact-MLP and Mask-MLP models trained on reliable subpopulations.
result Models achieve average MAEs of roughly 0.03, reducing MAE by 80%.
The paper compares machine learning models for forecasting residential gas demand, highlighting the impact of temperature forecasts.
problem Forecasting residential gas demand for optimal energy planning.
method Implemented and compared five models: Ridge Regression, GP, k-Nearest Neighbour, ANN, and Torus Model.
result ANN is the best model in terms of RMSE, while GP is the best in terms of MAE.
A visualization aids in comparing regression models by highlighting errors and correlations.
problem Comparing regression models is difficult due to varying hyper-parameters and metrics.
method Introduces a novel visualization approach using 2D residual space, Mahalanobis distance, and colormaps.
result Enhanced understanding of regression model performance differences and error distributions.
Predicts individual septic shock children's vasoactive response using RNN.
problem Personalized physiologic responses to vasoactive titrations in septic shock children.
method Retrospective analysis of EMR data using a Recurrent Neural Network (RNN).
result RNN model predicted physiologic responses more accurately than a linear model.
Study forecasts Bitcoin prices using ML algorithms.
problem Accurately predicting Bitcoin price movements.
method Applied four ML algorithms: SVM, ANN, NB, RF, and LR.
result RF outperforms other models in continuous dataset, NB in discrete.
New loss functions improve DNN performance with noisy labels.
problem Deep neural networks struggle with noisy training labels.
method Generalized cross entropy loss functions for robust training.
result Proposed loss functions enhance DNN performance in noisy scenarios.
Study uses ML and causal analysis to predict student performance factors.
problem Understanding socio-academic and economic factors affecting student performance.
method Employed machine learning techniques and causal analysis on 1,050 student profiles.
result Ridge Regression achieved robust predictions with MAE of 0.12 and MSE of 0.024.
New methods improve insurance data quality for catastrophic events.
problem Improving precision and size of insurance data for catastrophic events.
method Bootstrap, bootknife, and GAN algorithms.
result Compared MSE and MAE of simulated outputs, direct algorithm for fuzzy expert opinion.
This study compares deep learning and statistical models for stock price forecasting.
problem Accurate stock price prediction is challenging due to market volatility.
method Used deep learning (LSTM, RNN, CNN, FULL CNN) and statistical models (ARIMA, Moving Averages) on S&P 500 data.
result LSTM model showed the lowest Mean Absolute Error (MAE), indicating highest accuracy.
A novel algorithm predicts customized allergy seasons using multi-variate triple-regression.
problem Predicting customized allergy seasons for individual patients.
method Triple-regression algorithm with pre-processing and three-stage regressions.
result Improved forecasting accuracy and reduced uncertainty.
Proposes MGCE for improved classification performance.
problem Optimizing between robustness and optimization difficulty in classification.
method Minimax formulation of GCE leading to convex optimization over margins.
result MGCE achieves strong accuracy and better calibration, especially in noisy labels.
Optimizes shortfall risk using gradient-based methods.
problem Optimizing utility-based shortfall risk measures.
method Gradient-based stochastic optimization, non-asymptotic bounds derivation.
result Non-asymptotic convergence rate for optimizing UBSR.
LSTM models outperform traditional ARIMA in S&P 500 forecasting.
problem Forecasting volatile financial data with non-linear dependencies.
method Compared LSTM and ARIMA models using historical data and technical indicators.
result LSTM models outperformed ARIMA in accuracy and error metrics.
LALR adapts learning rate for faster convergence in regression and neural nets.
problem Finding optimal learning rates for faster convergence in regression and neural networks.
method Lipschitz continuity theory applied to Mean Absolute Error and Quantile loss functions.
result Adaptive learning rate policy enables up to 20x faster convergence.
New framework assesses extreme errors in machine learning models.
problem Current validation methods fail to quantify extreme errors in high-stakes domains.
method Uses Extreme Value Theory (EVT) to estimate worst-case failures.
result Establishes EVT as a fundamental tool for assessing model reliability.
The study improves VaR forecast accuracy by modeling conditional quantile dynamics.
problem Improving the accuracy of Value-at-Risk (VaR) forecasts for time-varying quantiles.
method Time-varying modeling of VaR, evaluation via simulation, asymmetric Mean Absolute Deviation loss function.
result Substantial improvements in forecasting conditional quantiles by maintaining predicted quantile unchanged.
New model predicts sales of new products with short life cycles.
problem Forecasting sales of new products with short lead times and life cycles.
method Developed an exponential factorization machine (EFM) to consider attributes and pairwise interactions.
result EFM model outperforms existing models in terms of MAPE and MAE.
Method converts age labels into distributions to improve speaker age estimation.
problem Label ambiguity in age labels makes precise speaker age estimation challenging.
method Converts age labels into label distributions and uses label distribution learning.
result Our method outperforms baseline methods by reducing MAE by 10% on a real-world dataset.
Method estimates parameters of complex nonlinear systems.
problem Parameter estimation for nonlinear systems with derivative states.
method Regularized linear regression using differentiation filtering and least squares.
result Finite-sample bound on mean absolute error of estimation.
Deep learning models predict acute oral toxicity with high accuracy.
problem Challenges in interpreting relationships between chemical properties and features.
method Molecular graph encoding convolutional neural networks (MGE-CNN) for automatic feature extraction.
result Deep learning models outperform previous methods in predicting AOT with high accuracy.
Robustly estimates linear regression coefficients with adversarial and noisy data.
problem Estimating robust linear regression coefficients with adversarial and noisy data.
method Adversarial robust weighted Huber regression with polynomial computational complexity.
result Derives an estimation error bound that depends on the stable rank and condition number of the covariance matrix.
ANN model predicts zinc leaching filter cake moisture accurately.
problem Modeling cake moisture in zinc leaching pressure filtration.
method Developed ANN model using 7 parameters.
result High accuracy in predicting cake moisture (R2 > 0.8, MSE < 1e-6).
Predict stock prices using financial news sentiment analysis.
problem Predicting stock market trends for better investment returns.
method Deep Learning (MLP, LSTM, FinBERT-LSTM) integrating news sentiment.
result FinBERT-LSTM model predicts stock prices more accurately.
XGBoost outperforms other models in predicting housing prices.
problem Accurate housing price prediction for socio-economic development.
method Employed XGBoost and other machine learning algorithms on housing price datasets.
result XGBoost outperformed other models in predicting housing prices.
This paper compares ML models for predicting COVID-19 trends.
problem Forecasting the spread of COVID-19 to reduce its impact.
method Applied ARDL method to identify relationships, then used ML models (SVM, RF, KNN, ANN) for prediction.
result Models accurately forecasted COVID-19 cases with low MAPE.
Deep neural network improves CT synthesis from MRI.
problem Generating accurate CT images from MRI for PET reconstruction.
method Deep fully convolutional neural network that recursively reduces residuals.
result Decreased PET reconstruction error from 14.3% to 7.2%.
NARME loss function speeds up neural network training for regression models.
problem Training neural networks on large datasets is time-consuming.
method Introducing Nth Absolute Root Mean Error (NARME) loss function.
result NARME reduces training time by up to 90% compared to other loss functions.
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.
This paper achieves optimal regret bounds for locally private linear contextual bandit.
problem Designing locally private linear contextual bandit algorithms with optimal regret bounds.
method New algorithmic and analytical ideas, including mean absolute deviation analysis and layered principal component regression.
result Achieves an i l d e O ( T ) ilde O(\sqrt{T}) i l d e O ( T ) regret upper bound for locally private linear contextual bandit. MTCNet uses MTL to estimate crowd density and count.
problem Crowd count estimation challenges due to scale variations and perspective.
method MTL deep neural network architecture with two tasks: density estimation and count classification.
result Achieves lower MAE than state-of-the-art methods on multiple datasets.
Neural networks predict stock prices better than traditional methods.
problem Predicting stock prices in volatile financial markets.
method Compared five neural network models (BP, RBF, GRNN, SVMR, LS-SVMR) on three stocks.
result BP neural network outperformed other models in accuracy.
Graph neural controlled differential equations learn graph dynamics from vertex observations.
problem Predicting future states of dynamical systems on graphs with limited vertex data.
method Incorporates graph topology information into NCDE to predict graph dynamics.
result Informed NCDE requires fewer parameters and lower MAE compared to previous methods.
Hybrid model outperforms benchmarks in financial forecasting.
problem Robust asset price forecasting in finance.
method Combining LSTM with Neural Levy Processes using Grey Wolf Optimizer and ANN calibration.
result Hybrid model outperforms base LSTM and other models.
A new framework for semi-supervised ordinal regression.
problem Lack of evaluation metrics and theoretical guarantees in existing semi-supervised ordinal regression.
method Empirical risk minimization principle, flexible model choices, and estimation error bound.
result Consistent risk estimator and improved performance across various metrics.
Deep model improves option pricing for CSI 300 index with sentiment and volatility features.
problem Challenges in real market option pricing, especially with constant volatility assumption.
method Deep Forward-Backward Stochastic Differential Equation (FBSDE) framework with dual-network architecture.
result Significant reduction in MAE and MAPE compared to BSM model.