Paper presents a robust model to improve prediction accuracy for real-life mobile phone data.
problem Noisy instances in real-life mobile phone data affect model accuracy.
method Identify and eliminate noisy instances using naive Bayes and Laplace estimators, then build a decision tree model.
result The robust model improves prediction accuracy as shown by experimental results.
Experiment sets personalized emotion baselines for real-life prediction.
problem Establishing reliable ground-truth estimates for real-life emotion recognition.
method Controlled experiment with adaptive stimuli selection and user feedback.
result 85% accuracy in predicting emotional state with few features.
Study compares resampling methods for rare event prediction in longitudinal studies.
problem Predicting rare events in longitudinal follow-up studies.
method Comparison of resampling methods to improve standard regression models.
result Effect of sampling rate on model predictive performance.
A new model predicts spatio-temporal data using adaptive decision trees and point processes.
problem Predicting spatio-temporal data with real-life applications.
method Hawkes process, adaptive decision tree, joint optimization algorithm.
result Significant improvement in predictions compared to standard methods.
New algorithm predicts spatio-temporal events with improved accuracy.
problem Non-stationary spatio-temporal prediction on dense and sparse sequences.
method Probabilistic approach using point processes and self-organizing decision trees.
result Significant performance improvements over baseline and state-of-the-art methods.
Markovian RNN adapts to nonstationary data using HMM for better time series prediction.
problem Nonstationary sequential data in real-life applications.
method Markovian RNN with HMM for regime switching and end-to-end optimization.
result Significant performance gains over vanilla RNN and Markov Switching ARIMA.
Cloud-based BCI predicts epileptic seizures from big EEG data.
problem Nonstationary EEG signals and big data storage/computational challenges.
method Dimensionality reduction, stacked autoencoder, cloud computing.
result Proposes a patient-specific BCI system for real-time seizure prediction.
The paper adds explanation to predictive process monitoring.
problem Equipping predictive business process monitoring with explanation capabilities.
method Used game theory of Shapley Values to obtain robust explanations.
result First time explanations given in predictive business process monitoring.
Enhances kernel regression with network data for better predictions.
problem Improving predictive power in high-dimensional data.
method Combines kernel regression with network cohesion data to model nonlinearities.
result Significantly better predictive performances in high-dimensional data.
Proposes models for learning latent representations of evolving network vertices over time.
problem Inadequate models for capturing temporal smoothness in evolving networks.
method Proposes two models: retrofitted and linear transformation, to capture temporal smoothness.
result Proposed models significantly outperform existing models in temporal link prediction tasks.
This paper evaluates how different imputation methods affect predictive models.
problem The impact of different imputation methods on predictive models' performance.
method Systematic evaluation of various imputation methods for different data sets and machine learning algorithms.
result Recommendation of a general method for empirical benchmarking of imputation methods.
End-to-end deep learning detects emotions in real-life emergency calls.
problem Recognizing emotions in real-life emergency call center recordings.
method Used an end-to-end deep learning architecture trained on IEMOCAP and CEMO datasets.
result Obtained 45.6% Unweighted Accuracy Recall on CEMO with 4 classes, 76.9% on 2 classes (Anger, Neutral).
Local elasticity in neural networks makes predictions resilient to dissimilar updates.
problem Understanding resilience of neural network predictions to updates from dissimilar data.
method Simulation and geometric interpretation using neural tangent kernel.
result Local elasticity persists in neural networks with nonlinear activation functions, not in linear ones.
New bound for neural nets on non-iid data.
problem Generalization of deep nets for dependent data.
method Establishes a generalization bound for feed-forward neural networks on φ-mixing data. result Proves neural nets can generalize well on non-iid data.
Accelerated Gradient Boosting improves performance and sparsity of predictions.
problem Improving prediction accuracy and sparsity in machine learning models.
method Combining gradient boosting with Nesterov's accelerated descent.
result Accelerated Gradient Boosting (AGB) outperforms traditional gradient boosting in terms of sparsity and sensitivity to shrinkage parameters.
The paper predicts survival functions using random survival trees and concordance maximization.
problem Predicting conditional survival functions in right-censored data.
method The approach combines regression strategies with random survival trees and maximizes concordance.
result The proposed weighted predictor outperforms the usual survival cobra in terms of concordance.
Study shows sample noise impacts active learning performance.
problem Impact of sample noise on active learning performance.
method Proposed Incremental Weighted K-Means for noisy samples.
result Robust sampler improves synthetic tasks but only marginally in real-life.
Develops auction theory for real-life applications with positive valuations.
problem Real-life auction settings with positive valuations and interdependent bidders.
method Approximations using log-normal distribution, positive symmetric discrete distribution, and interdependent valuations.
result New auction theory results applicable to finance and procurement.
Tomal et al. propose Regression Phalanxes for better regression predictions.
problem Improving regression prediction accuracy in high-dimensional data.
method Hierarchical clustering to automatically choose Regression Phalanxes, building models for each phalanx, ensembling.
result Regression Phalanxes improve prediction accuracy compared to effective methods like Lasso or Random Forests.
End-to-end model predicts ATR rehabilitation outcomes from missing data.
problem Predicting rehabilitation outcomes for Achilles Tendon Rupture patients from incomplete data.
method Probabilistic framework for simultaneous imputation and prediction.
result Proposed method outperforms traditional methods in predicting ATR rehabilitation outcomes.
Exclusive Lasso improves survival prediction in cancer datasets.
problem Enhanced survival prediction in cancer datasets with high-dimensional genomic and clinical data.
method Proposes Exclusive Lasso regularization for feature selection in Cox regression models for grouped variables.
result Demonstrates improved survival prediction performance using Exclusive Lasso compared to standard Cox regression.
This paper provides a theoretical and computational justification of the long held claim that of the similarity of the probit and logit link functions often used in binary classification. Despite this widespread recognition of the strong similarities between these two link functions, very few (if any) researchers have …
RegFlow models future states with flexible probability distributions.
problem Predicting future states under complex, non-deterministic scenarios.
method Hypernetwork architecture and continuous normalizing flow model.
result RegFlow achieves state-of-the-art results on benchmark datasets.
New graph AE and VAE model predicts directed links better than existing methods.
problem Link prediction in directed graphs, especially for unobserved edges.
method Gravity-inspired decoder scheme for directed graphs.
result Outperforms standard graph AE and VAE on three real-world directed link prediction tasks.
Neural network feature optimization for causal inference.
problem Estimating heterogeneous treatment effects from data.
method Genetic algorithm optimization of intermediate neural network layers for feature representations.
result Retains useful features for outcome prediction even if related to treatment assignment.
The paper improves neural network predictions by integrating process knowledge.
problem Improving neural network predictions for process execution data.
method Integrates background process knowledge into neural networks with attention mechanisms.
result Improves prediction accuracy for process execution data.
Paper proposes a new combined regression strategy for conditional survival prediction.
problem Improving survival prediction accuracy using conditional survival function.
method Uses regression-based weak learners with area-norm proximity measure to create an ensemble technique.
result The proposed model outperforms Random Survival Forest and selects important variables effectively.
MCC algorithm predicts with partial modalities, outperforming full modalities.
problem Predicting with inconsistent and diverse multi-modal data.
method Instance-oriented Multi-modal Classifier Chains (MCC) algorithm.
result MCC outperforms full modalities in prediction.
A new framework predicts multi-step time series with stability and efficiency.
problem General probabilistic multi-step time series regression challenges.
method Sequence-to-Sequence Neural Networks, Quantile Regression, Direct Multi-Horizon Forecasting, Forking-sequences training scheme.
result Framework accommodates temporal and static covariates, learning across multiple series.
The study examines denoising and noisy-input regression under distribution shift, revealing double descent behavior and insights for data augmentation.
problem Understanding denoising in machine learning, especially under noisy inputs and distribution shift.
method Theoretical analysis of supervised denoising and noisy-input regression, considering low-rank data and proportional regime.
result The test error exhibits double descent under general distribution shift, indicating that overfitting the noise can be benign, tempered, or catastrophic.
Deep learning predicts semitransparent pigment mixtures for novice painters.
problem Support novice painters in learning color mixing.
method Built a watercolor dataset with transmittance and reflectance data. Used a deep neural network to train a model for predicting pigment mixtures.
result Trained model accurately predicts semitransparent pigment mixtures.
Prediction markets are used in real life to predict outcomes of interest such as presidential elections. This paper presents a mathematical theory of artificial prediction markets for supervised learning of conditional probability estimators. The artificial prediction market is a novel method for fusing the prediction …
TEASER improves early time series classification accuracy and speed.
problem Early and accurate classification of time series data.
method TEASER models eTSC as a two-tier classification problem, using a first-tier classifier to assess class probabilities and a second-tier to decide reliability.
result TEASER is two to three times faster at predictions than competitors while maintaining or improving accuracy.
Superensemble classifier improves predictions in imbalanced datasets.
problem Imbalanced datasets lead to poor performance in minority class examples.
method Maps Hellinger distance decision trees into RBFN framework for universal consistency and parameter optimization.
result The proposed model outperforms state-of-the-art models in various real-life data sets.
Neural eliminators reduce unreliable classification by eliminating improbable classes.
problem Unreliable classification due to noise, insufficient data, overlapping distributions, and unclear class definitions.
method Construct eliminators using classifiers with modified error functions, assigning cases to multiple classes instead of one.
result Elimination of improbable classes improves classification accuracy in real-life medical applications.
Paper predicts future sales using machine learning techniques.
problem Predict future sales of retail products using past data.
method Used eXtreme Gradient Boosting (XGBoost) and Long Short Term Memory (LSTM) for forecasting.
result XGBoost performed better than LSTM in predicting sales.
SurvivalBoost improves prediction of event times in competing risks scenarios.
problem Predicting event times in scenarios with multiple possible outcomes.
method Developed a strictly proper censoring-adjusted scoring rule for stochastic optimization of competing risks.
result SurvivalBoost outperforms 12 state-of-the-art models across various metrics.
AdaVol adapts QML for real-time GARCH volatility prediction.
problem Real-time estimation of GARCH volatility in streaming data.
method Adaptive recursive estimation routine with Variance Targeting Estimation.
result AdaVol provides a stable and adaptive method for real-life data.
Extends Demographic Parity for fairer wage predictions with expert knowledge.
problem Inadequate fairness metrics limit user domain knowledge and ignore intersectional fairness.
method Develops a parametric method to incorporate expert knowledge in fair predictions.
result Offers a robust solution for real-life applications with limited data and spending constraints.
Review of childhood data for predicting overweight and obesity in later life.
problem Predicting overweight and obesity in later life using childhood data.
method Bibliographic searches and iterative searching of references.
result High performance prediction models often use short time periods or late childhood data.
Proposes ANN for robust sensor data prediction.
problem Predicting component health from noisy, failing sensors.
method Artificial Neural Network framework with data augmentation.
result Accurate predictions despite noisy sensor data.
We analyze empirical data for 4,000 real-life trading portfolios (U.S. equities) with holding periods of about 0.7-19 trading days. We find a simple scaling C ~ 1/T, where C is cents-per-share, and T is the portfolio turnover. Thus, the portfolio return R has no statistically significant dependence on the turnover T. W…
New approach to fairness in machine learning through stochastic optimization.
problem Fairness issues in machine learning predictions.
method Stochastic multi-objective optimization for accuracy-fairness trade-offs.
result Well-spread and accurate Pareto fronts for handling streaming data.
The paper shows FtU can reduce discrimination without sacrificing accuracy.
problem Discrimination in machine learning predictions.
method Theoretical and empirical analysis of FtU, connecting with Model Multiplicity.
result FtU can reduce discrimination without reducing accuracy.
DPLP predicts links while protecting some node-pairs' privacy.
problem Link prediction with protected connections in private networks.
method DPLP uses differential privacy on graphs to protect node-pairs, applying a monotone transform and noise to base scores.
result DPLP effectively balances privacy and link prediction accuracy.
HL-VAE extends VAE for heterogeneous temporal and longitudinal data.
problem Handling heterogeneous data in temporal and longitudinal datasets.
method Proposes HL-VAE, an extension of existing VAEs for temporal and longitudinal data, incorporating likelihood models for various data types.
result HL-VAE achieves competitive performance in missing value imputation and predictive accuracy.
Improved pedestrian crossing prediction for AVs using contextual factors.
problem Accurate prediction of pedestrian crossing behavior for autonomous vehicles.
method Factored Latent-Dynamic Conditional Random Fields (FLDCRF) for multi-label sequence prediction and joint interaction modeling.
result Achieved at least 0.9 seconds earlier prediction accuracy for pedestrian crossing behavior compared to existing methods.
KernelCobra combines multiple predictors using a kernel to improve prediction performance.
problem Combining multiple predictors for better classification and regression.
method Kernel-based ensemble learning using COBRA algorithm with kernel smoothing.
result KernelCobra outperforms COBRA in classification and regression tasks.