We introduce a unified framework for random forest prediction error estimation based on a novel estimator of the conditional prediction error distribution function. Our framework enables simple plug-in estimation of key prediction uncertainty metrics, including conditional mean squared prediction errors, conditional bi…
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48 results for “predictive framework”
Proposes CRA framework for certifying fair predictive models.
problem Certifying fairness of predictive models trained on biased data.
method Formulates CRA for fairness queries, uses background knowledge and limited target population stats.
result Builds certifiably fair predictive models on target population.
This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the …
Hybrid model predicts stock prices using online forum sentiments and popularity.
problem Predicting stock prices accurately considering investor sentiment.
method XLNET for sentiment analysis, BiLSTM-highway model integration, combining post popularity.
result Hybrid model outperforms traditional methods in stock price prediction.
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into r…
In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles. The majority of current researches focused on the prediction of an single entity without incorporating the environment information. Although some approache…
Enhances fairness in predictions without sacrificing accuracy.
problem Balancing fairness and predictive performance in machine learning.
method Model ensemble-based post-processing framework.
result Framework effectively enhances fairness while maintaining predictive accuracy.
Framework predicts mortality risk in MAFLD subjects.
problem Lack of mortality prediction methods for MAFLD subjects.
method Artificial Intelligence-based framework MAFUS using ML algorithms.
result Support Vector Machines (SVM) is the best model for mortality prediction.
The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear. While several studies have focused on developing prediction models with meaningful parameters, the causal relationships between the predictors and the actual prediction have not been considered. Here, we co…
Paper proposes a new framework for predictive optimization without training data.
problem Prediction in a new domain without training samples.
method Proposes a simple framework for predictive optimization with zero-shot domain adaptation.
result Demonstrates the potential usefulness of the proposed framework through numerical experiments.
Framework for assessing fairness across similar predictive models.
problem Fairness in predictive models across different groups.
method Develops a framework for characterizing fairness over the set of good models under selective labels.
result Framework can replace or audit models for better fairness properties.
Unified framework for generalized Venn and Venn-Abers calibration for reliable prediction.
problem Asymptotic guarantees of popular distribution-free methods in model calibration.
method Unified framework extending Vovk's approach to generic loss functions, transforming predictors into set-valued predictions.
result Finite-sample set predictions shrink to a single conditionally calibrated prediction, capturing epistemic uncertainty.
Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Predictionq-fin.TR
A new distillation framework predicts stock trading volumes more accurately with less model size.
problem Predicting stock trading volumes using regression models without class correlations.
method Transformed regression model into a probabilistic forecasting model, matching distributions and correlational relationships.
result Framework achieves superior prediction accuracy with significantly smaller model size.
We improve prediction set coverage by assigning weights to individual sets.
problem Aggregating multiple prediction sets weakens overall coverage guarantee.
method Propose a framework for weighted aggregation of prediction sets.
result Achieve tighter coverage bounds that interpolate between and guarantees.
A framework for private prediction sets using conformal prediction and differential privacy.
problem Jointly addressing reliability and privacy in machine learning predictions.
method Split conformal prediction with privatized quantile subroutine.
result Private prediction sets can be generated from privately-trained models.
Develops a hybrid deep learning model for stock price prediction.
problem Predicting daily stock prices in the stock market.
method Representation learning with Stock2Vec embedding and temporal convolutional layers.
result Achieves better performance on stock price prediction than benchmarks.
Framework mitigates risk non-monotonicity in high-dimensional predictions.
problem Risk non-monotonicity in high-dimensional predictions.
method Model-agnostic framework using cross-validation and data-driven methodologies (zero- and one-step).
result Modified prediction procedures achieve monotonic asymptotic risk behavior.
Framework predicts implied volatility surface without arbitrage.
problem Predicting implied volatility surface without static arbitrage.
method Two-step framework: feature selection and deep neural network (DNN) construction.
result DNN model for surface construction removes static arbitrage and reduces prediction error.
JAPAN uses flow-based models to create adaptive prediction areas with better coverage guarantees.
problem Inadequate prediction areas from existing conformal prediction methods, especially for multimodal distributions.
method JAPAN employs density-based conformity scores using flow-based models to construct context-adaptive prediction areas.
result JAPAN produces more accurate and context-adaptive prediction areas compared to existing methods.
Deep learning architectures have proved versatile in a number of drug discovery applications, including the modelling of in vitro compound activity. While controlling for prediction confidence is essential to increase the trust, interpretability and usefulness of virtual screening models in drug discovery, techniques t…
Unified framework denoises data and abstains from uncertain predictions.
problem Data quality and predictive uncertainty in deep neural networks.
method Unified filtering framework leveraging data density.
result Framework outperforms state-of-the-art techniques in denoising and abstaining.
A new framework predicts links in time-dependent networks using Bernoulli autoregression.
problem Predicting links in time-dependent networks with additional auxiliary information.
method A Bernoulli autoregressive model with regularization for link discovery.
result The model can discover new links not present in the data.
dboost optimizes prediction models for convex cone problems.
problem Optimizing prediction models for decision-making.
method Gradient boosting with implicit differentiation for convex quadratic cone programming.
result dboost reduces out-of-sample decision regret.
Proposes a test to ensure predictive algorithms predict intended outcomes better than unintended ones.
problem Unintended model behavior leading to prediction of unintended outcomes.
method Falsification framework using nonparametric hypothesis testing to compare prediction losses across outcomes.
result Establishes discriminant validity with respect to gender but not race in an admissions setting.
Prediction-Powered Inferencestat.ML
Valid inference from data and predictions.
problem Valid statistical inference with machine learning predictions.
method Framework for valid inference using machine learning predictions.
result Valid confidence intervals without assumptions on predictions.
Boosting framework for vector-valued prediction with geometric stability.
problem Lack of a general theoretical understanding of aggregation for structured prediction.
method Identifies -stability property and proposes a boosting framework based on exponential reweighting and geometric-median aggregation.
result Obtains exponential decay of empirical divergence error under weak learner condition and -stability.
A Distribution-to-Distribution Neural Probabilistic Forecasting Framework for Dynamical Systemsstat.ML
Develops a neural framework for probabilistic forecasting of dynamical systems.
problem Uncertainty quantification in dynamical systems using trajectory-oriented approaches.
method D2D neural probabilistic forecasting framework using kernel mean embeddings and mixture density networks.
result The D2D model captures distributional evolution in chaotic systems and produces skillful probabilistic forecasts.
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictionscs.LG
Flexible framework for bounding high-loss predictions using quantiles.
problem Need for rigorous guarantees in risk-sensitive applications.
method Order statistics of loss values, flexible quantile-based metrics.
result Ability to rigorously control loss quantiles on real-world datasets.
CPPS selects portfolios using conformal prediction for better returns.
problem Optimizing portfolio returns with predictive models and uncertainty.
method Conformal prediction framework for portfolio selection.
result CPPS outperforms simpler strategies in delivering superior returns.
Bayesian framework for online consensus prediction from expert feedback.
problem Online classification with expert consensus prediction, cost-effective.
method General Bayesian framework for dynamic expert consensus estimation.
result Demonstrated superior performance on large-scale crowdsourced datasets.
Proposes a new bankruptcy prediction model using Bayesian framework with expert knowledge.
problem Bankruptcy prediction using accounting ratios and statistical models.
method Generative modeling with Expert Bayesian framework.
result Model is either comparable or superior to existing methods with lower False Positive Rate.
Proposes a falsification framework to test algorithmic discriminant validity.
problem Unintended model behavior in predictive algorithms.
method Falsification framework based on statistical tests comparing prediction losses across outcomes.
result Establishes discriminant validity for some outcomes but not others.
Framework predicts and prepares for rain-induced microwave link attenuation.
problem Severe signal attenuation due to weather conditions degrades network performance.
method Predictive Network Reconfiguration (PNR) framework using LSTM for attenuation prediction and MSNR for dynamic routing.
result Framework improves network utilization by more than 200% compared to reactive algorithms.
The paper proposes a framework for information-theoretic predictive uncertainty measures.
problem The need for reliable estimation of predictive uncertainty in machine learning.
method Revisiting core concepts, categorizing predictive uncertainty measures based on model and approximation of true distribution.
result Identification of conditions under which certain predictive uncertainty measures excel.
Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see how they are related. A unified view on these methods has been missing. We present the generalized SIPA (sampling, intervention, prediction, aggregation) …
Conformal Prediction in Hierarchical Classification with Constrained Representation Complexitystat.ML
New algorithms for hierarchical classification using conformal prediction.
problem Valid prediction sets in hierarchical classification tasks.
method Extended split conformal prediction framework with two inference algorithms.
result Empirical evaluations show effectiveness in achieving nominal coverage.
A framework uses a mixture of predictors for semi-supervised inference.
problem Limited labeled data, abundant unlabeled data.
method Mixture of Experts (MOE) for semi-supervised inference.
result MOE-powered inference framework achieves smallest possible variance.
A new method for predicting insurance claims with statistical guarantees.
problem Creating accurate prediction intervals for insurance claims.
method Model-agnostic framework using split conformal prediction for frequency-severity modeling.
result Shows effectiveness on simulated and real datasets using various models.
Kandinsky conformal prediction expands conditional coverage guarantees.
problem Disparities in coverage guarantees across different subpopulations.
method Flexible handling of overlapping and fractional group memberships.
result Minimax-optimal high-probability conditional coverage bound.
New framework improves generative models with prediction and consistency constraints.
problem Improving generative models with sparse labeled data.
method Optimizes variational autoencoders with prediction and consistency constraints.
result Promising image classification performance, especially in semi-supervised scenarios.
This research improves deep neural networks for parameter identification and prediction in stochastic Volterra integral equations.
problem Parameter identification and prediction in Volterra integral equations driven by Gaussian noise.
method Improved deep neural networks framework that incorporates inter-output relationships into the loss function.
result The framework enhances parameter estimation accuracy and provides accurate solutions for modeling stochastic systems.
A new conformal prediction framework for graph-valued outputs using Z-Gromov-Wasserstein distances.
problem Lack of principled uncertainty quantification for graph-valued supervised prediction.
method Proposes a conformal prediction framework using Z-Gromov-Wasserstein distances for graph-valued outputs.
result Provides distribution-free coverage guarantees for graph-valued outputs.
A new framework predicts hidden Markov model regimes online.
problem Efficiently identify hidden Markov model regimes in streaming data.
method Develops a predictive-first optimisation framework for streaming HMMs, approximating the full posterior predictive distribution.
result The method provides competitive prequential performance compared to Online EM and Sequential Monte Carlo.
Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industrystat.ML
Paper predicts bearing degradation stages for pharmaceutical industry maintenance.
problem Predicting when to maintain specific parts of production machines.
method AutoEncoder-based k-means segmentation of high-frequency vibration data.
result Framework generates reliable predictions for bearing degradation stages.
We consider the use of decision trees for decision-making problems under the predict-then-optimize framework. That is, we would like to first use a decision tree to predict unknown input parameters of an optimization problem, and then make decisions by solving the optimization problem using the predicted parameters. A …
Unified framework for set-valued classification tackles ambiguous multi-class datasets.
problem Ambiguous multi-class datasets in modern statistics.
method Unified statistical framework encompassing various set-valued classification formulations.
result Infinite sample optimal strategies and plug-in principle for data-driven algorithms.
With the growth of using cell phones and the increase in diversity of smart mobile devices, a massive volume of data is generated continuously in the process of using these devices. Among these data, Call Detail Records, CDR, is highly remarkable. Since CDR contains both temporal and spatial labels, mobility analysis o…
A model predicts visual motion by learning from natural videos.
problem Temporal prediction accuracy in visual perception.
method Self-supervised representation learning using Fourier shift theorem.
result Achieves better prediction performance than traditional methods.