PIVEN predicts both specific values and prediction intervals.
problem Improving robustness of neural nets in regression tasks.
method PIVEN is a deep neural network that produces both a prediction interval and a specific value prediction.
result PIVEN produces tighter uncertainty bounds than state-of-the-art approaches for prediction intervals.
Identifies patient-specific root causes of disease using structural equation models.
problem Detecting significant variables in complex diseases that differ between patients.
method Defining patient-specific root causes as exogenous errors in a structural equation model, quantifying predictivity using Shapley values, and developing a fast algorithm called Root Causal Inference.
result Significant improvements in accuracy by uncovering root causes with large effect sizes at the individual level but clinically insignificant effect sizes at the group level.
FavMac maximizes value while controlling cost in multi-label prediction.
problem Value-maximizing predictions with strict cost control in multi-label scenarios.
method FavMac pipeline combining any multi-label classifier with online update mechanism.
result FavMac achieves higher value with strict cost control compared to baselines.
New method identifies root causes in presence of latent confounding.
problem Identifying root causes in the presence of latent variables.
method Extract Errors with Latents (EEL) procedure for inferring root causes.
result Superior accuracy and robustness compared to previous methods.
Machine learning models predict brain age with systematic bias, corrected in this study.
problem Systematic bias in machine learning regression models for brain age prediction.
method General constrained optimization approach to correct bias.
result Our method effectively eliminates the bias from brain age predictions.
Simplified calculus for semimartingales makes complex transformations easier.
problem Complex transformations of semimartingales.
method Unified treatment of transformations for real and complex semimartingales.
result Unified calculus for semimartingales simplifies various transformations.
Audit financial machine learning workflows to detect spurious predictability.
problem Spurious predictability in financial machine learning models.
method Falsification audit testing predictive workflows against synthetic environments.
result Many apparent financial predictions are artifacts, not genuine.
Improved forecast accuracy for energy systems through decision-focused fine-tuning.
problem Challenges in integrating forecast values into time series models for diverse and specific instances.
method Decision-focused fine-tuning within time series foundation models for dispatchable feeder optimization.
result Improvement of 9.45% in average total daily costs.
Study evaluates how much knowledge LLMs have by comparing their prediction accuracy to flexible models.
problem Evaluating the predictive power of LLMs without access to their training data.
method Equivalent sample size measure, comparing LLM's prediction error to flexible models trained on varying amounts of domain-specific data.
result LLMs encode varying amounts of predictive information across different economic variables.
This paper improves prediction accuracy for multi-input classification tasks using p-value aggregation.
problem Generating accurate predictive sets with guaranteed coverage for multi-input classification tasks.
method Integrates p-values from each observation to reduce the size of the predicted label set while maintaining class-conditional coverage.
result The method reduces the size of the predicted label set while preserving the required coverage guarantee.
Paper relaxes set-valued prediction in hierarchical classification by considering representation complexity.
problem Uncertainty in class labels in hierarchical multi-class classification problems.
method Introduces representation complexity for predicted sets, proposes three methods for inference.
result Recursive tree search method is computationally more efficient.
Gaussian Processes improve missing value imputation in datasets.
problem Handling missing values in large datasets.
method Sparse Gaussian Processes combined with stochastic variational inference.
result MGP significantly outperforms other imputation methods.
Method predicts spatial values with few data using GP framework.
problem Few data limit predictive performance in spatial regression.
method Trains neural network to infer task representation from small data, uses GP framework to predict spatial values.
result Proposed method achieves better predictive performance than meta-learning methods.
This paper proposes a new approach to RL by focusing on the value-improvement path.
problem Value prediction problems in RL are sequence-dependent and require holistic approach.
method Characterize and approximate the value-improvement path holistically.
result A representation that spans the value-improvement path provides accurate value approximations for future policy improvements.
The paper develops distribution-free methods for ordinal classification.
problem Constructing valid prediction sets for ordinal classification problems.
method Leveraging conformal prediction and multiple testing with FWER control.
result The proposed methods achieve satisfactory levels of marginal and class-specific conditional coverages.
We propose to explain the predictions of a deep neural network, by pointing to the set of what we call representer points in the training set, for a given test point prediction. Specifically, we show that we can decompose the pre-activation prediction of a neural network into a linear combination of activations of trai…
Imitation by observation is an approach for learning from expert demonstrations that lack action information, such as videos. Recent approaches to this problem can be placed into two broad categories: training dynamics models that aim to predict the actions taken between states, and learning rewards or features for com…
Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite, albeit potentially huge, output spaces, in this paper we discuss how structured prediction can be extended to a continuous scenario. Specifi…
BCCP uses bandit feedback to provide reliable predictions with limited labeled data.
problem Limited labeled data and bandit feedback challenge online set-valued classification.
method BCCP uses stochastic gradient descent to train model and make set-valued inferences with unbiased estimation of true label.
result BCCP offers coverage guarantees on a class-specific granularity.
MSB framework improves survival prediction in immunotherapy patients with missing data.
problem High dimensionality and blockwise missingness in multimodal clinical data.
method MSB is a late-fusion framework that independently models modality-specific features before aggregating predictions via cross-validated stacking.
result MSB outperformed baseline algorithms in predicting progression-free survival in lung cancer patients.
DistShap parallelizes GNN explanation for large graphs.
problem Computational expense in attributing GNN predictions to specific edges or features.
method Distributed Shapley values across multiple GPUs for scalable GNN explanations.
result DistShap outperforms existing methods and scales to models with millions of features.
Blood lactate concentration is a strong indicator of mortality risk in critically ill patients. While frequent lactate measurements are necessary to assess patient's health state, the measurement is an invasive procedure that can increase risk of hospital-acquired infections. For this reason we formally define the prob…
Gradient boosting of regression trees is a competitive procedure for learning predictive models of continuous data that fits the data with an additive non-parametric model. The classic version of gradient boosting assumes that the data is independent and identically distributed. However, relational data with interdepen…
Objective: Predict patient-specific vitals deemed medically acceptable for discharge from a pediatric intensive care unit (ICU). Design: The means of each patient's hr, sbp and dbp measurements between their medical and physical discharge from the ICU were computed as a proxy for their physiologically acceptable state …
Framework for imputing time series data with uncertainty measures.
problem Handling missing values in time series data, especially in healthcare.
method Uncertainty-aware multivariate time series imputation framework.
result Selective imputation of less uncertain values improves downstream tasks.
Blood glucose value prediction is an important task in diabetes management. While it is reported that glucose concentration is sensitive to social context such as mood, physical activity, stress, diet, alongside the influence of diabetes pathologies, we need more research on data and methodologies to incorporate and ev…
Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty. However, tracking uncertainty for iterative, multi-step predictions in general leads to an analytically intractable problem. While approximation methods exist, they do not come with guarantees, mak…
In the regression problem, L1 and L2 are the most commonly used loss functions, which produce mean predictions with different biases. However, the predictions are neither robust nor adequate enough since they only capture a few conditional distributions instead of the whole distribution, especially for small datasets. …
Study improves dynamic PT fleet optimization under noisy demand predictions.
problem Accurately predicting dynamic public transport demand for effective fleet management.
method Experimental case study in Copenhagen, using linear programming to optimize fleets.
result Optimized fleet performance is mainly affected by noise distribution skew and large errors.
Adaptive coverage policies improve conformal prediction accuracy.
problem Fixed coverage levels in traditional conformal prediction lead to uninformative predictions.
method Optimizes adaptive coverage policy using a neural network trained on leave-one-out calibration.
result Adaptive coverage policies produce more informative and flexible prediction sets.
This paper investigates methods for quantifying similarity between audio signals, specifically for the task of of cover song detection. We consider an information-theoretic approach, where we compute pairwise measures of predictability between time series. We compare discrete-valued approaches operating on quantised au…
New methods predict language model out-of-distribution behaviors using causal mechanisms.
problem Predicting how language models behave on unseen data.
method Two methods: counterfactual simulation and value probing.
result Both methods achieve high AUC-ROC and outperform causal-agnostic approaches in out-of-distribution settings.
Real GDP growth rate in developed countries is found to be a sum of two terms. The first term is the reciprocal value of the duration of the period of mean income growth with work experience, Tcr. The current value of Tcr in the USA is 40 years. The second term is inherently related to population and defined by the rel…
Modelling financial time series as a time change of a simpler process has been proposed in various forms over the years. One of such recent approaches is called volatility homogenisation decomposition, and has been designed specifically to aid the forecasting of price changes on financial markets. The authors of this m…
SCORE improves tree-based predictions with boosted residual extraTrees.
problem Improving tree-based prediction models with reduced errors.
method Inspired by representation learning, SCORE uses boosting, regularized regression, and variable selection.
result SCORE provides comparable or superior performance compared to other models.
We introduce a new tool for predicting the evolution of an option for the cases where at some specific time, there is a high-degree of uncertainty for identifying its price. We work over the special case where we can predict the evolution of the system by joining a single price for the Option, defined at some specific …
The paper tackles fVaR prediction methods in finance.
problem Predicting future values at risk (fVaR) in finance.
method Various methods including Nested MC-empirical quantile, percentiles from distributions, quantile regressions, and limited inner simulations.
result Improved methods for predicting fVaRs, including those that are computationally efficient.
Improved local multivariable regression for better inference with limited data.
problem Limited sample size hampers local polynomial/multivariable regression.
method Prediction-Powered Inference (PPI) algorithm for local multivariable regression.
result Significantly reduces estimation variance without increasing error.
Develops optimal uncertainty quantification for risk-averse decision makers.
problem Quantifying prediction uncertainty for risk-sensitive domains.
method Decision-theoretic foundations connecting uncertainty quantification with risk-averse decision-making.
result Risk-Averse Calibration (RAC) algorithm provides optimal prediction sets for risk-averse decision makers.
Model predicts depegging dynamics of stablecoins like Tether and Bitcoin.
problem Understanding depegging effects of stablecoins on cryptocurrencies.
method Multivariate Hawkes process model.
result Numerical example shows model's effectiveness.
AUC is unreliable in rare event settings but stable with moderate numbers of events.
problem Misleading performance metrics in rare event settings.
method Simulation study varying dataset sizes and event rates.
result AUC is unreliable in rare event settings but stable with moderate numbers of events.
New method identifies important features and interactions in RF models.
problem Limited theoretical understanding of local feature and interaction importance in RF models.
method Combines global and local analysis to identify frequent feature co-occurrences.
result Proves consistent recovery of true local signal features and interactions.
A new method for batch prediction sets in classification problems.
problem Constructing reliable prediction sets for multiple unlabeled examples.
method Proposes a uniformly more powerful approach to batch prediction sets using specific combinations of conformal p-values.
result The proposed method provides narrower prediction sets compared to the Bonferroni correction.
In safety-critical applications a probabilistic model is usually required to be calibrated, i.e., to capture the uncertainty of its predictions accurately. In multi-class classification, calibration of the most confident predictions only is often not sufficient. We propose and study calibration measures for multi-class…
Q-SHAP efficiently calculates feature contributions in boosting trees.
problem Global evaluation of feature contributions in tree models.
method Q-SHAP, an efficient algorithm that reduces Shapley values calculation to polynomial time.
result Q-SHAP improves computational efficiency and enhances accuracy of feature-specific R2 estimates. Within Reinforcement Learning, there is a growing collection of research which aims to express all of an agent's knowledge of the world through predictions about sensation, behaviour, and time. This work can be seen not only as a collection of architectural proposals, but also as the beginnings of a theory of machine k…
IFGAN uses feature-specific GANs for missing value imputation.
problem Missing value imputation in data mining.
method Feature-specific Generative Adversarial Networks (GAN).
result IFGAN outperforms state-of-the-art algorithms in various missing conditions.
SConU improves uncertainty prediction for large models by testing for outliers and reducing miscoverage.
problem Real-world deployment of large language models requires reliable guarantees of task-specific metrics.
method SConU implements significance tests to identify and exclude outliers that violate exchangeability assumptions.
result SConU reduces miscoverage rates and enhances prediction efficiency in high-stakes tasks.