New refit strategy improves probability estimation for multicategory angle-based classifiers.
problem Improving probability estimation for multicategory angle-based classifiers in high dimensional applications.
method Proposes a new refit strategy for multicategory angle-based classifiers, adding small computation cost.
result Significant improvement in probability estimation with minimal additional computation.
EVARS-GPR refines Gaussian Process Regression for seasonal data with sudden scale changes.
problem Challenges in forecasting with changing system behavior over time.
method Combines online change point detection with data augmentation for refitting.
result 20.8% lower RMSE on real-world datasets compared to similar methods.
Efficiently refits black box predictions with wild refitting method.
problem Computing high-probability upper bounds on prediction errors.
method Three-step procedure: residuals, symmetrization, and solving a modified prediction problem.
result Wild refitting provides an upper bound on prediction error with high probability.
The paper tackles model failure detection and refitting in real-world systems.
problem Real-world data often fails statistical models due to heterogeneity.
method Develops tools for detecting and identifying model failures and refitting to improve accuracy.
result Empirical and theoretical results show the effectiveness of the proposed methodology.
New method refines model-free evaluation of complex machine learning models.
problem Evaluating the excess risk of opaque machine learning predictors.
method Perturbing derivatives to create pseudo-outcomes and refitting the model twice.
result Upper bound on excess risk derived efficiently without prior function class knowledge.
New method estimates model risk without knowing function class.
problem Evaluating model risk for complex, opaque models.
method Wild refitting with Bregman losses and randomized symmetrization.
result Valid upper bound on excess risk for opaque models.
New layer sparsity concept improves neural networks.
problem Improving neural network efficiency and interpretability.
method Formulated layer sparsity, introduced regularization and refitting schemes.
result Generated more compact and accurate neural networks.
Ribbon: Scalable Approximation and Robust Uncertainty Quantification
problem Reliably quantifying predictive uncertainty for complex models
method Ribbon, a scalable approximation to Dirichlet-reweighted bootstrap uncertainty
result Asymptotically equivalent to a flat-prior Laplace approximation under correct likelihood specification, recovers robust sandwich covariance under misspecification
The paper proposes a method to improve fairness in machine learning models without refitting.
problem Mitigating biases in machine learning models that disadvantage certain groups.
method Infinitesimal jackknife-based approach to drop selected training data points.
result The intervention improves fairness without significantly reducing predictive performance.
Framework detects and mitigates data-poisoning attacks in causal effect estimation.
problem Vulnerability to append-only attacks in observational causal analyses.
method Develops a data-poisoning audit for augmented inverse-probability-weighted estimation.
result Proposes a greedy scan to compute exact worst-case movement at every append budget.
Proposes joint LCA for multiview data to identify shared and view-specific components.
problem Extracting shared components sequentially from multiview data.
method Formulates a matrix decomposition model with joint and individual structures, proposes a penalty term objective function, and employs a refitting procedure.
result Achieves simultaneous estimation and rank selection for cross covariance.
Root-finding methods improve efficiency of conformal prediction sets.
problem Efficiently computing conformal prediction sets is difficult.
method Exploiting root-finding algorithms to approximate boundaries of conformal prediction intervals.
result The approach can overcome limitations of previous strategies.
New method approximates CV for model assessment and selection.
problem Efficient model assessment and selection with large number of folds.
method Approximates expensive refitting with a single Newton step warm-started from full training set optimizer.
result Uniform non-asymptotic, deterministic model assessment guarantees for approximate CV.
A new gradient boosting method improves interpretability of probabilistic models.
problem Learning interpretable yet accurate probabilistic models with limited rule complexity.
method A new objective function that measures the angle between risk gradient and condition output vector projection.
result Significantly improves comprehensibility/accuracy trade-off of fitted ensemble.
ICP improves prediction intervals for continuous outcomes at lower computational cost.
problem Systematic bias in point predictions that undermines their use in decision-making.
method Develops Isotonic Conformal Prediction (ICP) framework to decouple calibration from prediction-set construction.
result SICP and TICP procedures match SC-CP coverage at lower computational cost.
Proposes a new method to approximate Bayesian predictive uncertainty.
problem Bayesian uncertainty quantification in model predictions.
method Self-supervised learning approach to approximate posterior predictive distribution.
result SSLA and ASSLA outperform classical Laplace approximations in predictive calibration.
Neural networks combining multiple data sources can reverse preferences, affecting decision reliability.
problem Preference reversals in neural networks under pooled data.
method Formalized through Case-Based Decision Theory, analyzed Gram geometry, introduced regularization, and developed auditing methods.
result Pooled refitting can reverse shared preferences, and conditions for preserving preferences are derived.
Develops a fast test to identify significant features in machine learning models.
problem Identifying significant features and interactions in machine learning models efficiently.
method Forward-selection approach for any model, learning task, and variable type, non-asymptotic, straightforward implementation.
result Identifies statistically significant features and feature interactions of any order.
This paper selects features in deep neural networks with theoretical guarantees.
problem Feature selection in deep neural networks with unknown nonlinear functions.
method Reformulate neural networks as index models, estimate feature sets using Stein's formula, and apply screening-and-selection mechanism.
result Consistent feature selection with theoretical guarantees, even in high-dimensional settings.
Proposes EASE estimators to improve linear regression efficiency using semi-supervised data.
problem Improving linear regression efficiency with semi-supervised data.
method Two-step EASE estimators, including semi-non-parametric imputation and cross-validation.
result Improved efficiency under model mis-specification and equal efficiency under linear model.
Renet improves Elastic Net by dynamically selecting between convex blending and refitting, enhancing prediction accuracy.
problem Elastic Net's shrinkage bias limits its prediction accuracy in high-dimensional settings.
method Adaptive relaxation procedure that dynamically dispatches between convex blending and efficient sub-path refitting.
result Renet consistently outperforms standard Elastic Net and Adaptive Elastic Net in high-dimensional, low signal-to-noise ratio, and high-multicollinearity scenarios.
Paper uses bootstrapping to estimate ensemble methods' performance.
problem Estimating operating characteristics of ensemble methods.
method Bootstrap resampling for infinite resampling without refitting.
result Alternative methods improve predictive accuracy in meta-parameter selection.
Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using ℓ1-penalization methods. We propose and study the following method. We combine a multiple regression approach with ideas of thresholding and refitting: first we infer a sparse u…
Cross validation residuals extended to GLS models.
problem Validating models with correlated data.
method Leave-M-out cross validation for GLS models, demonstrating relationship with Cook's distance.
result No need to refit model for reduced datasets.
New approach to meaningful and robust algorithmic recourse.
problem Ineffective and unmeaningful algorithmic recourse explanations.
method Meaningful Algorithmic Recourse (MAR) and Effective Algorithmic Recourse (EAR).
result Proposes new constraints for algorithmic recourse that improve both prediction and target.
Efficient algorithms compute conformal prediction sets for regression problems.
problem Providing strong coverage guarantees for predictions without distributional assumptions.
method Approximate homotopy continuation for convex regularized empirical risk minimization.
result Efficient algorithms to compute conformal prediction sets for regression problems.
Framework calibrates ML models for risk control in various tasks.
problem Achieving statistical guarantees for model predictions.
method Reframing risk control as multiple hypothesis testing, applying statistical techniques.
result New calibration methods for multi-label classification, instance segmentation, outlier detection, and confidence set coverage.
Framework monitors insurance pricing models for drift and recalibration.
problem Maintaining predictive performance of pricing models in evolving insurance portfolios.
method Formalizes deviance loss and Murphy's score, studies Gini score, develops monitoring framework.
result Framework guides decisions on refitting or recalibrating pricing models.
FMP sampling improves model calibration without sharing data.
problem Difficulties in specifying priors for modern neural networks.
method FMP sampling replaces prior and likelihood with a predictive distribution, running the sampler centrally.
result FMP sampling significantly improves calibration over baseline methods.
Enhances GBDT robustness with one-hot encoding and regularization.
problem Low robustness of GBDT models against covariate perturbation.
method One-hot encoding to linear framework, risk decomposition, L1 or L2 regularization. result Regularization enhances GBDT robustness.
Study on estimating causal effects with limited data and multiple environments.
problem Estimating causal effects under hidden confounding with unpaired data and sparse effects.
method Instrumental variable (IV) regression with cross-fold sample splitting and ℓ1-regularized estimation. result Proposed GMM-type estimator is consistent as the number of environments grows.
Paper accelerates conformal prediction by using approximate leave-one-out estimators.
problem Limited computational cost for conformal prediction.
method Incorporates approximate leave-one-out estimators to accelerate conformal prediction.
result ALO-based methods achieve comparable coverage and efficiency to exact methods but with significantly reduced runtime.
Recourse explanations can become invalid if collective actions change statistical data.
problem Recourse explanations may become invalid due to collective behavior changing data statistics.
method Formal characterization of conditions under which recourse explanations remain valid under performativity.
result Recourse actions may become invalid if they are influenced by or intervene on non-causal variables.
TSL learns separable models to avoid signal cancellation and off-support extrapolation.
problem Signal cancellation and off-support extrapolation in additive models.
method Tensor Separation Learning (TSL) via stagewise greedy procedure with orthogonal refitting.
result TSL avoids information loss caused by marginalizing higher-order interactions.
Researchers formalize PD and PFI to relate them to data generating process.
problem Lack of theory linking PD and PFI to data generating process.
method Formalize PD and PFI as estimators of ground truth estimands, account for model variance with learner-PD and learner-PFI.
result PD and PFI estimates deviate from ground truth due to statistical biases, model variance, and Monte Carlo approximation errors.
Study improves predictive models for ICU data across hospitals.
problem Degradation of predictive model performance in new hospitals.
method Anchor regression and anchor boosting for domain generalization.
result Anchor regularization enhances out-of-distribution performance.
Alpha-trimming prunes trees in random forests to improve predictive performance.
problem Improving predictive performance of random forests by locally adaptive tree pruning.
method Alpha-trimming is a fast pruning algorithm that prunes trees in a random forest based on signal-to-noise ratio, controlled by a tuning parameter.
result Alpha-trimming often lowers mean squared prediction error compared to fully grown random forests.
New methods for assessing and visualizing feature groups in machine learning models.
problem Lack of methods for interpreting feature groups in machine learning models.
method Permutation-based, refitting, and Shapley-based techniques for grouped feature importance. Introduced a sequential procedure for identifying stable feature combinations. Developed a combined features effect plot.
result Effective methods for assessing and visualizing the importance and effect of feature groups in machine learning models.
Two approaches extend knowledge distillation to Gaussian Processes, showing relationships to existing methods.
problem Applying knowledge distillation to Gaussian Processes for regression and classification.
method Data-centric and distribution-centric approaches to extend distillation to GPR and GPC.
result Distribution-centric approach for GPC approximately corresponds to data duplication and scaling.
A central goal of neuroscience is to understand how activity in the nervous system is related to features of the external world, or to features of the nervous system itself. A common approach is to model neural responses as a weighted combination of external features, or vice versa. The structure of the model weights c…
Proposes efficient sensitivity analysis for complex Bayesian models.
problem Inefficiency of sensitivity analyses in complex Bayesian models.
method SA-ABI: weight sharing and neural network rapid inference.
result Efficiently integrates sensitivity analyses into Bayesian inference.
The paper develops methods to accurately locate change points in high-dimensional mean shift models.
problem Locating change points in high-dimensional mean shift models.
method Locally refitted least squares estimator, component-wise and simultaneous rates of estimation.
result Asymptotic validity of component-wise and simultaneous confidence intervals for change point parameters.
New framework learns complex AI attitudes from heterogeneous data.
problem Heterogeneous ordinal structure in AI attitudes, poorly captured by existing methods.
method Monotone Gaussian score embedding, BNP complexity discovery, confirmatory fixed-K estimation.
result Reduced holdout MSE by 25.8% over single-graph baseline.
New algorithm speeds up greedy feature selection for large datasets.
problem High running time for greedy feature selection in large datasets.
method Developed a new algorithm that achieves better performance by exploiting distributed computation and stochastic evaluation.
result Submodularity is not required for multiplicative approximation guarantees of these algorithms.
Optimal self-distillation improves generative models' velocity risk and mode recovery.
problem Improving generative models' velocity risk and mode recovery.
method Proved optimal self-distillation for rectified flow via linear probing, derived mixing coefficient, and provided validation tuning.
result Optimal self-distillation improves velocity risk and mode recovery.
A new method for predicting with confidence for complex models.
problem Lack of reliable confidence in high-stake decision-making models.
method Developed a full-CP for sparse high-order interaction model using homotopy mining.
result SHIM achieves comparable accuracy to complex models and superior statistical power.
SBS policy improves crypto market forecasts by 0.15% with minimal model changes.
problem Improving crypto market forecasting models while minimizing model state transitions.
method Shadow Before Swap (SBS) policy that warm-refits and evaluates challenger models.
result Reduces NLL by 0.1472% relative to continuous maintenance in historical data.
Paper develops an efficient method for conformal prediction in sparse linear models.
problem Computing conformal prediction sets for sparse linear models is computationally infeasible.
method Numerical continuation techniques to approximate the solution path efficiently.
result The method accurately approximates conformal prediction sets for sparse linear models.