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Found 32 papers, 21 papers with code
Regression Trees for Fast and Adaptive Prediction Intervals
Our approach is based on pursuing the coarsest partition of the feature space that approximates conditional coverage.
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data.
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes
A primary objective is to generate a distribution-free joint prediction region for the arrival time and mark, with a finite-sample marginal coverage guarantee.
Expertise-based Weighting for Regression Models with Noisy Labels
In summary, this method offers a simple, fast, and effective solution for training regression models with noisy labels derived from diverse expert opinions.
Is augmentation effective to improve prediction in imbalanced text datasets?
Imbalanced datasets present a significant challenge for machine learning models, often leading to biased predictions.
Flexible conditional density estimation for time series
This paper introduces FlexCodeTS, a new conditional density estimator for time series.
RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features
Kernel methods provide a flexible and theoretically grounded approach to nonlinear and nonparametric learning.
Model interpretation using improved local regression with variable importance
A fundamental question on the use of ML models concerns the explanation of their predictions for increasing transparency in decision-making.
Simulator-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
We also illustrate how our approach can correct overly confident posterior regions computed with normalizing flows.
Conditionally Calibrated Predictive Distributions by Probability-Probability Map: Application to Galaxy Redshift Estimation and Probabilistic Forecasting
The same regression function morphs the misspecified PD to a re-calibrated PD for all $\mathbf{x}$.
A unified framework for dataset shift diagnostics
It is versatile, suitable for regression and classification tasks, and accommodates diverse data forms - tabular, text, or image.
A new LDA formulation with covariates
This model allows the identification of mixed-membership clusters in discrete data and provides inference on the relationship between covariates and the abundance of these clusters.
Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery
We prove that type I error control is guaranteed as long as the distribution of the label series is well-estimated, which is made easier by the extensive historical data for binary TC event labels.
Hierarchical clustering: visualization, feature importance and model selection
We propose methods for the analysis of hierarchical clustering that fully use the multi-resolution structure provided by a dendrogram.
Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones
Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatio-temporal observations (e. g., satellite imagery) and model output (e. g., numerical weather prediction, statistical models) to produce forecasts every 6 hours.
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
In this work, we propose a unified and modular inference framework that bridges classical statistics and modern machine learning providing (i) a practical approach to the Neyman construction of confidence sets with frequentist finite-sample coverage for any value of the unknown parameters; and (ii) interpretable diagnostics that estimate the empirical coverage across the entire parameter space.
MeLIME: Meaningful Local Explanation for Machine Learning Models
Additionally, we introduce modifications to standard training algorithms of local interpretable models fostering more robust explanations, even allowing the production of counterfactual examples.
CD-split and HPD-split: efficient conformal regions in high dimensions
We also present simulations that show how to tune CD-split.
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
In this paper, we present $\texttt{ACORE}$ (Approximate Computation via Odds Ratio Estimation), a frequentist approach to LFI that first formulates the classical likelihood ratio test (LRT) as a parametrized classification problem, and then uses the equivalence of tests and confidence sets to build confidence regions for parameters of interest.
Flexible distribution-free conditional predictive bands using density estimators
In order to obtain this property, these methods require strong conditions on the dependence between the target variable and the features.
NLS: an accurate and yet easy-to-interpret regression method
We develop NLS (neural local smoother), a method that is complex enough to give good predictions, and yet gives solutions that are easy to be interpreted without the need of using a separate interpreter.
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders
Given two distinct datasets, an important question is if they have arisen from the the same data generating function or alternatively how their data generating functions diverge from one another.
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
We provide sample code in $\texttt{Python}$ and $\texttt{R}$ as well as examples of applications to photometric redshift estimation and likelihood-free cosmological inference via CDE.
Conditional independence testing: a predictive perspective
Conditional independence testing is a key problem required by many machine learning and statistics tools.
Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space
For such, they induce interpretable models on the neighborhood of the instance to be explained.
The NN-Stacking: Feature weighted linear stacking through neural networks
We show that while our approach keeps the interpretative features of Breiman's method at a local level, it leads to better predictive power, especially in datasets with large sample sizes.
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
At the heart of our approach is a two-sample test that quantifies the quality of the fit at fixed parameter values, and a global test that assesses goodness-of-fit across simulation parameters.
Quantification under prior probability shift: the ratio estimator and its extensions
The quantification problem consists of determining the prevalence of a given label in a target population.
ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
Approximate Bayesian Computation (ABC) is typically used when the likelihood is either unavailable or intractable but where data can be simulated under different parameter settings using a forward model.
Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation
There is a growing demand for nonparametric conditional density estimators (CDEs) in fields such as astronomy and economics.
A Spectral Series Approach to High-Dimensional Nonparametric Regression
We expand the unknown regression on the data in terms of the eigenfunctions of a kernel-based operator, and we take advantage of orthogonality of the basis with respect to the underlying data distribution, P, to speed up computations and tuning of parameters.
Learning with many experts: model selection and sparsity
Next, we discuss how this loss can be used to tune a penalization which introduces sparsity in the parameters of a traditional class of models.
