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Found 26 papers, 12 papers with code
Distributional Principal Autoencoders
Dimension reduction techniques usually lose information in the sense that reconstructed data are not identical to the original data.
Engression: Extrapolation for Nonlinear Regression?
Our experimental results indicate that this model is typically suitable for many real data sets.
Confidence and Uncertainty Assessment for Distributional Random Forests
The Distributional Random Forest (DRF) is a recently introduced Random Forest algorithm to estimate multivariate conditional distributions.
Robust detection and attribution of climate change under interventions
Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variability (detection), and whether observed changes can be assigned to specific external drivers (attribution).
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics.
fairadapt: Causal Reasoning for Fair Data Pre-processing
Machine learning algorithms are useful for various predictions tasks, but they can also learn how to discriminate, based on gender, race or other sensitive attributes.
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU.
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
Random Forest (Breiman, 2001) is a successful and widely used regression and classification algorithm.
Fair Data Adaptation with Quantile Preservation
The data adaptation is based on a presumed counterfactual model for the data.
Causal discovery in heavy-tailed models
Causal questions are omnipresent in many scientific problems.
Methodology
Anchor regression: heterogeneous data meets causality
If anchor regression and least squares provide the same answer (anchor stability), we establish that OLS parameters are invariant under certain distributional changes.
Methodology
Grouping-By-ID: Guarding Against Adversarial Domain Shifts
If two or more samples share the same class and identifier, (Y, ID)=(y, i), then we treat those samples as counterfactuals under different style interventions on the orthogonal or style features.
Conditional Variance Penalties and Domain Shift Robustness
Our goal is to minimize a loss that is robust under changes in the distribution of these style features.
Causal Structure Learning
Causal models can be viewed as a special class of graphical models that not only represent the distribution of the observed system but also the distributions under external interventions.
Methodology
Invariant Causal Prediction for Nonlinear Models
In this work, we present and evaluate an array of methods for nonlinear and nonparametric versions of ICP for learning the causal parents of given target variables.
Methodology
Preserving Differential Privacy Between Features in Distributed Estimation
Privacy is crucial in many applications of machine learning.
Scalable Adaptive Stochastic Optimization Using Random Projections
We show that the regret of Ada-LR is close to the regret of full-matrix AdaGrad which can have an up-to exponentially smaller dependence on the dimension than the diagonal variant.
The xyz algorithm for fast interaction search in high-dimensional data
When performing regression on a dataset with $p$ variables, it is often of interest to go beyond using main linear effects and include interactions as products between individual variables.
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
We present DUAL-LOCO, a communication-efficient algorithm for distributed statistical estimation.
backShift: Learning causal cyclic graphs from unknown shift interventions
We propose a simple method to learn linear causal cyclic models in the presence of latent variables.
Causal inference using invariant prediction: identification and confidence intervals
In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables.
Methodology
LOCO: Distributing Ridge Regression with Random Projections
We propose LOCO, an algorithm for large-scale ridge regression which distributes the features across workers on a cluster.
On b-bit min-wise hashing for large-scale regression and classification with sparse data
Large-scale regression problems where both the number of variables, $p$, and the number of observations, $n$, may be large and in the order of millions or more, are becoming increasingly more common.
Minimum Distance Estimation for Robust High-Dimensional Regression
We propose a minimum distance estimation method for robust regression in sparse high-dimensional settings.
Random Intersection Trees
We show that informative interactions are retained with high probability, and the computational complexity of our procedure is of order $p^\kappa$ for a value of $\kappa$ that can reach values as low as 1 for very sparse data; in many more general settings, it will still beat the exponent $s$ obtained when using a brute force search constrained to order $s$ interactions.
Stability Selection
Estimation of structure, such as in variable selection, graphical modelling or cluster analysis is notoriously difficult, especially for high-dimensional data.
Methodology
