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Found 14 papers, 2 papers with code
One-Shot Federated Conformal Prediction
In this paper, we introduce a conformal prediction method to construct prediction sets in a oneshot federated learning setting.
A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension
Identifying the relevant variables for a classification model with correct confidence levels is a central but difficult task in high-dimension.
Online Orthogonal Matching Pursuit
Greedy algorithms for feature selection are widely used for recovering sparse high-dimensional vectors in linear models.
Aggregation of Multiple Knockoffs
We develop an extension of the Knockoff Inference procedure, introduced by Barber and Candes (2015).
Rejoinder on: Minimal penalties and the slope heuristics: a survey
This text is the rejoinder following the discussion of a survey paper about minimal penalties and the slope heuristics (Arlot, 2019.
Aggregated Hold-Out
Aggregated hold-out (Agghoo) is a method which averages learning rules selected by hold-out (that is, cross-validation with a single split).
Minimal penalties and the slope heuristics: a survey
Explicit connections are made with residual-variance estimators-with an original contribution on this topic, showing that for this task the slope heuristics performs almost as well as a residual-based estimator with the best model choice-and some classical algorithms such as L-curve or elbow heuristics, Mallows' C p , and Akaike's FPE.
Comments on: "A Random Forest Guided Tour" by G. Biau and E. Scornet
This paper is a comment on the survey paper by Biau and Scornet (2016) about random forests.
Semidefinite and Spectral Relaxations for Multi-Label Classification
In this paper, we address the problem of multi-label classification.
Metric Learning for Temporal Sequence Alignment
The learning examples for this task are time series for which the true alignment is known.
Analysis of purely random forests bias
Under some regularity assumptions on the regression function, we show that the bias of an infinite forest decreases at a faster rate (with respect to the size of each tree) than a single tree.
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation
Then, we compute the variance of V-fold cross-validation and related criteria, as well as the variance of key quantities for model selection performance.
Data-driven calibration of linear estimators with minimal penalties
This paper tackles the problem of selecting among several linear estimators in non-parametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge regression or spline smoothing, and the choice of a kernel in multiple kernel learning.
