arXiv research

In this paper we consider the problem of Gaussian process classifier (GPC) model selection with different Leave-One-Out (LOO) Cross Validation (CV) based optimization criteria and provide a practical algorithm using LOO predictive distributions with such criteria to select hyperparameters. Apart from the standard avera…

New method improves transductive learning predictions with multiplicative oracle inequalities.

Improved LOO cross-validation for function approximation.

A new method improves robustness and efficiency of Bayesian LOO-CV.

LOO prediction method improves generalization guarantees for arbitrary datasets.

The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integration over the latent values is approximated using the Laplace method or expectation propagation (EP). We study the properties of several B…

LOO-StabCP speeds up CP for multiple predictions.

Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO) is a general approach for assessing the generalizability of a model, but unfortunately, LOO does not scale well to large datasets. We propose a combination of u…

Captures data influence changes during training.

A method for non-parametric conditional distribution estimation using CRPS-optimal binning.

A method to improve surrogate model accuracy using multiple fidelity models.

Recursive partitioning approaches producing tree-like models are a long standing staple of predictive modeling, in the last decade mostly as ``sub-learners'' within state of the art ensemble methods like Boosting and Random Forest. However, a fundamental flaw in the partitioning (or splitting) rule of commonly used tre…

Study shows minimizing the norm of the ERM solution stabilizes kernel ridge-less regression.

A reflexion space is generalization of a symmetric space introduced by O. Loos. We generalize locally symmetric spaces to local reflexion spaces in the similar way. We investigate, when local reflexion spaces are equivalently given by a locally flat Cartan connection of certain type.

New method optimizes hyperparameters for non-smooth problems efficiently.

Statistical machine learning models should be evaluated and validated before putting to work. Conventional k-fold Monte Carlo Cross-Validation (MCCV) procedure uses a pseudo-random sequence to partition instances into k subsets, which usually causes subsampling bias, inflates generalization errors and jeopardizes the r…

Backward Conformal Prediction offers flexible control over prediction set sizes while ensuring coverage guarantees.

New algorithms optimize actions under time-varying constraints without projecting.

A new KDE model prevents singular solutions and accelerates optimization for probabilistic modeling.

We classify homotopes of classical symmetric spaces (studied in Part I of this work). Our classification uses the fibered structure of homotopes: they are fibered as symmetric spaces, with flat fibers, over a non-degenerate base; the base spaces correspond to inner ideals in Jordan pairs. Using that inner ideals in cla…

New proof for symmetric spaces with rectangular lattices.

The paper proves LOO CV is reliable under estimator stability.

Hua domain, named after Chinese mathematician Loo-Keng Hua, is defined as a domain in $\mathbb{C}^{n}$ fibered over an irreducible bounded symmetric domain $Ω\subset \mathbb{C}^{d}\;(d<n)$ with the fiber over $z\in Ω$ being a $(n-d)$-dimensional generalized complex ellipsoid $Σ(z)$. In general, a Hua domain is a nonhom…

This is the pdf -version of the author's Ph.D. thesis (1995, ULB, Belgium). The notion of symeplectic symmertic space is introduced and studied via Lie theoretical and symplectic geoemetrical methods. The first chapter concerns basic poperties, however, an explicit formula for the Loos connection in the symplectic fram…

Deep neural networks obtain state-of-the-art performance on a series of tasks. However, they are easily fooled by adding a small adversarial perturbation to input. The perturbation is often human imperceptible on image data. We observe a significant difference in feature attributions of adversarially crafted examples f…

A Banach symmetric space in the sense of O. Loos is a smooth Banach manifold $M$ endowed with a multiplication map $μ\colon M \times M \to M$ such that each left multiplication map $μ_x := μ(x,\cdot)$ (with $x \in M$) is an involutive automorphism of $(M,μ)$ with the isolated fixed point $x$. We show that morphisms of …

Dynamic skewness models improve financial time series analysis.

Improves visualization of high-dimensional data by correcting misleading artifacts in neighbor embedding methods.

Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.

Self-calibrating conformal prediction improves interval efficiency and offers a practical alternative.

Study uses deep learning to predict asset prices, finds complex target processes lead to meaningless predictions.

The paper emphasizes the importance of joint predictions over marginal predictions for decision-making.

Behavior modification improves prediction accuracy by nudging user behavior.

Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather than set predictions. The extra information provided by conformal predictive systems may be useful, e.g., in decision making problems. Conf…

Predictions can shape outcomes, study helps predict these effects.

Prediction problems often admit competing models that perform almost equally well. This effect challenges key assumptions in machine learning when competing models assign conflicting predictions. In this paper, we define predictive multiplicity as the ability of a prediction problem to admit competing models with confl…

Proposes feature conformal prediction for broader application in semantic feature spaces.

Acute kidney injury (AKI) commonly occurs in hospitalized patients and can lead to serious medical complications. In order to optimally predict AKI before it develops at any time during a hospital stay, we present a novel framework in which AKI is continually predicted automatically from EHR data over the entire hospit…

AutoCP automates the construction of accurate prediction intervals.

Proposes a method to apply conformal prediction to probabilistic time series forecasting models.

ICP improves prediction intervals for continuous outcomes at lower computational cost.

Optimizes predictions for specific tasks using parametrized decision analysis.

This paper discusses a counterpart of conformal prediction for e-values, conformal e-prediction. Conformal e-prediction is conceptually simpler and had been developed in the 1990s as a precursor of conformal prediction. When conformal prediction emerged as result of replacing e-values by p-values, it seemed to have imp…

RFpredInterval package builds prediction intervals for random forests and boosted forests.

FPPI selectively uses predictions to improve inference efficiency.

COP improves online conformal prediction by incorporating data patterns, leading to tighter prediction sets.

Most existing examples of full conformal predictive systems, split-conformal predictive systems, and cross-conformal predictive systems impose severe restrictions on the adaptation of predictive distributions to the test object at hand. In this paper we develop split-conformal and cross-conformal predictive systems tha…

This paper studies trade-offs in private prediction methods.