A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Gaussian processes struggle with compositional functions, but deep Gaussian processes can outperform.
Compositional data have two unique characteristics compared to typical multivariate data: the observed values are nonnegative and their summand is exactly one. To reflect these characteristics, a specific regularized regression model with linear constraints is commonly used. However, linear constraints incur additional…
This paper addresses measurement errors in high-dimensional compositional data using a log-contrast model calibration approach.
Unified analysis of multi-task functional linear regression with manifold and composite penalties.
Paper introduces arctan pinball loss for XGBoost quantile regression.
New methods for predicting compositional data using conformal prediction.
Deep model tackles claim size modeling with quantile-based regression.
Paper tackles distributed linear regression with compositional covariates.
Deep-HGP uses Bayesian nonparametric approach for complex data regression.
Adapts Altman's model to compositional data for bankruptcy prediction.
Develops methods for causal inference in compositional data using instrumental variables.
In this paper, we extend the geometric descent method recently proposed by Bubeck, Lee and Singh to tackle nonsmooth and strongly convex composite problems. We prove that our proposed algorithm, dubbed geometric proximal gradient method (GeoPG), converges with a linear rate and thus achieves the optimal …
We consider the minimization of composite objective functions composed of the expectation of quadratic functions and an arbitrary convex function. We study the stochastic dual averaging algorithm with a constant step-size, showing that it leads to a convergence rate of O(1/n) without strong convexity assumptions. This …
New method for causal inference with complex treatment compositions.
New sparse GP model learns compositional kernels efficiently.
Study uses AI and ML to predict and optimize corrosion resistance of aluminum alloys.
Logistic regression models are a popular and effective method to predict the probability of categorical response data. However inference for these models can become computationally prohibitive for large datasets. Here we adapt ideas from symbolic data analysis to summarise the collection of predictor variables into his…
New active learning methods for Gaussian process improve predictive modeling of composite fuselage.
This paper introduces compositional data analysis for financial ratios, improving industry-level analysis.
Consider the multivariate nonparametric regression model. It is shown that estimators based on sparsely connected deep neural networks with ReLU activation function and properly chosen network architecture achieve the minimax rates of convergence (up to -factors) under a general composition assumption on the re…
We formalize notions of robustness for composite estimators via the notion of a breakdown point. A composite estimator successively applies two (or more) estimators: on data decomposed into disjoint parts, it applies the first estimator on each part, then the second estimator on the outputs of the first estimator. And …
Fast detection of changepoints in linear regression models.
KernelBiome tackles microbiome research by improving predictive performance and interpretability.
Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applying machine learning methods to create predictive models capable of mapping from composition and crystal structures to materials properties. …
Neural networks outperform NTK on compositional tasks, revealing a complexity gap.
The paper explores using historical data to improve clinical trial analysis by optimizing covariate weights.
Unified algorithm for minimizing composite functions with flexible design.
HKRR adapts to MIM, overcoming the curse of dimensionality.
In many applications one may acquire a composition of several signals that may be corrupted by noise, and it is a challenging problem to reliably separate the components from one another without sacrificing significant details. Adding to the challenge, in a compressive sensing framework, one is given only an undersampl…
Point forecasting of univariate time series is a challenging problem with extensive work having been conducted. However, nonparametric probabilistic forecasting of time series, such as in the form of quantiles or prediction intervals is an even more challenging problem. In an effort to expand the possible forecasting p…
We propose a robust inferential procedure for assessing uncertainties of parameter estimation in high-dimensional linear models, where the dimension can grow exponentially fast with the sample size . Our method combines the de-biasing technique with the composite quantile function to construct an estimator that …
This paper advances FL algorithms for composite optimization and statistical recovery.
This paper shows that scientific discovery can be efficiently learned via compositional function trees, reducing the sample complexity.
Many machine learning models, such as logistic regression~(LR) and support vector machine~(SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization~(DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better per…
In this paper, we propose a compositional nonparametric method in which a model is expressed as a labeled binary tree of nodes, where each node is either a summation, a multiplication, or the application of one of the basis functions to one of the covariates. We show that in order to recover a labeled bi…
SCQRNN prevents quantile crossing and improves computational efficiency.
Enhances topic-metadata relationship modeling using Bayesian methods.
A new method for linear regression using feature graphs and hierarchical shrinkage.
In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for composite functions. Our approach exploits closed-form solutions of proximal operators and envelope representations based on the Moreau, Fo…
DSPPs improve predictive distributions in scalable regression tasks.
In this contribution we describe an approach to evolve composite covariance functions for Gaussian processes using genetic programming. A critical aspect of Gaussian processes and similar kernel-based models such as SVM is, that the covariance function should be adapted to the modeled data. Frequently, the squared expo…
Enhances DGPs with adaptive RKHS Fourier features for better non-stationary pattern modeling.
Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art. We define a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels. We present a method for searching over this space of structures which mirro…
We study pathwise invariances of centred random fields that can be controlled through the covariance. A result involving composition operators is obtained in second-order settings, and we show that various path properties including additivity boil down to invariances of the covariance kernel. These results are extended…
Paper proposes deep neural networks for nonparametric regression from dependent data.
Deep single-index Fréchet regression for metric space-valued outputs
Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions. The Automatic Bayesian Covariance Discovery (ABCD) system constructs natural-language description of time-series data by treating unknown time-series data nonparametrically using GP with …
Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, [1] pointed out that the hierarchical s…