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.
Optimal rates for vector-valued regression on various norms.
Study confirms learning rates for vector-valued spectral algorithms, proving consistency.
Randomized algorithm solves vector-valued regression problems with low-rank operators.
Paper analyzes error bounds for learning with vector-valued RF, improving existing analyses.
Study optimizes online learning for vector-valued data regression.
The report analyzes infinite-dimensional output space regression.
Study improves self-normalized bounds for vector-valued processes beyond sub-Gaussianity.
Framework for transferring discount curve estimates across fixed-income product classes.
We demonstrate an equivalence between reproducing kernel Hilbert space (RKHS) embeddings of conditional distributions and vector-valued regressors. This connection introduces a natural regularized loss function which the RKHS embeddings minimise, providing an intuitive understanding of the embeddings and a justificatio…
Multi-output Gaussian processes (MOGP) are probability distributions over vector-valued functions, and have been previously used for multi-output regression and for multi-class classification. A less explored facet of the multi-output Gaussian process is that it can be used as a generative model for vector-valued rando…
Abstract: Generalizes multisymplectic forms to vector-valued versions.
We study the stability properties of nonlinear multi-task regression in reproducing Hilbert spaces with operator-valued kernels. Such kernels, a.k.a. multi-task kernels, are appropriate for learning prob- lems with nonscalar outputs like multi-task learning and structured out- put prediction. We show that multi-task ke…
Reduced-rank method improves least-squares regression under output regularity.
New algorithms optimize multiple tasks with shared similarities, reducing regret.
The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.
We discuss sharp Sobolev inequalities for vector valued maps.
Motivated by multi-task machine learning with Banach spaces, we propose the notion of vector-valued reproducing kernel Banach spaces (RKBS). Basic properties of the spaces and the associated reproducing kernels are investigated. We also present feature map constructions and several concrete examples of vector-valued RK…
SL(n) covariant valuations on Orlicz spaces are represented and characterized.
Proposes Siegel neural networks for improved classification tasks.
Boosting framework for vector-valued prediction with geometric stability.
The space of vector-valued forms on any manifold is a graded Lie algebra with respect to the Frolicher-Nijenhuis bracket. In this paper we consider multiplicative vector-valued forms on Lie groupoids and show that they naturally form a graded Lie subalgebra. Along the way, we discuss various examples and different char…
Vector-valued learning, where the output space admits a vector-valued structure, is an important problem that covers a broad family of important domains, e.g. multi-task learning and transfer learning. Using local Rademacher complexity and unlabeled data, we derive novel semi-supervised excess risk bounds for general v…
The paper shows vector-valued risk measures ignore dependence structures.
The paper introduces a new FOR framework using Huber and ε-insensitive losses.
The paper proposes methods to find a shared active subspace for multivariate vector-valued functions.
Optimal transport for vector Gaussian mixtures improves efficiency and structure preservation.
We present a framework to derive risk bounds for vector-valued learning with a broad class of feature maps and loss functions. Multi-task learning and one-vs-all multi-category learning are treated as examples. We discuss in detail vector-valued functions with one hidden layer, and demonstrate that the conditions under…
We approximate derivatives of functions on manifolds by embedding them and applying vector-valued operators.
Paper introduces vector-valued variation spaces for multi-output neural networks.
Nyström approximation for scalable operator learning
In this paper we find solutions to a certain class of vector-valued parabolic Allen-Cahn equation that as develops as interface a given triod evolving under curve shortening flow.
Develops vector-valued RKBS for neural networks and operators.
This work improves multi-task regression performance using approximations of full-conformal prediction.
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed norm regularizers, including those that induce sparsity, to be imposed on a dictionary of vector-valued Reproducing Kernel Hilbert Spaces. W…
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed norm regularizers, including those that induce sparsity, to be imposed on a dictionary of vector-valued Reproducing Kernel Hilbert Spaces. W…
Paper develops a theory for Patterson-Sullivan measures in higher rank symmetric spaces.
New method transfers emotions in facial images.
In this study, we propose a new definition of multivariate conditional value-at-risk (MCVaR) as a set of vectors for discrete probability spaces. We explore the properties of the vector-valued MCVaR (VMCVaR) and show the advantages of VMCVaR over the existing definitions given for continuous random variables when adapt…
Paper proposes a new method to evaluate joint risk under uncertainty.
We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution. This equivalence provides a new understanding of kernel Bayesian inference. Moreover, the optimization problem induces a new regularization for the …
PSLR classifies functional data with scalar covariates using path signatures.
Study on identifying most preferred policy in bandits with vector-valued rewards.
In this work we address the problem of approximating high-dimensional data with a low-dimensional representation. We make the following contributions. We propose an inverse regression method which exchanges the roles of input and response, such that the low-dimensional variable becomes the regressor, and which is tract…
This paper presents a new model called infinite mixtures of multivariate Gaussian processes, which can be used to learn vector-valued functions and applied to multitask learning. As an extension of the single multivariate Gaussian process, the mixture model has the advantages of modeling multimodal data and alleviating…
This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued. We extend the graph trend filtering framework to denoising vector-valued graph signals with a family of non-convex regularizers, which exhibi…
Consider vector valued harmonic maps of at most linear growth, defined on a complete non-compact Riemannian manifold with non-negative Ricci curvature. For the norm square of the pull-back of the target volume form by such maps, we report a strong maximum principle, and equalities among its supremum, its asymptotic ave…
As in a symmetric space of noncompact type, one can associate to an oriented geodesic segment in a Euclidean building a vector valued length in the Euclidean Weyl chamber; in addition to the metric length it contains information on the direction of the segment. We study in this paper restrictions on the vector valued s…
Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie for the selection of groups of correlated variables. To investigate on the statistical properties of this scheme and in particular on its consistency properties, we set up …