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.
New algorithm improves dynamic mode decomposition for high-dimensional data.
This paper introduces Kernel-based Information Criterion (KIC) for model selection in regression analysis. The novel kernel-based complexity measure in KIC efficiently computes the interdependency between parameters of the model using a variable-wise variance and yields selection of better, more robust regressors. Expe…
Paper proposes an efficient causal discovery method with linear computational complexity.
FastKCI speeds up KCI tests for causal inference on large datasets.
Novel confidence intervals improve convergence rates for sparse kernel-based models.
Kernel based methods have shown effective performance in many remote sensing classification tasks. However their performance significantly depend on its hyper-parameters. The conventional technique to estimate the parameter comes with high computational complexity. Thus, the objective of this letter is to propose an fa…
Prediction of dynamical time series with additive noise using support vector machines or kernel based regression has been proved to be consistent for certain classes of discrete dynamical systems. Consistency implies that these methods are effective at computing the expected value of a point at a future time given the …
Adaptive rule improves kernel-based gradient descent performance.
Graph-structured data arise in wide applications, such as computer vision, bioinformatics, and social networks. Quantifying similarities among graphs is a fundamental problem. In this paper, we develop a framework for computing graph kernels, based on return probabilities of random walks. The advantages of our proposed…
Convolutional Neural Networks, as most artificial neural networks, are commonly viewed as methods different in essence from kernel-based methods. We provide a systematic translation of Convolutional Neural Networks (ConvNets) into their kernel-based counterparts, Convolutional Kernel Networks (CKNs), and demonstrate th…
This thesis improves kernel-based distances for statistical inference and integration.
We propose kernel-based collocation methods for numerical solutions to Heath-Jarrow-Morton models with Musiela parametrization. The methods can be seen as the Euler-Maruyama approximation of some finite dimensional stochastic differential equations, and allow us to compute the derivative prices by the usual Monte Carlo…
Kernel-based reinforcement learning (KBRL) stands out among reinforcement learning algorithms for its strong theoretical guarantees. By casting the learning problem as a local kernel approximation, KBRL provides a way of computing a decision policy which is statistically consistent and converges to a unique solution. U…
Kernel-based methods enjoy powerful generalization capabilities in handling a variety of learning tasks. When such methods are provided with sufficient training data, broadly-applicable classes of nonlinear functions can be approximated with desired accuracy. Nevertheless, inherent to the nonparametric nature of kernel…
Method approximates high-dimensional feature vectors for supervised learning.
Performing exact posterior inference in complex generative models is often difficult or impossible due to an expensive to evaluate or intractable likelihood function. Approximate Bayesian computation (ABC) is an inference framework that constructs an approximation to the true likelihood based on the similarity between …
New tools evaluate and optimize conditional sequence models in bioinformatics.
We implement an all-optical setup demonstrating kernel-based quantum machine learning for two-dimensional classification problems. In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while the model training is processed o…
Quantum Kerr learning shows enhancements in convergence and generalization for kernel-based methods.
We propose graph kernels based on subgraph matchings, i.e. structure-preserving bijections between subgraphs. While recently proposed kernels based on common subgraphs (Wale et al., 2008; Shervashidze et al., 2009) in general can not be applied to attributed graphs, our approach allows to rate mappings of subgraphs by …
Paper shows robustness of kernel-based pairwise learning without strict assumptions.
Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their des…
Kernel-based function approximation improves reinforcement learning performance.
Variable selection is central to high-dimensional data analysis, and various algorithms have been developed. Ideally, a variable selection algorithm shall be flexible, scalable, and with theoretical guarantee, yet most existing algorithms cannot attain these properties at the same time. In this article, a three-step va…
GP+ is a Python library for Gaussian process learning.
RQMC improves kernel-based learning by reducing deterministic error and offering computational advantages.
This paper introduces Bayes Hilbert spaces for efficient posterior approximation.
Independent component analysis (ICA) is a method for recovering statistically independent signals from observations of unknown linear combinations of the sources. Some of the most accurate ICA decomposition methods require searching for the inverse transformation which minimizes different approximations of the Mutual I…
New method speeds up uncertainty estimation for large datasets in causal inference.
Graph-based methods pervade the inference toolkits of numerous disciplines including sociology, biology, neuroscience, physics, chemistry, and engineering. A challenging problem encountered in this context pertains to determining the attributes of a set of vertices given those of another subset at possibly different ti…
New method estimates hazard ratios without bias in observational studies.
Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, we study the problem …
Paper introduces EO_k for quantifying accuracy-fairness trade-offs in FRL.
Study provides guarantees for kernel clustering under non-parametric mixtures.
Kernel-based L2-boosting with structure constraints improves regression efficiency.
New method speeds up kernel-based machine learning for force field reconstruction.
Paper introduces MinDiff framework for balancing classifier performance and fairness.
New theoretical tools simplify kernel-based tests analysis.
Paper introduces a new test for conditional independence using weighted partial copulas.
Random feature method approximates operators with theoretical guarantees and reduced computation.
A new kernel-based CI test improves on existing methods.
Boosting Nyström improves accuracy of matrix approximations.
Laplace kernel feature selection offers statistical guarantees for nonparametric models with few samples.
A new kernel for ranked data tackles computational challenges.
Optimal kernel improves estimation accuracy in modal statistical methods.
Kernel-based test detects differences between two conditional distributions efficiently.
Algorithm optimizes collaborative learning among distributed clients using kernel-based bandits.
We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to learn the effective …