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Found 17 papers, 7 papers with code
On sampling determinantal and Pfaffian point processes on a quantum computer
Most applications require sampling from a DPP, and given their quantum origin, it is natural to wonder whether sampling a DPP on a quantum computer is easier than on a classical one.
Sparsification of the regularized magnetic Laplacian with multi-type spanning forests
We provide statistical guarantees for a choice of natural estimators of the connection Laplacian, and investigate two practical applications of our sparsifiers: ranking with angular synchronization and graph-based semi-supervised learning.
Recovering Hölder smooth functions from noisy modulo samples
We consider a fixed design setting where the modulo samples are given on a regular grid.
Nonparametric estimation of continuous DPPs with kernel methods
Determinantal Point Process (DPPs) are statistical models for repulsive point patterns.
Towards Deterministic Diverse Subset Sampling
Determinantal point processes (DPPs) are well known models for diverse subset selection problems, including recommendation tasks, document summarization and image search.
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
A generative model may overlook underrepresented modes that are less frequent in the empirical data distribution.
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems
Semi-parametric regression models are used in several applications which require comprehensibility without sacrificing accuracy.
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks
However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.
Denoising modulo samples: k-NN regression and tightness of SDP relaxation
The estimates of the samples $f(x_i)$ can be subsequently utilized to construct an estimate of the function $f$, with the aforementioned uniform error rate.
Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes
By using the framework of Determinantal Point Processes (DPPs), some theoretical results concerning the interplay between diversity and regularization can be obtained.
The Bures Metric for Generative Adversarial Networks
However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.
Diversity sampling is an implicit regularization for kernel methods
The Nystr\"om approximation -- based on a subset of landmarks -- gives a low rank approximation of the kernel matrix, and is known to provide a form of implicit regularization.
Wasserstein Exponential Kernels
In the context of kernel methods, the similarity between data points is encoded by the kernel function which is often defined thanks to the Euclidean distance, a common example being the squared exponential kernel.
Nyström landmark sampling and regularized Christoffel functions
In this context, we propose a deterministic and a randomized adaptive algorithm for selecting landmark points within a training data set.
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions
In machine learning or statistics, it is often desirable to reduce the dimensionality of a sample of data points in a high dimensional space $\mathbb{R}^d$.
Robust Classification of Graph-Based Data
A graph-based classification method is proposed for semi-supervised learning in the case of Euclidean data and for classification in the case of graph data.
Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning
In this paper, Kernel PCA is reinterpreted as the solution to a convex optimization problem.
