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05/25/2023
On sampling determinantal and Pfaffian point processes on a quantum computer
DPPs were introduced by Macchi as a model in quantum optics the 1970s. S...
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10/15/2022
Smoothing complex-valued signals on Graphs with Monte-Carlo
We introduce new smoothing estimators for complex signals on graphs, bas...
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08/31/2022
Sparsification of the regularized magnetic Laplacian with multi-type spanning forests
In this paper, we consider a U(1)-connection graph, that is, a graph whe...
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12/02/2021
Recovering Hölder smooth functions from noisy modulo samples
In signal processing, several applications involve the recovery of a fun...
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06/27/2021
Nonparametric estimation of continuous DPPs with kernel methods
Determinantal Point Process (DPPs) are statistical models for repulsive ...
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05/28/2021
Towards Deterministic Diverse Subset Sampling
Determinantal point processes (DPPs) are well known models for diverse s...
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04/06/2021
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
Commonly, machine learning models minimize an empirical expectation. As ...
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11/13/2020
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems
Semi-parametric regression models are used in several applications which...
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09/10/2020
Denoising modulo samples: k-NN regression and tightness of SDP relaxation
Many modern applications involve the acquisition of noisy modulo samples...
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06/24/2020
Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes
By using the framework of Determinantal Point Processes (DPPs), some the...
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06/16/2020
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks
Generative Adversarial Networks (GANs) are performant generative methods...
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06/12/2020
Disentangled Representation Learning and Generation with Manifold Optimization
Disentanglement is an enjoyable property in representation learning whic...
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02/20/2020
Diversity sampling is an implicit regularization for kernel methods
Kernel methods have achieved very good performance on large scale regres...
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02/05/2020
Wasserstein Exponential Kernels
In the context of kernel methods, the similarity between data points is ...
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05/29/2019
Nyström landmark sampling and regularized Christoffel functions
Selecting diverse and important items from a large set is a problem of i...
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11/20/2017
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions
In machine learning or statistics, it is often desirable to reduce the d...
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10/21/2016