Filters








7 Hits in 3.2 sec

Scalable Sampling for Nonsymmetric Determinantal Point Processes [article]

Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi
2022 arXiv   pre-print
A determinantal point process (DPP) on a collection of M items is a model, parameterized by a symmetric kernel matrix, that assigns a probability to every subset of those items.  ...  Furthermore, we develop a scalable sublinear-time rejection sampling algorithm by constructing a novel proposal distribution.  ...  All of the code implementing our constrained learning and sampling algorithms is publicly available † † . The proofs for our theoretical contributions are available in Appendix E.  ... 
arXiv:2201.08417v2 fatcat:rskpiwmvz5b4xge2l4qhsizxmm

Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes [article]

Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi
2022 arXiv   pre-print
A determinantal point process (DPP) is an elegant model that assigns a probability to every subset of a collection of n items.  ...  In this work, we develop a scalable MCMC sampling algorithm for k-NDPPs with low-rank kernels, thus enabling runtime that is sublinear in n.  ...  Amin Karbasi acknowledges funding in direct support of this work from NSF (IIS-1845032), ONR (N00014-19-1-2406), and the AI Institute for Learning-Enabled Optimization at Scale (TILOS).  ... 
arXiv:2207.00486v1 fatcat:4wdfyck74rfmxf3dqcfklibb7q

Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes [article]

Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel
2021 arXiv   pre-print
Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection.  ...  Recent work shows that nonsymmetric DPP (NDPP) kernels have significant advantages over symmetric kernels in terms of modeling power and predictive performance.  ...  INTRODUCTION Determinantal point processes (DPPs) have proven useful for numerous machine learning tasks.  ... 
arXiv:2006.09862v2 fatcat:ij6tpkl6bnhhppfikvatyffitm

Wasserstein Learning of Determinantal Point Processes [article]

Lucas Anquetil, Mike Gartrell, Alain Rakotomamonjy, Ugo Tanielian, Clément Calauzènes
2020 arXiv   pre-print
Determinantal point processes (DPPs) have received significant attention as an elegant probabilistic model for discrete subset selection.  ...  In this work, by deriving a differentiable relaxation of a DPP sampling algorithm, we present a novel approach for learning DPPs that minimizes the Wasserstein distance between the model and data composed  ...  In this work, we address the problem of training a determinantal point process (DPP), a probabilistic model for subsets drawn from a large collection of items.  ... 
arXiv:2011.09712v1 fatcat:onpygopy7bge7dawfsu3grlysu

Learning from DPPs via Sampling: Beyond HKPV and symmetry [article]

Rémi Bardenet, Subhroshekhar Ghosh
2020 arXiv   pre-print
Determinantal point processes (DPPs) have become a significant tool for recommendation systems, feature selection, or summary extraction, harnessing the intrinsic ability of these probabilistic models  ...  For DPPs with symmetric kernels, scalable HKPV samplers have been proposed that either first downsample the ground set of items, or force the kernel to be low-rank, using e.g.  ...  Introduction Determinantal point processes (abbrv. DPPs) have recently emerged as a powerful modelling paradigm in machine learning.  ... 
arXiv:2007.04287v1 fatcat:lt2dpjzhjjghjg66owsswthzxm

Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem [article]

Victor-Emmanuel Brunel
2018 arXiv   pre-print
Symmetric determinantal point processes (DPP's) are a class of probabilistic models that encode the random selection of items that exhibit a repulsive behavior.  ...  They have attracted a lot of attention in machine learning, when returning diverse sets of items is sought for. Sampling and learning these symmetric DPP's is pretty well understood.  ...  DETERMINANTAL POINT PROCESSES Definitions Definition 1 (Discrete Determinantal Point Process).  ... 
arXiv:1811.00465v1 fatcat:rdpvapqwr5bwnesbrtoulfpmnu

Nyström landmark sampling and regularized Christoffel functions [article]

Michaël Fanuel, Joachim Schreurs, Johan A.K. Suykens
2021 arXiv   pre-print
Beyond the known connection between Christoffel functions and leverage scores, a connection of our method with finite determinantal point processes (DPPs) is also explained.  ...  In this context, we propose a deterministic and a randomized adaptive algorithm for selecting landmark points within a training data set.  ...  Determinantal Point Processes.  ... 
arXiv:1905.12346v4 fatcat:cy4nua5a7rfp3ma5khkxrks44m