182 research outputs found
Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence
We design fast algorithms for repeatedly sampling from strongly Rayleigh
distributions, which include random spanning tree distributions and
determinantal point processes. For a graph , we show how to
approximately sample uniformly random spanning trees from in
time per sample after an initial
time preprocessing. For a determinantal point
process on subsets of size of a ground set of elements, we show how to
approximately sample in time after an initial
time preprocessing, where is
the matrix multiplication exponent. We even improve the state of the art for
obtaining a single sample from determinantal point processes, from the prior
runtime of to
.
In our main technical result, we achieve the optimal limit on domain
sparsification for strongly Rayleigh distributions. In domain sparsification,
sampling from a distribution on is reduced to sampling
from related distributions on for . We show that for
strongly Rayleigh distributions, we can can achieve the optimal
. Our reduction involves sampling from
domain-sparsified distributions, all of which can be produced efficiently
assuming convenient access to approximate overestimates for marginals of .
Having access to marginals is analogous to having access to the mean and
covariance of a continuous distribution, or knowing "isotropy" for the
distribution, the key assumption behind the Kannan-Lov\'asz-Simonovits (KLS)
conjecture and optimal samplers based on it. We view our result as a moral
analog of the KLS conjecture and its consequences for sampling, for discrete
strongly Rayleigh measures
Improved Financial Forecasting via Quantum Machine Learning
Quantum algorithms have the potential to enhance machine learning across a
variety of domains and applications. In this work, we show how quantum machine
learning can be used to improve financial forecasting. First, we use classical
and quantum Determinantal Point Processes to enhance Random Forest models for
churn prediction, improving precision by almost 6%. Second, we design quantum
neural network architectures with orthogonal and compound layers for credit
risk assessment, which match classical performance with significantly fewer
parameters. Our results demonstrate that leveraging quantum ideas can
effectively enhance the performance of machine learning, both today as
quantum-inspired classical ML solutions, and even more in the future, with the
advent of better quantum hardware
A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process
Learning rich skills through temporal abstractions without supervision of
external rewards is at the frontier of Reinforcement Learning research.
Existing works mainly fall into two distinctive categories: variational and
Laplacian-based skill (a.k.a., option) discovery. The former maximizes the
diversity of the discovered options through a mutual information loss but
overlooks coverage of the state space, while the latter focuses on improving
the coverage of options by increasing connectivity during exploration, but does
not consider diversity. In this paper, we propose a unified framework that
quantifies diversity and coverage through a novel use of the Determinantal
Point Process (DPP) and enables unsupervised option discovery explicitly
optimizing both objectives. Specifically, we define the DPP kernel matrix with
the Laplacian spectrum of the state transition graph and use the expected mode
number in the trajectories as the objective to capture and enhance both
diversity and coverage of the learned options. The proposed option discovery
algorithm is extensively evaluated using challenging tasks built with Mujoco
and Atari, demonstrating that our proposed algorithm substantially outperforms
SOTA baselines from both diversity- and coverage-driven categories. The codes
are available at https://github.com/LucasCJYSDL/ODPP
On simulation of continuous determinantal point processes
We review how to simulate continuous determinantal point processes (DPPs) and
improve the current simulation algorithms in several important special cases as
well as detail how certain types of conditional simulation can be carried out.
Importantly we show how to speed up the simulation of the widely used Fourier
based projection DPPs, which arise as approximations of more general DPPs. The
algorithms are implemented and published as open source software
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