arXiv research

This paper reviews state-of-the-art mixed data clustering algorithms.

Paper presents an econophysics model for mixed economies.

The theory of monotone Riemannian metrics on the state space of a quantum system was established by Denes Petz in 1996. In a recent paper he argued that the scalar curvature of a statistically relevant - monotone - metric can be interpreted as an average statistical uncertainty. The present paper contributes to this su…

The Riemannian Bures metric on the space of (normalized) complex positive matrices is used for parameter estimation of mixed quantum states based on repeated measurements just as the Fisher information in classical statistics. It appears also in the concept of purifications of mixed states in quantum physics. Here we d…

New model clusters mixed-type data with missing values, improving air quality analysis.

Mixed RL improves RL efficiency with dual representations.

Machine learning models accurately predict the state and dynamics of reactive mixing.

New method for mixed memberships using symmetrized Laplacian inverse matrix.

In this paper we propose a geometrization of the non-relativistic quantum mechanics for mixed states. Our geometric approach makes use of the Uhlmann's principal fibre bundle to describe the space of mixed states and as a novelty tool, to define a dynamic-dependent metric tensor on the principal manifold, such that the…

Researchers measure distances between quantum states to speed up machine learning.

HMQ improves quantization for edge devices with mixed precision.

New mixed singularities help classify real algebraic links.

initKmix generates better initial partitions for k-means clustering of mixed datasets.

Estimates Markov chain parameters from a single long sequence, analyzing complexity based on mixing properties.

PatchUp improves CNN robustness with mixed feature blocks.

We give a simple optimistic algorithm for which it is easy to derive regret bounds of $\tilde{O}(\sqrt{t_{\rm mix} SAT})$ after $T$ steps in uniformly ergodic Markov decision processes with $S$ states, $A$ actions, and mixing time parameter $t_{\rm mix}$. These bounds are the first regret bounds in the general, non-epi…

New AMP algorithm estimates signals and latent variables in mixed regression models.

In this article we discuss some of the consequences of the mixed membership perspective on time series analysis. In its most abstract form, a mixed membership model aims to associate an individual entity with some set of attributes based on a collection of observed data. Although much of the literature on mixed members…

The paper explores knotoids, pseudo knotoids, braidoids, and pseudo braidoids on the torus.

Many real-valued stochastic time-series are locally linear (Gassian), but globally non-linear. For example, the trajectory of a human hand gesture can be viewed as a linear dynamic system driven by a nonlinear dynamic system that represents muscle actions. We present a mixed-state dynamic graphical model in which a hid…

New RL method MAC improves performance in sparse reward settings.

Batchboost stabilizes training by mixing and pairing samples, improving accuracy.

Superpixel-mix enhances reliability in semantic segmentation.

QR-MIX models joint state-action values as a distribution to handle randomness in MARL.

Estimates mixing time of non-reversible Markov chains from a single trajectory.

MiVaBo optimizes mixed-variable functions efficiently, handling constraints.

MVRSM optimizes expensive functions with mixed variables, outperforming state-of-the-art methods.

New RL theory reduces sample complexity for mixing MDPs.

Unified framework for solving MDPs with stochastic mirror descent.

A new method generates mixed-type features in tabular data with improved realism and accuracy.

We introduce 'mixed LICORS', an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like sof…

A new clustering model for mixed datasets combines continuous and non-continuous data.

Paper tackles offline RL from mixed datasets with adaptive KL regularizer.

Unified approach tackles logical constraints in mixed-integer optimization.

MALA mixes optimally in κ√d steps for log-concave sampling.

MOCA-HESP optimizes high-dimensional combinatorial and mixed spaces using hyper-ellipsoid partitioning.

NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia

Efficiently finds diverse coherent counterfactual explanations.

Paper proposes a new approach for agents to explore environments efficiently.

New GP kernel handles mixed-categorical data, improving model accuracy.

Improved Metropolized HMC mixing time with multi-step gradients.

Improved state estimation in nonlinear models using amortized backward variational inference.

The analysis of mixed data has been raising challenges in statistics and machine learning. One of two most prominent challenges is to develop new statistical techniques and methodologies to effectively handle mixed data by making the data less heterogeneous with minimum loss of information. The other challenge is that …

Scientific explanation often requires inferring maximally predictive features from a given data set. Unfortunately, the collection of minimal maximally predictive features for most stochastic processes is uncountably infinite. In such cases, one compromises and instead seeks nearly maximally predictive features. Here, …

A quantum state generation method that respects physical constraints.

Common perpendiculars equidistribute in negatively curved spaces.

ILTS iteratively refines linear regression models on subsets of data.

Proposes CDTD, a diffusion model for mixed-type tabular data.