arXiv research

Estimates financial market impacts of COVID-19 using time-varying kernel density.

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced per…

Study shows volume density in central harmonic spaces can vary arbitrarily.

Improved Mapper algorithm for datasets with varying density.

Generative model prices basket options efficiently.

BDMBC clusters data with varying densities using a new PLLS measure.

As one type of efficient unsupervised learning methods, clustering algorithms have been widely used in data mining and knowledge discovery with noticeable advantages. However, clustering algorithms based on density peak have limited clustering effect on data with varying density distribution (VDD), equilibrium distribu…

In this paper, we are concerned with the problem of creating flattening maps of simply-connected open surfaces in $\mathbb{R}^3$. Using a natural principle of density diffusion in physics, we propose an effective algorithm for computing density-equalizing flattening maps with any prescribed density distribution. By var…

The study shows how geometric Weyl bulk-density exponent rigidifies spectral encodings in O-regularly varying classes.

Develops diffusion models for time-varying correlation on the circle.

When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporating these paradigms are Spectral Clustering and DBSCAN. Both paradigms have their pros and cons. While minimum cut clusterings are sensitive t…

FNFs model parameter-dependent densities by combining a fixed flow with a polynomial parameter-dependent transformation.

This paper provides an insight to the time-varying dynamics of the shape of the distribution of financial return series by proposing an exponential weighted moving average model that jointly estimates volatility, skewness and kurtosis over time using a modified form of the Gram-Charlier density in which skewness and ku…

LAAT detects multiple low-density manifolds in noisy data.

This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm. Our investigation be…

Bayesian DDR models complex multivariate distributions.

A new tensor ring mixture model improves density estimation efficiency.

Develops diffusion samplers for target distributions with efficient score and density estimates.

Derives continuum model from discrete $\varepsilon$-graphs with connectivity functional.

A new method generates counterfactual treatment outcomes for time-varying treatments.

Variational inference provides a powerful tool for approximate probabilistic in- ference on complex, structured models. Typical variational inference methods, however, require to use inference networks with computationally tractable proba- bility density functions. This largely limits the design and implementation of v…

This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalized cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. M…

The Normalizing Flow (NF) models a general probability density by estimating an invertible transformation applied on samples drawn from a known distribution. We introduce a new type of NF, called Deep Diffeomorphic Normalizing Flow (DDNF). A diffeomorphic flow is an invertible function where both the function and its i…

This paper tackles continuous covariate shift by adaptively training predictors.

Joint distributions over many variables are frequently modeled by decomposing them into products of simpler, lower-dimensional conditional distributions, such as in sparsely connected Bayesian networks. However, automatically learning such models can be very computationally expensive when there are many datapoints and …

We develop a new density-based clustering algorithm named CRAD which is based on a new neighbor searching function with a robust data depth as the dissimilarity measure. Our experiments prove that the new CRAD is highly competitive at detecting clusters with varying densities, compared with the existing algorithms such…

New CTRL algorithm adapts to varying problem difficulty.

BASS efficiently learns time-varying graphs with low complexity and automatic tuning.

Enhances FAVAR models with autoencoder for better economic forecasting and interpretability.

A new kernel Stein test assesses fit for variable-length sequential data.

Accurate and reliable predictions of infectious disease dynamics can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task, using different model structures, covariates, and targets for prediction. Expe…

New sampling method uses gradient-free IPS with RKHS velocity field.

Robustly infers manifold density and geometry under high-dimensional noise.

In this paper, we study the benefits of using polyharmonic splines and node layouts with smoothly varying density for developing robust and efficient radial basis function generated finite difference (RBF-FD) methods for pricing of financial derivatives. We present a significantly improved RBF-FD scheme and successfull…

Adaptive ML learns complex time-varying systems without new data.

Many clustering schemes are defined by optimizing an objective function defined on the partitions of the underlying set of a finite metric space. In this paper, we construct a framework for studying what happens when we instead impose various structural conditions on the clustering schemes, under the general heading of…

Study on continuity of solutions for complex Monge-Ampère equations with movable singularities.

A new MCMC method combines low and high-fidelity models to reduce computation.

Lumbermark clusters data robustly, slicing limbs of mutual reachability trees.

Likelihood-free inference refers to inference when a likelihood function cannot be explicitly evaluated, which is often the case for models based on simulators. Most of the literature is based on sample-based `Approximate Bayesian Computation' methods, but recent work suggests that approaches based on deep neural condi…

We develop a novel "decouple-recouple" dynamic predictive strategy and contribute to the literature on forecasting and economic decision making in a data-rich environment. Under this framework, clusters of predictors generate different latent states in the form of predictive densities that are later synthesized within …

A new method detects anomalies in trajectory data using normalizing flows.

Let $X\subset \mathbb R^n$ be a connected locally closed definable set in an o-minimal structure. We prove that the following three statements are equivalent: (i) $X$ is a $C^1$ manifold, (ii) the tangent cone and the paratangent cone of $X$ coincide at every point in $X$, (iii) for every $x \in X$, the tangent cone of…

UAPCA projects uncertain data to low dimensions using GMMs.

The paper analyzes Kernel Density Estimation in high dimensions with varying data and dimensionality.

In a recent work (Chattopadhyay, A. K. et al, Europhys. Lett. {\bf 91}, 58003, 2010) based on food consumption statistics, we showed how a stochastic agent based model could represent the time variation of the income distribution statistics in a developing economy, thereby defining an alternative \enquote{poverty index…

This paper compares log-likelihood and BLEU scores for sequence generation tasks.

Algorithm learns graph ARMA processes for missing signal estimation.