AdaCat improves density estimation and planning in autoregressive models.
problem Efficiently modeling sharp density changes in continuous data.
method Adaptive Categorical Discretization (AdaCat) for autoregressive models.
result Improves density estimation and planning in various data types.
We prove that the crossing changes, Delta moves, and sharp moves are unknotting operations on welded knots.
A new method for unsupervised disentanglement using axis-aligned cliffs.
problem Unsupervised disentanglement of latent factors under nonlinear maps.
method Encouraging axis-aligned discontinuities (cliffs) in the estimated density of factors.
result Cliff method outperforms baselines on disentanglement benchmarks.
Sharp changes in time series representing market dynamics are studied by means of the self--similar analysis suggested earlier by the authors. These sharp changes are market booms and crashes. Such crises phenomena in markets are analogous to critical phenomena in physics. A simple classification of the market crisis p…
Random groups prove length constraints on product of conjugates.
problem Quantify products of conjugates in random groups.
method Sharp van Kampen diagram argument and boundary block-counting.
result Prove a sharp inequality for products of conjugates in random groups.
In this work, we propose new objective functions to train deep neural network based density ratio estimators and apply it to a change point detection problem. Existing methods use linear combinations of kernels to approximate the density ratio function by solving a convex constrained minimization problem. Approximating…
Sharp lower bound found for integral varifolds' mean curvature.
problem Finding a sharp lower bound for the mean curvature integral of integral varifolds.
method Developed a new approach using integral varifolds and mean curvature.
result A sharp lower bound on the mean curvature integral with critical power for integral varifolds.
Generalized change-point detection using various binary models.
problem Discovering changes in time series distribution.
method Direct density ratio estimation with Gradient Boosting over Decision Trees and Neural Networks.
result Proposed methods outperform classical RuLSIF algorithm.
Motivated by a recent work of Ache and Chang concerning the sharp Sobolev trace inequality and Lebedev-Milin inequalities of order four on the Euclidean unit ball, we derive such inequalities on the Euclidean unit ball for higher order derivatives. By using, among other things, the scattering theory on hyperbolic space…
DIF extends NF with stochastic discrete latent variables for better density estimation.
problem Improving density estimation with discontinuities and fine details.
method Discretely indexed flows as an extension of Normalizing Flows with stochastic latent variables.
result DIF inherit good computational behavior of NF and can capture distributions with discontinuities.
SGD favors flat minima exponentially more than sharp minima in deep learning.
problem Understanding how SGD selects flat minima in deep learning.
method Developed a density diffusion theory (DDT) to analyze minima selection.
result SGD exponentially favors flat minima over sharp minima due to Hessian-dependent noise.
We study the asymptotic behavior of distribution densities arising in stock price models with stochastic volatility. The main objects of our interest in the present paper are the density of time averages of the squared volatility process and the density of the stock price process in the Stein-Stein and the Heston model…
Develops a dynamical method to prove the sharp Berezin-Li-Yau inequality.
problem Proving the sharp Berezin-Li-Yau inequality for convex domains.
method Volume-preserving mean curvature flow and a new monotonicity principle.
result Shows the sharp Berezin-Li-Yau bound for every smooth convex domain.
Unified framework for generative models incorporating VAE and GAN.
problem Flexible incorporation of diverse measures of probability distance in generative models.
method Unified f-divergence generative model (f-GM) that incorporates both VAE and f-GAN.
result Unified f-GM enables flexible design of f-divergence functions without changing network structure.
The quotient of random variables with normal distributions is examined and proven to have have power law decay, with density f(x)≃f0x−2, with the coefficient depending on the means and variances of the numerator and denominator and their correlation. We also obtain the conditional probability…
Model tracks structural changes in Brownian particle configurations on a sphere.
problem Tracking structural changes in Brownian particle configurations on a sphere.
method Introduces Frustrated Distance Matrix (FDM) model for dynamic distance matrices on S^2.
result Preserves static BBS template with dynamics as redistributed spectral mass.
We derive the sharp Moser-Trudinger-Onofri inequalities on the standard n-sphere and CR (2n+1)- sphere as the limit of the sharp fractional Sobolev inequalities for all n≥1. On the 2-sphere and 4-sphere, this was established recently by S.-Y. Chang and F. Wang. Our proof uses an alternative and elementary …
The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the rela…
Deep belief networks can approximate any multivariate density with binary hidden units.
problem Approximating multivariate probability densities with binary hidden units.
method Sharp quantitative bounds on approximation error in terms of hidden units.
result Deep belief networks can approximate any multivariate density with binary hidden units under mild integrability requirements.
Sharp fractional Sobolev inequalities on closed manifolds identified.
problem Critical fractional Sobolev embedding on closed Riemannian manifolds.
method Intrinsic heat-kernel based framework, determining optimal coefficients, proving sharp inequalities.
result Sharp p-power inequality and almost sharp inequality established. Detect changes in noisy dynamical systems using empirical approximations and finite-sample bounds.
problem Change detection in noisy dynamical systems
method Partition-based empirical approximations and finite-state stationary distribution stability
result Finite-sample bound for empirical stationary density
Region crossing change for a knot or a proper link is an unknotting operation. In this paper, we provide a sharp upper bound on the region unknotting number for a large class of torus knots and proper links. Also, we discuss conditions on torus links to be proper.
The paper analyzes Kernel Density Estimation in high dimensions with varying data and dimensionality.
problem High-dimensional Kernel Density Estimation with growing data and dimensionality.
method Examines the behavior of Kernel Density Estimators in the regime where both data points and dimensionality grow with a fixed ratio.
result Three distinct statistical regimes are identified for Kernel-based density estimates, each with different statistical properties.
Optimizes quickest change detection with bounded means under ARL constraint.
problem Quickest detection of changepoints with bounded means under ARL constraint.
method Derives universal lower and upper bounds for detection delay.
result Achieves universal lower bound in the bounded mean detection setting.
We develop efficient and sharp bounds on policy value under perturbations in MDPs.
problem Evaluating policies under best- and worst-case perturbations in MDPs with transition observations.
method Proposed a perturbation model for MDPs, developed semiparametrically efficient estimator with asymptotic normality.
result Semiparametrically efficient and asymptotically normal estimator for policy value bounds.
The paper proves properties of strain tensors on surfaces with changing Gauss curvature.
problem Regularity of solutions to strain tensor equations on surfaces with variable Gauss curvature.
method Proof of regularity, density property, and matching property.
result Established matching property and density of smooth infinitesimal isometries.
We have discovered a "little" gap in our proof of the sharp conjecture that in Rn with volume and perimeter densities rm and rk, balls about the origin are uniquely isoperimetric if 0<m≤k−k/(n+k−1), that is, if they are stable (and m>0). The implicit unjustified assumption is that the g…
Study how nodal domains change on surfaces under perturbations.
problem How eigenfunction nodal domains change on surfaces under smooth perturbations.
method Sector/graph count near nodal critical points, upper semicontinuity proof, branch-free on spectral clusters, wavelength-scale analysis.
result Upper semicontinuity of nodal domain count, no new domains created at wavelength scale, stable count in noncritical cases.
We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we \emph{directly} learn the network structure change by estimating the ratio of Markov network m…
Study detects P-type bifurcations in single system realizations using unreliable kernel density estimates.
problem Detecting P-type bifurcations in signals with unreliable kernel density estimates.
method Create persistence diagrams from single system realization, statistically analyze resulting set, compare point process modeling methods.
result Subsampling outperforms other point process modeling methods in predicting P-type bifurcations.
Sharp generalization of boundary regularity for area minimizing currents with arbitrary multiplicity.
problem Boundary regularity of area minimizing currents with multiplicity.
method Sharp generalization of Allard's boundary regularity theorem to higher multiplicity settings.
result The set of density Q/2 singular boundary points of T is Hm−3-rectifiable. We prove that the density of a topologically nontrivial, area-minimizing hypercone with an isolated singularity must be greater than the square root of 2. The Simons' cones show that this is the best possible constant. If one of the components of the complement of the cone has nontrivial kth homotopy group, we prove a …
Sharp inequality for compactifying Poincaré-Einstein manifolds.
problem Proving a sharp relative comparison inequality for compactifying Poincaré-Einstein manifolds.
method Proved a sharp relative comparison inequality for type-I Escobar-Yamabe compactification.
result Confirms a conjecture by proving the sharp relative comparison inequality.
The paper approximates CARMA models for option pricing.
problem Approximating the transition density of CARMA(p, q) models.
method Using Gauss-Laguerre quadrature and time changed Brownian Motion.
result Provides an analytical formula for option prices.
Sharp curvature estimates lead to optimal C1,1 regularity for Lp Minkowski problems.
problem Optimal regularity for solutions to Lp Minkowski problems. method Anisotropic Gauss curvature flows and curvature estimates.
result Sharp C1,1 regularity for solutions to Lp Minkowski problems. Sharp bounds on negative impact identified from observational data.
problem Identifying the fraction of users negatively affected by a treatment.
method Developed robust inference algorithm to derive tightest-possible bounds on negative impact.
result Valid conservative bounds on the fraction negatively affected, even when functions are mislearned.
New IF method improves accuracy in deep neural networks with noisy data.
problem Inaccurate influence estimates in deep neural networks, especially with noisy data.
method Established a connection between influence estimation error, validation set risk, and sharpness, introducing a novel estimation form for flat validation minima.
result Our novel Influence Function approach provides more accurate influence estimates, validated across various tasks.
Improved Mapper algorithm for datasets with varying density.
problem Difficulty in tuning resolution for datasets with varying density.
method Generalized cover type and incorporated lens-space density into the cover.
result Graph produced by Mapper converges to Reeb graph of Rips complex.
Estimates eigenvalues of Jacobi operator for harmonic spaces.
problem Estimating eigenvalues of Jacobi operator.
method Using density function of a harmonic space.
result Sharp estimates imply symmetric Osserman space.
We study the problem of learning sparse structure changes between two Markov networks P and Q. Rather than fitting two Markov networks separately to two sets of data and figuring out their differences, a recent work proposed to learn changes \emph{directly} via estimating the ratio between two Markov network models…
We propose an artificial market model based on deterministic agents. The agents modify their ask/bid price depending on past price changes. The temporal development of market price fluctuations is calculated numerically. A probability density function of market price changes has power law tails. Autocorrelation coeffic…
AdaAnn optimizes annealing for efficient probability density approximation.
problem Efficiently approximating complex probability distributions with multiple modes.
method AdaAnn is an adaptive annealing scheduler that adjusts temperature increments based on KL divergence.
result AdaAnn improves computational efficiency in variational inference and parameter estimation.
The World Trade Web (WTW) is a weighted network whose nodes correspond to countries with edge weights reflecting the value of imports and/or exports between countries. In this paper we introduce to this macroeconomic system the notion of extinction analysis, a technique often used in the analysis of ecosystems, for the…
In this paper, we obtain sharp asymptotic formulas with error estimates for the Mellin convolution of functions, and use these formulas to characterize the asymptotic behavior of marginal distribution densities of stock price processes in mixed stochastic models. Special examples of mixed models are jump-diffusion mode…
Develops a new test for comparing two groups' densities, showing minimax optimality.
problem Comparing probability densities between two groups.
method Probabilistic tensor product smoothing spline framework for joint density modeling; penalized likelihood ratio test for interaction testing.
result Proposed test is minimax optimal and outperforms conventional approaches.
Estimation of density derivatives is a versatile tool in statistical data analysis. A naive approach is to first estimate the density and then compute its derivative. However, such a two-step approach does not work well because a good density estimator does not necessarily mean a good density-derivative estimator. In t…
Hierarchical nucleation patterns emerge in deep neural network layers.
problem Understanding the generation of meaningful representations in deep neural networks.
method Analysis of the probability density of ImageNet dataset across hidden layers.
result Density peaks in subsequent layers mirror the semantic hierarchy of concepts, resembling nucleation process.
EagleEye detects localized density anomalies in multivariate data.
problem Identifying signal events, regime changes, or model mismatch in scientific data.
method EagleEye pinpoints local over- and under-densities by assigning anomaly scores based on binary membership sequences and binomial null models.
result EagleEye can detect genuine local anomalies and estimate background purity.