Regularized mixtures improve inflation and interest rate forecasts, especially correcting overconfidence.
problem Improving density forecasts of Eurozone inflation and real interest rates.
method Construct regularized mixtures of density forecasts with various objectives and penalties.
result Regularized mixtures outperform individual forecasters, especially correcting overconfidence.
New method resolves density ratio estimation saturation issues.
problem Error saturation in density ratio estimation methods.
method Iterated regularization to improve kernel methods.
result Achieves fast error rates on regular learning problems.
Noise regularization improves CDE models without overfitting.
problem Overfitting in neural network-based conditional density estimation.
method Noise regularization method that adds random perturbations to data.
result Noise regularization significantly outperforms other methods across various datasets and models.
Adapts RKHS methods to estimate density ratios with optimal error.
problem Estimating density ratios from limited data.
method Minimizes regularized Bregman divergence in RKHS, with Lepskii type parameter choice.
result Adaptive minimax optimal error rate for quadratic loss.
New method estimates densities using Sobolev regularization, outperforming existing algorithms.
problem Non-parametric density estimation with clear inductive bias.
method Regularizes Sobolev norm of density, approximates kernel via sampling, uses natural gradients for optimization.
result Method ranks second best on ADBench anomaly detection benchmark.
Log-density gradient estimation is a fundamental statistical problem and possesses various practical applications such as clustering and measuring non-Gaussianity. A naive two-step approach of first estimating the density and then taking its log-gradient is unreliable because an accurate density estimate does not neces…
A new method improves flow matching by dynamically weighting density estimates.
problem High-dimensional integration inefficiency in flow matching.
method Density-weighted Dynamic Stein operators.
result Significant improvement in vector field smoothness and sampling efficiency.
Unified framework for constructing nonconvex sparse recovery methods.
problem Constructing valid nonconvex regularization functions remains open.
method Unified framework based on probability density function, using Weibull distribution.
result New nonconvex sparse recovery method based on Weibull distribution.
Lower bounds on cone density for nontrivial complements in low dimensions.
problem Finding density limits for minimal cones with nontrivial complements.
method Proving lower bounds on cone density for cones of dimensions less than seven with nontrivial complements.
result Established lower bounds on cone density for minimal cones with nontrivial complements in dimensions less than seven.
A novel density regularizer improves data interpolation on non-simply-connected manifolds.
problem Topological differences between model-defined simply-connected manifolds and dataset's non-simply-connected regions.
method Density regularizer to circumvent low-probability-density regions (holes).
result Consistently better interpolation results on real-world image datasets.
Proves regularity for stable varifolds near cones, expanding previous work.
problem Understanding stable varifolds near singularities.
method Proves C1,α regularity for varifolds in SQ. result Furthers understanding of local structure near singularities.
New method trains deep neural networks for non-interacting kinetic-energy functionals in DFT.
problem Lack of exact relationship between electron density and non-interacting kinetic energy.
method Variational principle to regularize machine-learned density functionals.
result Excellent results on kinetic-energy functionals for various systems.
A method connects KDE to sparse mixture models with adaptive regularization.
problem Estimating Gaussian mixture models from sparse data.
method Generalized expectation-maximization method with adaptive regularization.
result Sparse mixture models retain details from adaptive KDE.
Study on regularity of optimal transport maps on convex domains with quadratic cost.
problem Regularity of optimal transport maps between convex domains with quadratic cost.
method Analysis of Cα-densities and C1,α boundary conditions, monotonicity formula for optimal transport maps. result Proves C1,1−ε-regularity for nondegenerate Cα-densities and C2,α-regularity for C1,α boundary. The paper improves neural network-based conditional density estimation for finance.
problem Capturing statistical relationships between variables using neural networks.
method Best practices and benchmarks for conditional density estimation with noise regularization and data normalization.
result Proposed methodology outperforms other estimators in various benchmarks.
A simple regularization method improves model generalization.
problem Overfitting due to lack of labeled data in machine learning models.
method Density-fixing regularization method based on class prior distribution.
result Improves model generalization performance by approximating class prior distribution.
Proves ε-regularity for capillary surfaces in Riemannian manifolds.
problem Regularity of minimal surfaces with capillary boundary conditions.
method Uniform first variation control and ε-regularity theorems for varifolds.
result Capillary varifolds with bounded mean curvature and close to a capillary half-plane coincide with a C1,α properly embedded hypersurface. We accelerate CNF by reducing ODE truncation errors with polynomial regularization.
problem High computation cost of CNF due to large truncation errors in solving ODEs.
method Add polynomial regularization to approximate ODE trajectories with polynomial functions.
result 42.3% to 71.3% reduction of NFE on density estimation, 19.3% to 32.1% on variational auto-encoder.
The paper discusses new Lagrangian constructions and examples.
problem Exploring new Lagrangian constructions in complex projective spaces.
method Generalized Delaunay construction among minimal Lagrangians.
result Uncountably many new special Lagrangian cones found.
Unified view of score estimators for flexible densities.
problem Estimating the score from unknown distributions.
method Regularized nonparametric regression framework.
result Unified convergence analysis and new estimators with desirable properties.
Adversarial learning tackles unnormalized densities without samples.
problem Learning from unnormalized densities without direct samples.
method Adversarial learning extended to unnormalized densities, with new GAN regularization concepts.
result Encouraging results across applications, including deep soft Q-learning.
MAE tackles KL Varnishing in VAEs by controlling latent space geometry.
problem KL Varnishing in VAEs with expressive decoders.
method Mutual posterior-divergence regularization to control latent space geometry.
result MAE achieves comparable or superior density estimation and meaningful representation learning.
Statistical leverage scores emerged as a fundamental tool for matrix sketching and column sampling with applications to low rank approximation, regression, random feature learning and quadrature. Yet, the very nature of this quantity is barely understood. Borrowing ideas from the orthogonal polynomial literature, we in…
Paper compares optimal denoising methods for generative models, finding different results based on data regularity.
problem Optimizing denoising in score-based generative models for various data types.
method Comparison of full-denoising and half-denoising approaches, analyzing performance in terms of distribution distances.
result Different denoising methods perform better under different data regularity conditions.
Bi-Lipschitz flows approximate a wide range of distributions.
problem Characterizing the expressivity of bi-Lipschitz normalizing flows.
method Linking score regularity to transport map bi-Lipschitzness via probability flow ODE.
result Gaussian pullbacks induced by bi-Lipschitz variance-preserving transport maps are L1-dense among all probability densities. A new probabilistic mixup framework improves deep learning generalization.
problem Improving deep learning generalization performance.
method Introduces a novel probabilistic mixup framework for conditional density estimation.
result Empirical results show benefits over existing mixup variants.
For a given boundary set consisting of arcs and vertices, with two or more arcs meeting at each vertex, we treat the problem of estimating the area density of a soap film-like surface spanning the boundary.
We extend the geometric Hamilton-Jacobi formalism for hamiltonian mechanics to higher order field theories with regular lagrangian density. We also investigate the dependence of the formalism on the lagrangian density in the class of those yelding the same Euler-Lagrange equations.
A new method for faster estimation of Wasserstein distance using Sinkhorn divergence.
problem Estimating the squared Wasserstein distance between probability distributions.
method Proposes a new estimator based on the Sinkhorn divergence with debiasing terms, and analyzes its sample complexity and computational efficiency.
result The proposed estimator allows higher regularization levels, leading to improved computational complexity and speedup in practice.
Optimal transport framework for density estimation with constraints.
problem Density estimation under expectation constraints.
method Minimizes Wasserstein distance subject to expected value constraints and regularization.
result Framework effectively addresses non-smooth constraints through annealing-like algorithm.
Investigates regularity of solutions to complex Hessian equation.
problem Regularity of solutions to complex Hessian equation.
method Analyzes solutions to Dirichlet problem with specific density condition.
result Establishes conditions for regularity of solutions.
Estimates Gaussian location model with ridge regularization, comparing variational and spectral methods.
problem Estimating parameters in Gaussian location model with regularization.
method Ridge-regularized log-density-ratio estimation, variational and spectral approaches.
result Regularized variational estimator has lower risk with many observations, spectral estimator with fewer observations.
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. A new method bridges explicit and implicit deep generative models using Stein discrepancy.
problem Limitations of explicit and implicit deep generative models.
method Joint training framework that combines an explicit density estimator and an implicit sample generator via Stein discrepancy.
result The method improves the accuracy of density estimation and quality of generated samples.
The variational autoencoder (VAE) is a powerful generative model that can estimate the probability of a data point by using latent variables. In the VAE, the posterior of the latent variable given the data point is regularized by the prior of the latent variable using Kullback Leibler (KL) divergence. Although the stan…
PresGANs improve GANs by mitigating mode collapse and enhancing log-likelihood.
problem GANs struggle with mode collapse and lack a reliable way to evaluate generalization.
method PresGANs add noise to density networks and use entropy regularization to stabilize training and capture all modes.
result PresGANs reduce the gap in predictive log-likelihood between GANs and VAEs.
Proves regularity for stable varifolds near specific cones.
problem Regularity of stable codimension one integral varifolds near certain cones.
method Develops blow-up arguments and inductively performs finer blow-up procedures.
result Proves C1,α regularity for varifolds close to specific cones. A graph clustering method that moves nodes to highest-degree neighbors.
problem Graph clustering for data with Morse regularity.
method Max-degree hill-climbing on graph nodes.
result Asymptotically consistent for random geometric graphs.
Density of smooth functions in Sobolev space on manifolds with curvature bounds.
problem Density of Cc∞ in Wk,p on manifolds with curvature bounds. method Gradient regularity lemma, construction of counterexamples.
result Existence of manifolds where density in Wk,p does not hold. In this article we use the mean curvature flow with surgery to derive regularity estimates for the level set flow going past Brakke regularity in certain special conditions allowing for 2-convex regions of high density. We also show a stability result for the plane under the level set flow.
We prove that capillary surfaces converge to a specific energy density as the angle approaches zero.
problem Understanding the limiting behavior of capillary surfaces as the angle approaches zero.
method Rigorous limiting analysis and application to curvature estimates.
result Capillary area-density converges to the Weiss energy density as the angle tends to zero.
The paper studies quasimorphisms on density-preserving diffeomorphisms of the Möbius band.
problem Exploring quasimorphisms on groups of diffeomorphisms of non-orientable manifolds.
method Investigates the group of density-preserving diffeomorphisms on the Möbius band and shows the existence of unbounded quasimorphisms.
result The group of density-preserving diffeomorphisms on the Möbius band admits countably many unbounded quasimorphisms.
The study shows that certain graphs are regular at boundary points.
problem Boundary regularity of anisotropic minimal Lipschitz graphs.
method Proves regularity for graphs with bounded anisotropic mean curvature and atomic energy condition.
result Regularity at boundary points with density bounded above by 1/2 + σ.
Estimates tree-based density from random vectors.
problem Estimating the density of a random vector in high dimensions.
method Optimal spanning tree minimizes Kullback-Leibler divergence; tree density estimate constructed from i.i.d. data.
result Tree density estimate converges to true density as sample size increases.
Paper proves minimal resistance for a body in a fluid with decreasing density.
problem Minimal resistance for a body moving through a fluid with non-constant density.
method Local existence and regularity of radial solutions using a fixed-point theorem.
result Maximal domain of the solution is finite, terminating at a critical slope.
The paper proves smoothness of Brakke flows up to the end-time.
problem Smoothness of Brakke flows up to the end-time.
method Local regularity theorem for Brakke flows, extending White's theorem.
result Smooth extension of Brakke flows up to the end-time.
Combines coarse learners for nonparametric probabilistic regression.
problem Predicting full probability density functions without strong assumptions.
method Combining gradient boosted forests trained on coarsened target values.
result Prediction intervals have high fidelity and provide valuable insights.
We prove that a theorem of Pawlucki, showing that Whitney regularity for a subanalytic set with a smooth singular locus of codimension one implies the set is a finite union of differentiable manifolds with boundary, applies to definable sets in polynomially bounded o-minimal structures. We give a refined version of Paw…