Study inverse curve shortening flow on hyperbolic plane, classifying solitons.
problem Understanding the behavior of curves in hyperbolic geometry under a specific flow.
method Classifying solitons with respect to vector fields and studying their properties.
result Parabolic solitons are graphs on the y-axis, conformal solitons on the x-axis.
Simple connection between Harnack inequalities and concavity of arrival time functions.
problem Proving differential Harnack inequalities for various flows.
method Directly proving concavity properties of time-of-arrival functions for a class of flows using a concavity maximum principle.
result Short proof of Hamilton's and Andrews' differential Harnack inequalities.
The paper studies how the shape of surfaces changes over time using curvature.
problem Understanding how the shape of surfaces evolves over time using curvature.
method The authors use curvature flow with a power of a function of principal curvatures to study the evolution of surfaces.
result The complete smooth strictly convex solution exists and remains a graph until the maximal time of existence.
Method solves Bayesian inverse problems in function space without assuming log-concavity.
problem Bayesian inverse problems in infinite-dimensional nonlinear settings.
method Score-based diffusion models as a prior, Langevin-type MCMC on function spaces.
result Provable convergence bound for posterior sampling, dependent on score approximation.
It was recently proved that embedded solutions of Euclidean hypersurface flows with speeds given by concave (convex), degree one homogeneous functions of the Weingarten map are interior (exterior) non-collapsing. These results were subsequently extended to hypersurface flows in the sphere and hyperbolic space. In the f…
We consider curvature flows in hyperbolic space with a monotone, symmetric, homogeneous of degree 1 curvature function F. Furthermore we assume F to be either concave and inverse concave or convex. For compact initial hypersurfaces, which are strictly convex by horospheres, we show the long time existence of mixed volu…
We consider inverse curvature flows in the (n+1)-dimensional Euclidean space, n≥2, expanding by arbitrary negative powers of a 1-homogeneous, monotone curvature function F with some concavity properties. We obtain asymptotical roundness, meaning that circumradius minus inradius of the flow hypersurfaces decay…
We reformulate LIPs as min-max problems for easier solution.
problem Recovering signals from few linear measurements.
method Proposed a min-max reformulation of LIPs.
result Saddle points characterize solutions to LIPs.
The paper proves convergence of certain curvature flows to the origin.
problem Analyzing the convergence of specific curvature flows in Euclidean space.
method Examining fully nonlinear contracting curvature flows with given normal speeds.
result The flows converge exponentially to a sphere centered at the origin after rescaling.
The paper studies how certain surfaces evolve in space without collapsing.
problem Evolution of surfaces with inhomogeneous speeds without collapsing.
method Analyzes curvature flows with a specific speed function and structural conditions.
result Establishes exterior noncollapsing estimates for the flow.
Study Langevin Monte Carlo for sampling non-log-concave distributions.
problem Sampling from non-log-concave distributions, especially Gaussian mixtures.
method Discretizations of overdamped Langevin diffusions.
result Numerical simulations compare Langevin Monte Carlo algorithms' performance.
New algorithm speeds up sampling from log-concave distributions over polytopes.
problem Sampling from log-concave distributions constrained to polytopes efficiently.
method Improved Markov chain with efficient linear solvers and randomized estimators.
result Per-step complexity is nearly optimal, with reduced arithmetic operations.
New method improves posterior sampling for complex data models.
problem Sampling from posterior distributions in high-dimensional data.
method Tilted transport technique combining denoising oracle and log-likelihood.
result Boosted posterior is strongly log-concave, facilitating easier sampling.
We prove new pinching estimate for the inverse curvature flow of strictly convex hypersurfaces in the space form N of constant sectional curvature KN with speed given by F−α, where α∈(0,1] for KN=0,−1 and α=1 for KN=1, F is a smooth, symmetric homogeneous of degree one function which is inverse…
New flow expands hypersurfaces in hyperbolic space, showing round limiting shape for certain powers.
problem Understanding the limiting shape of hypersurfaces expanding in hyperbolic space.
method Introduced shifted inverse curvature flow with positive power p for a smooth curvature function. result For 0<p≤1, limiting shape is always round as maximal existence time is approached. Solves signal recovery from few linear measurements using convex duality.
problem Recovering signals from limited linear measurements in various applications.
method Develops a convex-concave min-max reformulation for linear inverse problems.
result Simple ascent-descent algorithms for solving linear inverse problems.
The paper studies stability of mean-field variational inference for log-concave distributions.
problem Stability of mean-field variational inference for log-concave distributions.
method Novel approach via linearized optimal transport, lifting non-convex problem to convex optimization over transport maps.
result Dimension-free Lipschitz continuity of the MFVI optimizer with respect to the target distribution, measured in 2-Wasserstein distance.
The article extends previous work on contracting convex hypersurfaces by nonhomogeneous curvature functions.
problem Contraction of convex hypersurfaces by nonhomogeneous functions of curvature.
method Extending previous results to various cases, showing convergence to asymptotically round points under pinching conditions.
result Convergence to asymptotically round points under suitable rescaling and pinching conditions.
We analyze a nonlinear equation proposed by F. Black (1968) for the optimal portfolio function in a log-normal model. We cast it in terms of the risk tolerance function and provide, for general utility functions, existence, uniqueness and regularity results, and we also examine various monotonicity, concavity/convexity…
Paper tackles sampling from non-log-concave distributions using denoising diffusion.
problem Sampling from non-log-concave distributions efficiently.
method DDMC framework, Zeroth-Order Diffusion Monte Carlo (ZOD-MC) algorithm.
result ZOD-MC achieves inverse polynomial dependence on sampling accuracy, efficient for low dimensions.
Researchers develop methods to recover agent behavior from sparse data using Gaussian processes.
problem Recovering agent behavior from limited, noisy data in potential mean field games.
method Two Gaussian process-based frameworks: inf-sup formulation and bilevel approach.
result Surrogate MFG models can accurately reproduce observed data, even when prior information is limited.
New methods tackle complex inverse problems with scalable optimization-based MCMC.
problem Estimating high-dimensional model parameters and hyperparameters in nonlinear hierarchical statistical inverse problems.
method Optimization-based Markov chain Monte Carlo (MCMC) methods using RTO and pseudo-marginal MCMC.
result Efficient sampling tools for hierarchical Bayesian inversion with robust performance to model parameter dimensions.
Study finds non-monotonic Value of Information in dynamic multi-market monopoly.
problem Investigates non-monotonicity in Value of Information for a price-setting monopolist.
method Uses a Bayesian inverse problem with Kalman-Bucy-Stratonovich filter in a dynamic discrete model.
result Non-monotonic relationship between signal variance and Value of Information.
A new method lifts training of input-convex neural networks to avoid dead weights and plateaued loss.
problem Training input-convex neural networks with non-negative weights.
method Introduces a hypernetwork that emits non-negative weights from a summary of the input batch, adding stochasticity to soften the loss landscape.
result The lift method achieves lower test loss than projected gradient descent and direct softplus reparametrization.
Polynomial mixing times for simulated tempering in mixture sampling problems.
problem Sampling from mixtures of log-concave distributions with location shifts.
method Conductance decomposition applied to an auxiliary Markov chain on an augmented space.
result First polynomial-time guarantee for simulated tempering with MALA.
Solves imaging inverse problems using a VAE prior and joint MAP optimization.
problem Solving ill-posed inverse problems in imaging.
method Joint Posterior Maximization with a VAE prior, using alternate optimization algorithms and stochastic encoding.
result Converges to high-quality solutions close to bi-convex, outperforming non-convex MAP approaches.
New algorithm proves convergence for MAP estimation with denoisers.
problem Proving convergence of MAP estimation methods using pretrained denoisers.
method A simple gradient descent algorithm on smoothed proximal objectives.
result Algorithm provably converges to the proximal operator under log-concavity.
We consider the quermassintegral preserving flow of closed \emph{h-convex} hypersurfaces in hyperbolic space with the speed given by any positive power of a smooth symmetric, strictly increasing, and homogeneous of degree one function f of the principal curvatures which is inverse concave and has dual f∗ approachi…
Predict covariance from features using convex optimization.
problem Predicting the covariance of a Gaussian vector from another feature vector.
method A generalized linear model with convex optimization for fitting parameters.
result Predicted covariance matrices are symmetric positive definite.
KIPLMC methods improve statistical inference in latent variable models.
problem Statistical inference in latent variable models.
method Joint diffusion process in parameter and latent variable spaces, with two explicit discretizations.
result KIPLMC methods achieve accelerated convergence rates in Wasserstein-2 distance.
Concave elliptic operators yield concave functions on cohomology.
problem Understanding concave functions on cohomology.
method General construction of concave elliptic operators.
result Generalized Khovanskii-Teissier inequalities.
The paper establishes conditions for strict power concavity in convolutions.
problem Conditions for strict power concavity in convolutions.
method Analyzes sufficient conditions for strict parabolic power concavity of convolutions.
result Establishes sufficient conditions for strict power concavity of convolutions.
Modified Vanna-Volga method constructs Normal volatility smiles.
problem No method existed for constructing Normal volatility smiles.
method Modified Vanna-Volga method applied to Normal volatilities.
result The Vanna-Volga method can easily fit both convex and concave smiles.
Proves convergence of PSGLA for sampling non-convex potentials.
problem Sampling from non-convex potentials with stability.
method Combines ULA and proximal optimization with stability analysis.
result First proof of convergence for PSGLA on non-convex potentials.
Minimal graph level sets are concave if boundary is concave.
problem Understanding curvature of minimal graph level sets.
method Proved an inequality and showed geometric properties.
result Level sets of minimal graphs are concave if boundary is concave.
New method identifies subgroups in censored data.
problem Identifying meaningful patterns in heterogeneous populations.
method Combining inverse probability weighting, M-estimation, and concave pairwise fusion penalization.
result Robust approach for censored data under heterogeneous AFT models.
The paper extends a Harnack inequality to noncompact evolving hypersurfaces.
problem Proving a Harnack inequality for noncompact evolving hypersurfaces.
method Using a differential Harnack inequality for noncompact convex hypersurfaces flowing with normal speed based on their principal curvatures.
result The extension of Andrews' result to noncompact hypersurfaces.
Proves log-concavity of cluster algebra coefficients for type An.
problem Log-concavity of cluster algebra coefficients.
method Introduced atomic theta basis and proved log-concavity for type An. result Proved log-concavity of coefficients for cluster algebra variables of type An. This paper studies GAIL's global convergence for general MDP and nonlinear rewards.
problem Understanding when GAIL algorithms achieve global convergence for general MDP and nonlinear rewards.
method Characterization of global convergence for various policy gradient algorithms applied to GAIL.
result First systematic theoretical study of GAIL for global convergence.
The paper studies sparsity in EBF with hyperpriors and proposes a PALM algorithm.
problem Promoting sparsity in sparse learning problems.
method Empirical Bayes framework, hyperpriors, proximal alternating linearized minimization (PALM) algorithm.
result Appropriate hyperpriors can significantly enhance sparsity and restoration accuracy.
Study improves sampling from non-log-concave distributions using Fisher information.
problem Sampling from non-log-concave distributions with high Fisher information guarantees.
method Proximal sampler with RGO implementation, leveraging log-concave sampling results.
result Improved complexity guarantee in relative Fisher information for non-log-concave sampling.
The paper studies price impacts in asset liquidation markets.
problem Understanding price impacts in asset liquidation markets.
method Equilibrium formulation and analysis of price impacts.
result Existence and uniqueness of clearing prices for portfolio liquidation.
Established concavity principle for curved spaces.
problem Solving equations on curved spaces with nonnegative curvature.
method Applied concavity principle to elliptic and parabolic equations on locally symmetric spaces with nonnegative curvature.
result First general concavity principle on spaces with non-constant sectional curvature.
Establishes log-concavity estimates for convex domains' first Dirichlet eigenfunctions.
problem Quantifying the Hessian of log-concave eigenfunctions on convex domains.
method Analyzes log-concavity properties of the first Dirichlet eigenfunction on convex domains.
result Obtains quantitative estimates for the Hessian of logu. New saddle network architectures preserve convex-concave geometry in optimization problems.
problem Optimization models with convex x and concave y components.
method Structured separable decomposition and saddle network architectures.
result Proven one-dimensional approximation theorem and high accuracy on various test functions.
Investigates concavity of spacetimes, showing conditions for local concavity.
problem Understanding the concavity of spacetimes in Finsler geometry.
method Analyzes flag curvature and future capsules to characterize concavity.
result Berwald spacetimes are locally concave if and only if their flag curvature is nonnegative in timelike directions.
Heat flow fails to preserve concavity in curved spaces.
problem Non-preservation of concavity properties in curved spaces.
method Analysis of Dirichlet heat flow on Riemannian manifolds.
result No concavity properties are preserved unless curvature is zero.
Solves imaging inverse problems using autoencoding priors.
problem Ill-posed inverse problems in imaging.
method Joint Posterior Maximization with Autoencoding Prior (JPMAP).
result JPMAP converges to a stationary point and provides robust solutions.