arXiv research

Paper presents a robust transfer learning method for active level set estimation.

The clusters of a distribution are often defined by the connected components of a density level set. However, this definition depends on the user-specified level. We address this issue by proposing a simple, generic algorithm, which uses an almost arbitrary level set estimator to estimate the smallest level at which th…

Estimating the level set of a signal from measurements is a task that arises in a variety of fields, including medical imaging, astronomy, and digital elevation mapping. Motivated by scenarios where accurate and complete measurements of the signal may not available, we examine here a simple procedure for estimating the…

New algorithms estimate function levels with near-optimal efficiency.

This paper introduces a more efficient method for estimating level sets with a stopping criterion.

We analyze the level sets of the norm of the Witten spinor in an asymptotically flat Riemannian spin manifold of positive scalar curvature. Level sets of small area are constructed. We prove curvature estimates which quantify that, if the total mass becomes small, the manifold becomes flat with the exception of a set o…

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

A new method optimizes spatial sampling for level set estimation in one dimension.

Bayesian Neural Networks improve high-dimensional level set estimation.

The level set tree approach of Hartigan (1975) provides a probabilistically based and highly interpretable encoding of the clustering behavior of a dataset. By representing the hierarchy of data modes as a dendrogram of the level sets of a density estimator, this approach offers many advantages for exploratory analysis…

We prove two new estimates for the level set flow of mean convex domains in Riemannian manifolds. Our estimates give control - exponential in time - for the infimum of the mean curvature, and the ratio between the norm of the second fundamental form and the mean curvature. In particular, the estimates remove a stumblin…

In this paper we establish a uniform $C^{2,θ}$ estimate for level sets of stable solutions to the singularly perturbed Allen-Cahn equation in dimensions $n\leq 10$ (which is optimal). The proof combines two ingredients: one is the infinite dimensional reduction method which enables us to reduce the $C^{2,θ}$ estimate …

In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is considered. A new algorithm for this problem in the Bayesian framework with a Gaussian Process (GP) prior is proposed. The proposed algorithm employs a hierarchical sequence of partitions to exp…

Proposes methods for online conformal prediction with nested prediction sets across multiple confidence levels.

We study the connections between spectral clustering and the problems of maximum margin clustering, and estimation of the components of level sets of a density function. Specifically, we obtain bounds on the eigenvectors of graph Laplacian matrices in terms of the between cluster separation, and within cluster connecti…

A novel dose-finding design for cancer clinical trials using level set estimation.

Develops privacy-preserving methods for longitudinal linear regression.

Adaptive batching improves Gaussian process surrogates for noisy level set estimation.

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.

An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and employ annotation schemes with limit…

Estimates population mean from user-level data with privacy, accounting for heterogeneity.

Improves model classification accuracy in black-box settings.

High density clusters can be characterized by the connected components of a level set $L(λ) = \{x:\ p(x)>λ\}$ of the underlying probability density function $p$ generating the data, at some appropriate level $λ\geq 0$. The complete hierarchical clustering can be characterized by a cluster tree ${\cal T}= \bigcup_λ L(λ)…

We show that DBSCAN can estimate the connected components of the $λ$-density level set $\{ x : f(x) \ge λ\}$ given $n$ i.i.d. samples from an unknown density $f$. We characterize the regularity of the level set boundaries using parameter $β> 0$ and analyze the estimation error under the Hausdorff metric. When the data …

Sharp gradient estimate for scalar curvature on 3-manifolds.

New scoring rules for multivariate distributions and level sets.

We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashion. The algorithm greedily shrinks a sum of truncated variances within a set of potential maximizers (BO) or unclassified points (LSE), which…

Study functional confounders in causal inference, enabling estimable effects.

We propose a deep learning strategy to estimate the mean curvature of two-dimensional implicit interfaces in the level-set method. Our approach is based on fitting feed-forward neural networks to synthetic data sets constructed from circular interfaces immersed in uniform grids of various resolutions. These multilayer …

New acquisition functions improve Bernoulli LSE.

SLS optimizes minimum-volume regions for conditional quantiles, bypassing density estimation.

We consider the problem of learning the level set for which a noisy black-box function exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian process (GP) metamodels. Our focus is on strongly stochastic samplers, in particular with heavy-tailed simulation noise and low signal-to-no…

New algorithms solve complex multi-level optimization problems with improved efficiency.

The paper optimizes training samples for image denoising across different noise levels.

In most classification tasks there are observations that are ambiguous and therefore difficult to correctly label. Set-valued classifiers output sets of plausible labels rather than a single label, thereby giving a more appropriate and informative treatment to the labeling of ambiguous instances. We introduce a framewo…

New method uses Winsorized mean estimators for privacy-preserving statistics on dependent data.

New method improves level set estimation with theoretical guarantees.

New method uses neural networks to estimate parameters without needing detector simulations.

Proposes a method to improve hierarchical clustering using set-level structural priors.

Sharp estimate for 2-systole on Kähler surfaces with positive scalar curvature.

New bounds for LDP with heterogeneous privacy levels guaranteeing high probability of accuracy.

We establish a geometric lower bound for the principal curvature of the level surfaces of solutions to $F(D^2u, Du, u, x)=0$ in convex ring domains, under a refined structural condition introduced by Bianchini-Longinetti-Salani in \cite{BLS}. We also prove a constant rank theorem for the second fundamental form of the …

In increasingly many settings, data sets consist of multiple samples from a population of networks, with vertices aligned across these networks. For example, brain connectivity networks in neuroscience consist of measures of interaction between brain regions that have been aligned to a common template. We consider the …

The paper tackles optimal level set estimation in crowdsourcing and tournaments.

The paper studies volume and area comparisons in non-compact 3-manifolds with non-negative scalar curvature.

Following Hartigan, a cluster is defined as a connected component of the t-level set of the underlying density, i.e., the set of points for which the density is greater than t. A clustering algorithm which combines a density estimate with spectral clustering techniques is proposed. Our algorithm is composed of two step…

VarFA efficiently estimates student skill levels with uncertainty for adaptive testing.

This paper compares two loss functions for learning from aggregated responses and introduces an interpolating estimator.