arXiv research

Two-dimensional Riemannian manifolds uniquely determined by boundary data.

Unique continuation for X-ray transforms of one-forms with partial data.

This work characterizes reward function partial identifiability and its impact on policy optimization.

Partial dependence curves (FPD) introduced by Friedman, are an important model interpretation tool, but are often not accessible to business analysts and scientists who typically lack the skills to choose, tune, and assess machine learning models. It is also common for the same partial dependence algorithm on the same …

Reconstructing a planar domain from its Dirichlet-to-Neumann data

Paper explores VRM for PSMLC with partially labeled medical images.

We solve the partial data Calderón problem for the connection Laplacian on Riemann surfaces.

Proposes a method to create predictive sets from partially labeled data.

In this paper, we prove that there exists a dimensional constant $δ> 0$ such that given any background Kähler metric $ω$, the Calabi flow with initial data $u_0$ satisfying \begin{equation*} \partial \bar \partial u_0 \in L^\infty (M) \text{ and } (1- δ)ω< ω_{u_0} < (1+δ)ω, \end{equation*} admits a unique short time so…

We present a simple, flexible, and general framework titled Partial Registration Network (PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed learning-based methods for registration, we use deep networks to tackle non-convexity of the alignment and partial correspondence problems. Whi…

New bounds for PDA using partial optimal transport improve domain alignment.

CPPO learns policies from partial offline data in MDPs with structural assumptions.

Ranked data appear in many different applications, including voting and consumer surveys. There often exhibits a situation in which data are partially ranked. Partially ranked data is thought of as missing data. This paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing…

The paper presents a framework for estimating treatment effects using partial network data.

Develops GNNs for incomplete graphs, improving learning from missing node attributes.

Differentiable relaxation for inferring partial orders from noisy linear data.

New method learns from non-uniform data and partial physical knowledge.

Study expands multiclass classification models with new rates and partial concept classes.

In this paper we consider the lens rigidity problem with partial data for conformal metrics in the presence of a magnetic field on a compact manifold of dimension $\geq 3$ with boundary. We show that one can uniquely determine the conformal factor and the magnetic field near a strictly convex (with respect to the magne…

New method for partial matching of shapes with Varifolds.

Proposes MGPLL for PL learning with non-random noise.

A new deep metric learning method for defect classification in threaded pipe connections.

Study uses deep neural networks for inference in partially linear models with dependent data.

Meta-learners estimate CATE from multiple environments with partial identification.

Hierarchical Partial-Order Models for Ranking

We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this two part treatise, we present our developments in the context of solving two main classes …

A new screening method for high-dimensional data reduces computational cost.

FDNet learns PDEs from data with fast predictions.

Method estimates sparse inverse covariance and partial correlation matrices efficiently.

CONCERT improves transfer learning by borrowing partial information from auxiliary datasets.

Extends PAC learning theory to handle partial concepts with special properties.

EnSF uses image inpainting to handle partial observations in data assimilation.

In this paper, we propose a mixture of probabilistic partial canonical correlation analysis (MPPCCA) that extracts the Causal Patterns from two multivariate time series. Causal patterns refer to the signal patterns within interactions of two elements having multiple types of mutually causal relationships, rather than a…

Unified approach for predicting missing segments in partially observed functions.

Proposes a topological model for partial equivariance in neural networks.

Model handles missing data in partial blackouts for multivariate time series.

Develops a regression model for partially observed dynamic tensor data.

FedAMD framework improves federated learning with partial client participation.

For a Riemannian manifold $(M,g)$ with strictly convex boundary $\partial M$, the lens data consists in the set of lengths of geodesics $γ$ with endpoints on $\partial M$, together with their endpoints $(x_-,x_+)\in \partial M\times \partial M$ and tangent exit vectors $(v_-,v_+)\in T_{x_-} M\times T_{x_+} M$. We show …

Study methods to recover unknown processes in PDEs from data.

Paper improves conformal prediction for imprecise training data.

New statistical models for predicting ranked preferences from partial orders.

Partially performative prediction studies how predictive models influence future data.

AUCRSS detects change points in partially observed multivariate autocorrelated data.

A new approach uses partial likelihood to improve tree-based density estimation and inference.

A novel transformer model improves classification of partially ordered sequences.

Proposes a new GAN architecture for generating data conditioned on partial information.

A reject option improves partial-label learning's accuracy.