arXiv research

New method for clustering tasks with heterogeneous data.

Bayesian and POD methods fuse noisy wind tunnel and simulated aerodynamic data.

The fused lasso is analyzed for high-dimensional piecewise-constant regression coefficients.

The paper develops estimators for variance in graph structures using fused lasso.

We study the property of the Fused Lasso Signal Approximator (FLSA) for estimating a blocky signal sequence with additive noise. We transform the FLSA to an ordinary Lasso problem. By studying the property of the design matrix in the transformed Lasso problem, we find that the irrepresentable condition might not hold, …

New method estimates mixture model components efficiently.

FUSE neural centrality framework improves data point measurement in high dimensions.

Proposes a method to align Hawkes processes across different event spaces.

We study regularized estimation in high-dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an…

DONUT improves treatment effect estimation by enforcing orthogonality constraints.

A new estimator reduces bias and improves efficiency for staggered adoption studies.

Proposes a method to improve regression model performance with limited target data using fused-regularizer.

We propose a new algorithm for solving the graph-fused lasso (GFL), a method for parameter estimation that operates under the assumption that the signal tends to be locally constant over a predefined graph structure. Our key insight is to decompose the graph into a set of trails which can then each be solved efficientl…

We consider the problem of predicting an outcome variable using $p$ covariates that are measured on $n$ independent observations, in the setting in which flexible and interpretable fits are desirable. We propose the fused lasso additive model (FLAM), in which each additive function is estimated to be piecewise constant…

This paper converts ADMM to proximal gradient for efficient sparse estimation.

The paper studies surfaces in a bounded domain with orthogonal boundaries and proves curvature estimates.

Novel prior for orthogonal functions improves functional component estimation.

Proposes integrating global and local entropy for more reliable LLMs.

We propose orthogonality as a necessary condition for disentangling aleatoric and epistemic uncertainty.

The fused lasso penalizes a loss function by the $L_1$ norm for both the regression coefficients and their successive differences to encourage sparsity of both. In this paper, we propose a Bayesian generalized fused lasso modeling based on a normal-exponential-gamma (NEG) prior distribution. The NEG prior is assumed in…

The paper studies algebraic structures related to quantum groups.

The solution path of the 1D fused lasso for an $n$-dimensional input is piecewise linear with $\mathcal{O}(n)$ segments (Hoefling et al. 2010 and Tibshirani et al 2011). However, existing proofs of this bound do not hold for the weighted fused lasso. At the same time, results for the generalized lasso, of which the wei…

All knots are fused isotopic to the unknot via a process known as virtualization. We extend and adapt this process to show that, up to fused isotopy, classical links are classified by their linking numbers.

New LT-O-learners improve HLTE estimation with low overlap.

We provide a comparative study of several widely used off-policy estimators (Empirical Average, Basic Importance Sampling and Normalized Importance Sampling), detailing the different regimes where they are individually suboptimal. We then exhibit properties optimal estimators should possess. In the case where examples …

Proposes an efficient lower bound for Gromov-Wasserstein discrepancy.

Method estimates heterogeneous causal effects on networks using orthogonal learning.

Study optimizes estimation of orthogonal and rotation matrices from noisy data.

In this paper we analyze the asymptotic properties of l1 penalized maximum likelihood estimation of signals with piece-wise constant mean values and/or variances. The focus is on segmentation of a non-stationary time series with respect to changes in these model parameters. This change point detection and estimation pr…

New framework models complex spatial data with basis functions and graphical vectors.

Double machine learning provides $\sqrt{n}$-consistent estimates of parameters of interest even when high-dimensional or nonparametric nuisance parameters are estimated at an $n^{-1/4}$ rate. The key is to employ Neyman-orthogonal moment equations which are first-order insensitive to perturbations in the nuisance param…

We construct the complete invariant for fused links. It is proved that the set of equivalence classes of $n$-component fused links is in one-to-one correspondence with the set of elements of the abelization $UVP_n/UVP_n^{\prime}$ up to conjugation by the elements from the symmetric group $S_n<UVB_n$.

Corrected whitening restores orthogonality in high-dimensional spherical Gaussian mixtures.

Paper introduces a novel framework for supervised graph prediction using Optimal Transport.

New method estimates and optimizes policy differences using orthogonal learning.

Optimal spectral estimators and AMP combine for efficient weak recovery in orthogonally invariant GLMs.

Forbidden moves categorify fused links into quivers.

Paper improves feature selection accuracy using transfer learning.

Random orthogonalization improves FL in massive MIMO systems without CSI.

We consider the problem of jointly estimating multiple inverse covariance matrices from high-dimensional data consisting of distinct classes. An $\ell_2$-penalized maximum likelihood approach is employed. The suggested approach is flexible and generic, incorporating several other $\ell_2$-penalized estimators as specia…

The Lasso is a very well known penalized regression model, which adds an $L_{1}$ penalty with parameter $λ_{1}$ on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an $L_{1}$ penalty with parameter $λ_{2}$ on the difference of neighboring coefficients, assuming the…

LEGO estimates tangent spaces more robustly than LPCA in noisy data.

FUSE improves verification quality without ground truth labels.

In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar structures. A motivating example is the analysis of brain networks of Alzheimer's disease using neuroimaging data. Specifically, we may wish to e…

Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie. Such common practice may cause significant information …

Four constructions of constant mean curvature (CMC) hypersurfaces in the (n+1)-sphere are given, which should be considered analogues of `classical' constructions that are possible for CMC hypersurfaces in Euclidean space. First, Delaunay-like hypersurfaces, consisting roughly of a chain of hyperspheres winding multipl…

This short paper describes our solution to the 2018 IEEE World Congress on Computational Intelligence One-Minute Gradual-Emotional Behavior Challenge, whose goal was to estimate continuous arousal and valence values from short videos. We designed four base regression models using visual and audio features, and then use…

Proposes an MTL method with clustering to improve regression accuracy.