arXiv research

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

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…

A wide class of regularization problems in machine learning and statistics employ a regularization term which is obtained by composing a simple convex function ωwith a linear transformation. This setting includes Group Lasso methods, the Fused Lasso and other total variation methods, multi-task learning methods and man…

Framework optimizes expensive manufacturing processes efficiently.

NON model improves tabular data classification accuracy.

Fused Encoder Networks improve momentum strategies on crypto data.

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.

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 $S_n<UVB_n$.

Introduce Collapsed Effective Operators for higher-order structures.

Forbidden moves categorify fused links into quivers.

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

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…

FUSE improves verification quality without ground truth labels.

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

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, …

Fuses posterior distributions from different datasets using KL divergence.

MASnet enhances speech on mobile devices with low latency.

We consider the group of unrestricted virtual braids, describe its structure and explore its relations with fused links. Also, we define the groups of flat virtual braids and virtual Gauss braids and study some of their properties, in particular their linearity.

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

RFX-Fuse combines Breiman and Cutler's Random Forest with modern ML capabilities.

In the present paper, we consider local moves on classical and welded diagrams: (self-)crossing change, (self-)virtualization, virtual conjugation, Delta, fused, band-pass and welded band-pass moves. Interrelationship between these moves is discussed and, for each of these move, we provide an algebraic classification. …

New autoencoder improves latent space learning by optimizing sliced Gromov-Wasserstein discrepancies.

A framework assesses the quality of crowdsourced weather data.

Multi-task learning has shown to significantly enhance the performance of multiple related learning tasks in a variety of situations. We present the fused logistic regression, a sparse multi-task learning approach for binary classification. Specifically, we introduce sparsity inducing penalties over parameter differenc…

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

New method estimates mixture model components efficiently.

CAF-HFCM automatically forms a cluster hierarchy and optimizes the number of clusters without trial-and-validation.

Bayesian model fuses diverse microbiome data types.

Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability …

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The …

During the past years there has been an explosion of interest in learning methods based on sparsity regularization. In this paper, we discuss a general class of such methods, in which the regularizer can be expressed as the composition of a convex function $ω$ with a linear function. This setting includes several metho…

MIOpen optimizes deep learning operators for GPUs, accelerating research.

In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for composite functions. Our approach exploits closed-form solutions of proximal operators and envelope representations based on the Moreau, Fo…

Incorporation of a new knowledge into neural networks with simultaneous preservation of the previous one is known to be a nontrivial problem. This problem becomes even more complex when new knowledge is contained not in new training examples, but inside the parameters (connection weights) of another neural network. Her…

DoRA improves adaptation efficiency for large models by factoring norms and fusing kernels.

Unified parallel ADMM for high-dimensional regression with combined regularizations.

M2VN forecasts financial volatility by fusing time series data with news embeddings.

We present the group fused Lasso for detection of multiple change-points shared by a set of co-occurring one-dimensional signals. Change-points are detected by approximating the original signals with a constraint on the multidimensional total variation, leading to piecewise-constant approximations. Fast algorithms are …

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

Enhances graph comparison by incorporating edge features using Fused Gromov-Wasserstein distance.

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…

ESE-FN improves elderly activity recognition accuracy.

ABM automates feature engineering and variable selection for loss-based models.

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

We study \emph{TV regularization}, a widely used technique for eliciting structured sparsity. In particular, we propose efficient algorithms for computing prox-operators for $\ell_p$-norm TV. The most important among these is $\ell_1$-norm TV, for whose prox-operator we present a new geometric analysis which unveils a …