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Found 12 papers, 2 papers with code
2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs
We provide a framework to approximate the 2-Wasserstein distance and the optimal transport map, amenable to efficient training as well as statistical and geometric analysis.
Object Type Clustering using Markov Directly-Follow Multigraph in Object-Centric Process Mining
This graph is used to calculate a similarity matrix for discovering clusters of similar case notions based on a threshold.
Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes
In such a scenario, where covariates are highly interdependent and partially missing, new theoretical challenges arise.
Subspace Clustering with Missing and Corrupted Data
Given full or partial information about a collection of points that lie close to a union of several subspaces, subspace clustering refers to the process of clustering the points according to their subspace and identifying the subspaces.
Variational Gram Functions: Convex Analysis and Optimization
We propose a new class of convex penalty functions, called \emph{variational Gram functions} (VGFs), that can promote pairwise relations, such as orthogonality, among a set of vectors in a vector space.
Relative Density and Exact Recovery in Heterogeneous Stochastic Block Models
For instance, $\log n$ is considered to be the standard lower bound on the cluster size for exact recovery via convex methods, for homogenous SBM.
Subspace Clustering via Tangent Cones
Given samples lying on any of a number of subspaces, subspace clustering is the task of grouping the samples based on the their corresponding subspaces.
Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models
The Stochastic Block Model (SBM) is a widely used random graph model for networks with communities.
New Computational and Statistical Aspects of Regularized Regression with Application to Rare Feature Selection and Aggregation
Prior knowledge on properties of a target model often come as discrete or combinatorial descriptions.
Persistent Reductions in Regularized Loss Minimization for Variable Selection
In the context of regularized loss minimization with polyhedral gauges, we show that for a broad class of loss functions (possibly non-smooth and non-convex) and under a simple geometric condition on the input data it is possible to efficiently identify a subset of features which are guaranteed to have zero coefficients in all optimal solutions in all problems with loss functions from said class, before any iterative optimization has been performed for the original problem.
Evaluating Perceived Usefulness and Ease of Use of CMMN and DCR
The study is performed by educating master level students with these languages over eight weeks by giving feedback on their assignments to reduce perceptions biases.
Adversarial Lagrangian Integrated Contrastive Embedding for Limited Size Datasets
First, the accuracy improvement and training convergence of the proposed pre-trained adversarial transfer are shown on various subsets of datasets with few samples.
