1 code implementation • 19 Feb 2019 • Amirhossein Taghvaei, Amin Jalali
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.
1 code implementation • 22 Jun 2022 • Amin Jalali
This graph is used to calculate a similarity matrix for discovering clusters of similar case notions based on a threshold.
no code implementations • 26 Feb 2018 • Amin Jalali, Rebecca Willett
In such a scenario, where covariates are highly interdependent and partially missing, new theoretical challenges arise.
no code implementations • 8 Jul 2017 • Zachary Charles, Amin Jalali, Rebecca Willett
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.
no code implementations • 16 Jul 2015 • Amin Jalali, Maryam Fazel, Lin Xiao
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.
no code implementations • 15 Dec 2015 • Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel
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.
no code implementations • NeurIPS 2017 • Amin Jalali, Rebecca Willett
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.
no code implementations • NeurIPS 2016 • Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel
The Stochastic Block Model (SBM) is a widely used random graph model for networks with communities.
no code implementations • 10 Apr 2019 • Amin Jalali, Adel Javanmard, Maryam Fazel
Prior knowledge on properties of a target model often come as discrete or combinatorial descriptions.
no code implementations • 30 Nov 2020 • Amin Jalali
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.
no code implementations • 20 Mar 2021 • Amin Jalali
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.
no code implementations • 6 Oct 2022 • Amin Jalali, Minho Lee
First, the accuracy improvement and training convergence of the proposed pre-trained adversarial transfer are shown on various subsets of datasets with few samples.