Search Results for author:
Found 5 papers, 0 papers with code
Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures
This is a corollary of the major contribution of the current paper: the first sample complexity upper bound for the problem (introduced in \cite{BK20}) of learning the vertices of a Latent $k-$ Polytope in ${\bf R}^d$, given perturbed points from it.
Random Separating Hyperplane Theorem and Learning Polytopes
Our first result, Random Separating Hyperplane Theorem (RSH), is a strengthening of this for polytopes.
Algorithms for finding $k$ in $k$-means
Two challenges are open: (i) Is there a data-determined definition of $k$ which is provably correct and (ii) Is there a polynomial time algorithm to find $k$ from data ?
Finding a latent k-simplex in O(k . nnz(data)) time via Subset Smoothing
In this paper we show that a large class of Latent variable models, such as Mixed Membership Stochastic Block(MMSB) Models, Topic Models, and Adversarial Clustering, can be unified through a geometric perspective, replacing model specific assumptions and algorithms for individual models.
A provable SVD-based algorithm for learning topics in dominant admixture corpus
Our aim is to develop a model which makes intuitive and empirically supported assumptions and to design an algorithm with natural, simple components such as SVD, which provably solves the inference problem for the model with bounded $l_1$ error.
