Search Results for author:
Found 13 papers, 3 papers with code
Approximately Optimal Core Shapes for Tensor Decompositions
This work studies the combinatorial optimization problem of finding an optimal core tensor shape, also called multilinear rank, for a size-constrained Tucker decomposition.
Subquadratic Kronecker Regression with Applications to Tensor Decomposition
By extending our approach to block-design matrices where one block is a Kronecker product, we also achieve subquadratic-time algorithms for (1) Kronecker ridge regression and (2) updating the factor matrices of a Tucker decomposition in ALS, which is not a pure Kronecker regression problem, thereby improving the running time of all steps of Tucker ALS.
Constant-Factor Approximation Algorithms for Socially Fair $k$-Clustering
We study approximation algorithms for the socially fair $(\ell_p, k)$-clustering problem with $m$ groups, whose special cases include the socially fair $k$-median ($p=1$) and socially fair $k$-means ($p=2$) problems.
Fast Low-Rank Tensor Decomposition by Ridge Leverage Score Sampling
Low-rank tensor decomposition generalizes low-rank matrix approximation and is a powerful technique for discovering low-dimensional structure in high-dimensional data.
A Parameterized Family of Meta-Submodular Functions
Submodular function maximization has found a wealth of new applications in machine learning models during the past years.
Socially Fair k-Means Clustering
We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (e. g., demographic groups).
Amortized Rejection Sampling in Universal Probabilistic Programming
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance.
Efficient Bayesian Inference for Nested Simulators
We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i. e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.
Beyond Submodular Maximization via One-Sided Smoothness
Using the multilinear framework and new matroid rounding techniques for quadratic objectives, we give an $\Omega(1/\sigma^{3/2})$-approximation for maximizing a $\sigma$-semi-metric diversity function subject to matroid constraint.
Data Structures and Algorithms Computational Geometry Discrete Mathematics
Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets
In this paper, we consider this problem as an optimization problem that seeks to maximize the sum of a sum-sum diversity function and a non-negative monotone submodular function.
Linear Relaxations for Finding Diverse Elements in Metric Spaces
We first study the approximation quality of the algorithm by comparing with the LP objective.
Active Distance-Based Clustering using K-medoids
k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses pairwise distances of data points and tries to directly decompose the dataset with $n$ points into a set of $k$ disjoint clusters.
Max-Sum Diversification, Monotone Submodular Functions and Semi-metric Spaces
In many applications such as web-based search, document summarization, facility location and other applications, the results are preferable to be both representative and diversified subsets of documents.
