Nadiia Chepurko
YOU?
Author Swipe
View article: Learning Program Representations for Food Images and Cooking Recipes
Learning Program Representations for Food Images and Cooking Recipes Open
In this paper, we are interested in modeling a how-to instructional procedure, such as a cooking recipe, with a meaningful and rich high-level representation. Specifically, we propose to represent cooking recipes and food images as cooking…
View article: Near-Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time
Near-Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time Open
In the numerical linear algebra community, it was suggested that to obtain nearly optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns (a basis), regression, or low-rank app…
View article: Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra Open
We create classical (non-quantum) dynamic data structures supporting queries for recommender systems and least-squares regression that are comparable to their quantum analogues. De-quantizing such algorithms has received a flurry of attent…
View article: Testing Positive Semi-Definiteness via Random Submatrices
Testing Positive Semi-Definiteness via Random Submatrices Open
We study the problem of testing whether a matrix $\mathbf{A} \in \mathbb{R}^{n \times n}$ with bounded entries ($\|\mathbf{A}\|_\infty \leq 1$) is positive semi-definite (PSD), or $ε$-far in Euclidean distance from the PSD cone, meaning th…
View article: Robust and Sample Optimal Algorithms for PSD Low Rank Approximation
Robust and Sample Optimal Algorithms for PSD Low Rank Approximation Open
Recently, Musco and Woodruff (FOCS, 2017) showed that given an $n \times n$ positive semidefinite (PSD) matrix $A$, it is possible to compute a $(1+ε)$-approximate relative-error low-rank approximation to $A$ by querying $O(nk/ε^{2.5})$ en…
View article: ARDA: Automatic Relational Data Augmentation for Machine Learning
ARDA: Automatic Relational Data Augmentation for Machine Learning Open
Automatic machine learning (\AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more accessible. While many recent works have focused on aspect…
View article: Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams.
Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams. Open
We study the Maximum Independent Set problem for geometric objects given in the data stream model. A set of geometric objects is said to be independent if the objects are pairwise disjoint. We consider geometric objects in one and two dime…
View article: Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams
Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams Open
We study the Maximum Independent Set problem for geometric objects given in the data stream model. A set of geometric objects is said to be independent if the objects are pairwise disjoint. We consider geometric objects in one and two dime…
View article: Polynomial Time Algorithm for $2$-Stable Clustering Instances
Polynomial Time Algorithm for $2$-Stable Clustering Instances Open
Clustering with most objective functions is NP-Hard, even to approximate well in the worst case. Recently, there has been work on exploring different notions of stability which lend structure to the problem. The notion of stability, $α$-pe…