11 citations · 19 across the 12 of their papers we have counts for
14 papers
DE-HNN: An effective neural model for Circuit Netlist representation
Zhishang Luo, Truong Son Hy, Puoya Tabaghi +7
The run-time for optimization tools used in chip design has grown with the complexity of designs to the point where it can take several days to go through one design cycle which ha…
Universal Representation of Permutation-Invariant Functions on Vectors and Tensors
Puoya Tabaghi, Yusu Wang
A main object of our study is multiset functions -- that is, permutation-invariant functions over inputs of varying sizes. Deep Sets, proposed by \cite{zaheer2017deep}, provides a…
Principal Component Analysis in Space Forms
Puoya Tabaghi, Michael Khanzadeh, Yusu Wang +1
Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical a…
Learning Ultrametric Trees for Optimal Transport Regression
Samantha Chen, Puoya Tabaghi, Yusu Wang
Optimal transport provides a metric which quantifies the dissimilarity between probability measures. For measures supported in discrete metric spaces, finding the optimal transport…
HyperAid: Denoising in hyperbolic spaces for tree-fitting and hierarchical clustering
Eli Chien, Puoya Tabaghi, Olgica Milenkovic
The problem of fitting distances by tree-metrics has received significant attention in the theoretical computer science and machine learning communities alike, due to many applicat…
Provably Accurate and Scalable Linear Classifiers in Hyperbolic Spaces
Chao Pan, Eli Chien, Puoya Tabaghi +2
Many high-dimensional practical data sets have hierarchical structures induced by graphs or time series. Such data sets are hard to process in Euclidean spaces and one often seeks…
Highly Scalable and Provably Accurate Classification in Poincare Balls
Eli Chien, Chao Pan, Puoya Tabaghi +1
Many high-dimensional and large-volume data sets of practical relevance have hierarchical structures induced by trees, graphs or time series. Such data sets are hard to process in…
On Procrustes Analysis in Hyperbolic Space
Puoya Tabaghi, Ivan Dokmanic
Congruent Procrustes analysis aims to find the best matching between two point sets through rotation, reflection and translation. We formulate the Procrustes problem for hyperbolic…
Linear Classifiers in Product Space Forms
Puoya Tabaghi, Chao Pan, Eli Chien +2
Embedding methods for product spaces are powerful techniques for low-distortion and low-dimensional representation of complex data structures. Here, we address the new problem of l…
Geometry of Similarity Comparisons
Puoya Tabaghi, Jianhao Peng, Olgica Milenkovic +1
Many data analysis problems can be cast as distance geometry problems in \emph{space forms} -- Euclidean, spherical, or hyperbolic spaces. Often, absolute distance measurements are…
Hyperbolic Distance Matrices
Puoya Tabaghi, Ivan Dokmanić
Hyperbolic space is a natural setting for mining and visualizing data with hierarchical structure. In order to compute a hyperbolic embedding from comparison or similarity informat…
Real Polynomial Gram Matrices Without Real Spectral Factors
Puoya Tabaghi, Ivan Dokmanić
It is well known that a non-negative definite polynomial matrix (a polynomial Gramian) can be written as a product of its polynomial spectral factors, .…
Learning Schatten--von Neumann Operators
Puoya Tabaghi, Maarten de Hoop, Ivan Dokmanić
We study the learnability of a class of compact operators known as Schatten--von Neumann operators. These operators between infinite-dimensional function spaces play a central role…
Kinetic Euclidean Distance Matrices
Puoya Tabaghi, Ivan Dokmanić, Martin Vetterli
Euclidean distance matrices (EDMs) are a major tool for localization from distances, with applications ranging from protein structure determination to global positioning and manifo…