Search Results - "Amini, Arash A."

ON SEMIDEFINITE RELAXATIONS FOR THE BLOCK MODEL by Amini, Arash A., Levina, Elizaveta

“…The stochastic block model (SBM) is a popular tool for community detection in networks, but fitting it by maximum likelihood (MLE) involves a computationally…”
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Compressibility of Deterministic and Random Infinite Sequences by Amini, A., Unser, M., Marvasti, F.

“…We introduce a definition of the notion of compressibility for infinite deterministic and i.i.d. random sequences which is based on the asymptotic behavior of…”
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Federated Learning of Generalized Linear Causal Networks by Ye, Qiaoling, Amini, Arash A., Zhou, Qing

“…Causal discovery, the inference of causal relations among variables from data, is a fundamental problem of science. Nowadays, due to an increased awareness of…”
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PSEUDO-LIKELIHOOD METHODS FOR COMMUNITY DETECTION IN LARGE SPARSE NETWORKS by Amini, Arash A., Chen, Aiyou, Bickel, Peter J., Levina, Elizaveta

“…Many algorithms have been proposed for fitting network models with communities, but most of them do not scale well to large networks, and often fail on sparse…”
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Adjusted chi-square test for degree-corrected block models by Zhang, Linfan, Amini, Arash A.

“…We propose a goodness-of-fit test for degree-corrected stochastic block models (DCSBM). The test is based on an adjusted chi-square statistic for measuring…”
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Optimizing Regularized Cholesky Score for Order-Based Learning of Bayesian Networks by Ye, Qiaoling, Amini, Arash A., Zhou, Qing

“…Bayesian networks are a class of popular graphical models that encode causal and conditional independence relations among variables by directed acyclic graphs…”
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Concentration of kernel matrices with application to kernel spectral clustering by Amini, Arash A., Razaee, Zahra S.

“…We study the concentration of random kernel matrices around their mean. We derive nonasymptotic exponential concentration inequalities for Lipschitz kernels…”
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High-Dimensional Analysis of Semidefinite Relaxations for Sparse Principal Components by Amini, Arash A., Wainwright, Martin J.

“…Principal component analysis (PCA) is a classical method for dimensionality reduction based on extracting the dominant eigenvectors of the sample covariance…”
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On the properties of the toxicity index and its statistical efficiency by Razaee, Zahra S., Amini, Arash A., Diniz, Márcio A., Tighiouart, Mourad, Yothers, Greg, Rogatko, André

“…Cancer clinical trials typically generate detailed patient toxicity data. The most common measure used to summarize patient toxicity is the maximum grade among…”
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SAMPLED FORMS OF FUNCTIONAL PCA IN REPRODUCING KERNEL HILBERT SPACES by Amini, Arash A., Wainwright, Martin J.

“…We consider the sampling problem for functional PCA (fPCA), where the simplest example is the case of taking time samples of the underlying functional…”
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Generalized Autoregressive Linear Models for Discrete High-Dimensional Data by Pandit, Parthe, Sahraee-Ardakan, Mojtaba, Amini, Arash A., Rangan, Sundeep, Fletcher, Alyson K.

“…Fitting multivariate autoregressive (AR) models is fundamental for time-series data analysis in a wide range of applications. This article considers the…”
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Spectrally-truncated kernel ridge regression and its free lunch by Amini, Arash A.

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Performance evaluation of automotive dealerships using grouped mixture of regressions by Almohri, Haidar, Chinnam, Ratna Babu, Amini, Arash A.

“…Finite Mixture of Regressions (FMR) are among the most widely used models for dealing with heterogeneity in regression problems. FMR is a model-based…”
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High-dimensional analysis of semidefinite relaxations for sparse principal components by Amini, A.A., Wainwright, M.J.

“…In problem of sparse principal components analysis (SPCA), the goal is to use n i.i.d. samples to estimate the leading eigenvector(s) of a p times p covariance…”
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Structured regression models for high-dimensional spatial spectroscopy data by Amini, Arash A., Levina, Elizaveta, Shedden, Kerby A.

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Sharp Bounds for Poly-GNNs and the Effect of Graph Noise by Vinas, Luciano, Amini, Arash A

“…We investigate the classification performance of graph neural networks with graph-polynomial features, poly-GNNs, on the problem of semi-supervised node…”
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Graph Neural Thompson Sampling by Wu, Shuang, Amini, Arash A

“…We consider an online decision-making problem with a reward function defined over graph-structured data. We formally formulate the problem as an instance of…”
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Step and Smooth Decompositions as Topological Clustering by Vinas, Luciano, Amini, Arash A

“…We investigate a class of recovery problems for which observations are a noisy combination of continuous and step functions. These problems can be seen as…”
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Simplifying GNN Performance with Low Rank Kernel Models by Vinas, Luciano, Amini, Arash A

“…We revisit recent spectral GNN approaches to semi-supervised node classification (SSNC). We posit that many of the current GNN architectures may be…”
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Approximation properties of certain operator-induced norms on Hilbert spaces by Amini, Arash A., Wainwright, Martin J.

“…We consider a class of operator-induced norms, acting as finite-dimensional surrogates to the L 2 norm, and study their approximation properties over Hilbert…”
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