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Statistical agencies often publish multiple data products from the samesurvey. First, they produce aggregate estimates of various features of thedistributions of several socio-demographic quantities of interest. Often thesearea-level estimates are tabulated at small geographies. Second...
The paper deals with learning the probability distribution of the observeddata by artificial neural networks. We suggest a so-called gradient conjugateprior (GCP) update appropriate for neural networks, which is a modification ofthe classical Bayesian update for conjugate priors. We establish a...
A general method of minimization using correlation coefficients and orderstatistics is evaluated relative to least squares procedures in the estimationof parameters for normal data in simple linear regression.
One of the biggest challenges in the research of generative adversarialnetworks (GANs) is assessing the quality of generated samples and detectingvarious levels of mode collapse. In this work, we construct a novel measure ofperformance of a GAN by comparing geometrical properties of the underlying...
People are living longer than ever before, and with this arise new complications and challenges for humanity. Among the most pressing of these challenges is to understand the role of aging in the development of dementia.This paper is motivated by the Mayo Clinic Study of Aging data for 4742subjects...
Facing large amounts of data, subsampling is a practical technique to extractuseful information. For this purpose, Wang et al. (2017) developed an OptimalSubsampling Method under the A-optimality Criterion (OSMAC) for logisticregression that samples more informative data points with higher...
We have introduce a new vision of stochastic processes through the geometryinduced by the dilation. The dilation matrices of a given processes areobtained by a composition of rotations matrices, contain the measureinformation in a condensed way. Particularly interesting is the fact that theobtention...
Transductive Adversarial Networks (TAN) is a novel domain-adaptation machinelearning framework that is designed for learning a conditional probabilitydistribution on unlabelled input data in a target domain, while also onlyhaving access to: (1) easily obtained labelled data from a related...
We consider the estimation of the average treatment effect in the treated asa function of baseline covariates, where there is a valid (conditional)instrument.We describe two doubly robust (DR) estimators: a locally efficientg-estimator, and a targeted minimum loss-based estimator (TMLE). These two...
We consider Non-Homogeneous Hidden Markov Models (NHHMMs) for forecastingunivariate time series. We introduce two state NHHMMs where the time series aremodeled via different predictive regression models for each state. Also, thetime-varying transition probabilities depend on exogenous variables...
We present a variational renormalization group approach using deep generativemodel composed of bijectors. The model can learn hierarchical transformations between physical variables and renormalized collective variables. It can directly generate statistically independent physical configurations by...
Proteins are commonly used by biochemical industry for numerous processes.Refining these proteins' properties via mutations causes stability effects aswell. Accurate computational method to predict how mutations affect proteinstability are necessary to facilitate efficient protein design. However...
Leadership urged to consider societal and ethical questions alongside the technical.
Subjective classification of galaxies can mislead us in the quest of theorigin regarding formation and evolution of galaxies. Multivariate analyses arethe best tools used for such kind of purpose to better understand thedifferences between various objects, in an objective manner. In the presentstudy...
With new approach, researchers specify desired properties of a material, and a computer system generates a structure accordingly.
"We need to look to the past in the face of modern innovations in machine learning, robotics, artificial intelligence, big data, and beyond," says the economist.
The following email was sent today to the MIT community by President L. Rafael Reif.To the members of the MIT community,This morning, MIT is launching a major new Institute-wide initiative on human and machine intelligence — the MIT Intelligence Quest, or MIT IQ — and I’m eager to explain why this…
Online Decomposition of Compressive Streaming Data Using $n$-$\ell_1$ Cluster-Weighted Minimization.
We consider a decomposition method for compressive streaming data in thecontext of online compressive Robust Principle Component Analysis (RPCA). Theproposed decomposition solves an $n$-$\ell_1$ cluster-weighted minimization todecompose a sequence of frames (or vectors), into sparse and low...
New Institute-wide initiative will advance human and machine intelligence research.
Digital currency and blockchain technology added to this year’s list of “playful tech” eligible startups.
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