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Efficient Algorithm for Extremely Large Multi-task Regression with Massive Structured Sparsity
Algorithm Large Multi-task Regression Massive Structured Sparsity
2012/9/17
We develop a highly scalable optimization method called “hierarchical group-thresholding”for solving a multi-task regression model with complex structured sparsity constraints on both input and output...
Information-Theoretic Measures of Influence Based on Content Dynamics
entropy link prediction causality social networks
2012/9/18
The fundamental building block of social in uence is for one person to elicit a response in another. Researchers measur-ing a \response" in social media typically depend either on detailed models of h...
Higher Variations of the Monty Hall Problem (3.0 and 4.0) and Empirical Definition of the Phenomenon of Mathematics, in Boole's Footsteps, as Something the Brain Does
Artificial Intelligence Binary Structure Boolean Algebra Boolean Operators Boole’s Algebra Brain Science, Cognition Cognitive Science Definition of Mathematics Definition of Probability Theory Digital Mathematics Electrical Engineering, Foundations of Mathematics Human Intelligence, Linguistics, Logic, Monty Hall Problem, Neuroscience Non-quantitative and Quantitative Mathematics Probability Theory Rational Thought and Language
2012/9/17
In Advances in Pure Mathematics (www.scirp.org/journal/apm) , Vol. 1, No. 4 (July 2011), pp.
136-154, the mathematical structure of the much discussed problem of probability known as the Monty Ha...
Variable Selection with Exponential Weights and $l_0$-Penalization
Variable selection model selection sparse linear model xponential weights Gibbs sampler identifiability condition.
2012/9/17
In the context of a linear model with a sparse coefficient vector, exponential weights methods have been shown to be achieve oracle inequalities for prediction. We show that such methods also succeed ...
Nonparametric sparsity and regularization
Sparsity Nonparametrics Variable selection Regularization Proximal meth-ods RKHS
2012/9/17
In this work we are interested in the problems of supervised learning and variable se-lection when the input-output dependence is described by a nonlinear function depending on a few variables. Our go...
Modelling interactions in high-dimensional data with Backtracking
Backtracking interactions Lasso parallel computing path algorithm.
2012/9/17
We study the problem of high-dimensional regression when there may be interacting vari-ables. We introduce a new idea called Backtracking, that can be incorporated into many existing high-dimensional ...
Fractal-driven distortion of resting state functional networks in fMRI: a simulation study
Fractal-driven distortion resting state functional networks simulation study
2012/9/17
Fractals are self-similar and scale-invariant patterns found ubiquitously in nature. A lot of evidences implying fractal properties such as 1/f power spectrums have been also observed in resting state...
Comparative Bi-stochastizations and Associated Clusterings/Regionalizations of the 1995-2000 U. S. Intercounty Migration Network
Comparative Bi-stochastizations Associated Clusterings/Regionalizations the 1995-2000 U. S. Intercounty Migration Network
2012/9/18
Wang, Li and Konig have recently compared the cluster-theoretic properties of bi-stochasticized symmetric data similarity (e. g. kernel) matrices, produced by minimizing two di erent forms of Bregman...
Comparative Bi-stochastizations and Associated Clusterings/Regionalizations of the 1995-2000 U. S. Intercounty Migration Network
Comparative Bi-stochastizations Associated Clusterings/Regionalizations the 1995-2000 U. S. Intercounty Migration Network
2012/9/18
Wang, Li and Konig have recently compared the cluster-theoretic properties of bi-stochasticized symmetric data similarity (e. g. kernel) matrices, produced by minimizing two di erent forms of Bregman...
On the consistency of AUC Optimization
AUC consistency surrogate loss cost-sensitive learning learning to rank
2012/9/18
AUC (area under ROC curve) is an important evaluation criterion, which has been popularly used in diverse learning tasks such as class-imbalance learning, cost-sensitive learning, learning to rank and...
The Pythagorean Won-Loss Formula and Hockey: A Statistical Justification for Using the Classic Baseball Formula as an Evaluative Tool in Hockey
The Pythagorean Won-Loss Formula Hockey Statistical Justification Baseball Formula Evaluative Tool
2012/9/17
Originally devised for baseball, the Pythagorean Won-Loss formula estimates the percentage of games a team should have won at a particular point in a season. For decades, this formula had no mathemat...
Test MaxEnt in Social Strategy Transitions with Experimental Two-Person Constant Sum 2$\times$2 Games
maximum entropy principle social strategy transitions constant sum game experimental eco-nomics
2012/9/18
Using laboratory experimental data, we test the uncertainty of social state transitions in various competing environments of fixed paired two-person constantsum 2×2 games. It firstly shows that,the di...
Exploring wind direction and SO2 concentration by circular-linear density estimation
Circular distributions Circular kernel estimation Circular{linear data Copula.
2012/9/17
The study of environmental problems usually requires the description of variables with di er-ent nature and the assessment of relations between them. In this work, an algorithm for exible estimation o...
Statistical inference in compound functional models
Compound functional model minimax estimation sparse additive structure dimen-sion reduction structure adaptation
2012/9/18
We consider a general nonparametric regression model called the compound model. It includes,as special cases, sparse additive regression and nonparametric (or linear) regression with many covariates b...
General lower bounds on maximal determinants of binary matrices
General lower bounds maximal determinants binary matrices
2012/9/18
We give general lower bounds on the maximal determinant ofn×n{+1,−1}matrices, both with and without the assumption of the Hadamard conjecture. Our bounds improve on earlier resultsof de Launey a...