Publications related to Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse ...

IEEE2019

Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces

Volkan Cevher, Junhong Lin

In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space. We investigate a class of spectral/regularized algorithms, including ridge regressi ...

2018

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Volkan Cevher, Junhong Lin

We investigate regularized algorithms combining with projection for least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space. We prove convergence results with respect to variants of n ...

ICML2018

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Dimitri Nestor Alice Van De Ville, Stephan Morgenthaler, Thomas William Arthur Bolton, Maria Giulia Preti, Djalel Eddine Meskaldji

An important question in neuroscience is to reveal the relationship between individual performance and brain activity. This could be achieved by applying model regression techniques, in which functional connectivity derived from resting-state functional ma ...

Ieee2016

From averaging to acceleration, there is only a step-size

Nicolas Henri Bernard Flammarion

We consider the optimization of a quadratic objective function whose gradients are only accessible through a stochastic oracle that returns the gradient at any given point plus a zero-mean finite variance random error. We present the first algorithm that a ...

2015

Forecasting Uncertainty in Electricity Demand

Tri Kurniawan Wijaya

Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and lepto ...

2015

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

In this paper, we prove a new identity for the least-square solution of an over-determined set of linear equation

Ax=b

, where

A

is an

m\times n

full-rank matrix,

b

is a column-vector of dimension

m

, and

m

(the number of equations) is larger tha ...

2015

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

In this paper, we prove a new identity for the least-square solution of an over-determined set of linear equation

Ax=b

, where

A

is an

m\times n

full-rank matrix,

b

is a column-vector of dimension

m

, and

m

(the number of equations) is larger tha ...

Idiap2015

MATHICSE Technical Report : Discrete least squares polynomial approximation with random evaluations – application to parametric and stochastic elliptic PDES

Fabio Nobile, Giovanni Migliorati

Motivated by the numerical treatment of parametric and stochastic PDEs, we analyze the least-squares method for polynomial approximation of multivariate func- tions based on random sampling according to a given probability measure. Recent work has shown th ...

MATHICSE2014

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

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Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

From averaging to acceleration, there is only a step-size

Forecasting Uncertainty in Electricity Demand

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

MATHICSE Technical Report : Discrete least squares polynomial approximation with random evaluations – application to parametric and stochastic elliptic PDES

Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

From averaging to acceleration, there is only a step-size

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Forecasting Uncertainty in Electricity Demand

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

MATHICSE Technical Report : Discrete least squares polynomial approximation with random evaluations – application to parametric and stochastic elliptic PDES