this paper we consider the advantage that may be taken of possible sparsity in the sequence. In what contexts do problems of this kind arise? Some examples are the following
The use of L1 regularisation for sparse learning has generated immense research interest, with succe...
Learning sparsity pattern in high dimension is a great challenge in both implementation and theory. ...
To restrict ourselves to the regime of sparse solutions has become the new paradigm for modern stati...
The problem of estimating a high-dimensional sparse vector θ ∈ ℝ n from an observation in i.i.d. Gau...
Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by...
We construct a classifier which attains the rate of convergence $\log n/n$ under sparsity and margin...
In the general signal+noise (allowing non-normal, non-independent observations) model, we construct ...
In the signal+noise model, we assume that the signal has a more general sparsity structure in the se...
The problem of estimating a high-dimensional sparse vector $\boldsymbol{\theta} \in \mathbb{R}^n$ fr...
We discuss predictive density for Poisson sequence models under sparsity constraints. Sparsity in co...
Suppose that a sequence of unknown parameters is observed sub ject to independent Gaussian noise. Th...
Thesis (Ph.D.)--University of Washington, 2019The concept of `sparsity' is common to see in many top...
International audienceDue to its linear complexity, naive Bayes classification remains an attractive...
Presented on August 31, 2018 from 2:00 p.m.-3:00 p.m. at the Georgia Institute of Technology (Georgi...
Abstract Recent years witnessed the proliferation of the notion of sparsity and its applications in ...
The use of L1 regularisation for sparse learning has generated immense research interest, with succe...
Learning sparsity pattern in high dimension is a great challenge in both implementation and theory. ...
To restrict ourselves to the regime of sparse solutions has become the new paradigm for modern stati...
The problem of estimating a high-dimensional sparse vector θ ∈ ℝ n from an observation in i.i.d. Gau...
Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by...
We construct a classifier which attains the rate of convergence $\log n/n$ under sparsity and margin...
In the general signal+noise (allowing non-normal, non-independent observations) model, we construct ...
In the signal+noise model, we assume that the signal has a more general sparsity structure in the se...
The problem of estimating a high-dimensional sparse vector $\boldsymbol{\theta} \in \mathbb{R}^n$ fr...
We discuss predictive density for Poisson sequence models under sparsity constraints. Sparsity in co...
Suppose that a sequence of unknown parameters is observed sub ject to independent Gaussian noise. Th...
Thesis (Ph.D.)--University of Washington, 2019The concept of `sparsity' is common to see in many top...
International audienceDue to its linear complexity, naive Bayes classification remains an attractive...
Presented on August 31, 2018 from 2:00 p.m.-3:00 p.m. at the Georgia Institute of Technology (Georgi...
Abstract Recent years witnessed the proliferation of the notion of sparsity and its applications in ...
The use of L1 regularisation for sparse learning has generated immense research interest, with succe...
Learning sparsity pattern in high dimension is a great challenge in both implementation and theory. ...
To restrict ourselves to the regime of sparse solutions has become the new paradigm for modern stati...
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