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Found 6 papers, 0 papers with code
Screening Rules for Convex Problems
We propose a new framework for deriving screening rules for convex optimization problems.
Algorithms for Learning Sparse Additive Models with Interactions in High Dimensions
A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is a Sparse Additive Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in \mathcal{S}}\phi_{l}(x_l)$ where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll d$.
Learning Sparse Additive Models with Interactions in High Dimensions
For some $\mathcal{S}_1 \subset [d], \mathcal{S}_2 \subset {[d] \choose 2}$, the function $f$ is assumed to be of the form: $$f(\mathbf{x}) = \sum_{p \in \mathcal{S}_1}\phi_{p} (x_p) + \sum_{(l, l^{\prime}) \in \mathcal{S}_2}\phi_{(l, l^{\prime})} (x_{l}, x_{l^{\prime}}).$$ Assuming $\phi_{p},\phi_{(l, l^{\prime})}$, $\mathcal{S}_1$ and, $\mathcal{S}_2$ to be unknown, we provide a randomized algorithm that queries $f$ and exactly recovers $\mathcal{S}_1,\mathcal{S}_2$.
Efficient Sampling for Learning Sparse Additive Models in High Dimensions
We consider the problem of learning sparse additive models, i. e., functions of the form: $f(\vecx) = \sum_{l \in S} \phi_{l}(x_l)$, $\vecx \in \matR^d$ from point queries of $f$.
Stochastic continuum armed bandit problem of few linear parameters in high dimensions
We consider a stochastic continuum armed bandit problem where the arms are indexed by the $\ell_2$ ball $B_{d}(1+\nu)$ of radius $1+\nu$ in $\mathbb{R}^d$.
Continuum armed bandit problem of few variables in high dimensions
We consider the stochastic and adversarial settings of continuum armed bandits where the arms are indexed by [0, 1]^d.
