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Automatic identification of bird calls without manual intervention has been a challenging task for meaningful research on the taxonomy and monitoring of bird migrations in ornithology. In this paper we apply several techniques used in speech recognition to the automatic identification of bird calls. A new technique which computes the ensemble average on the… (More)

We consider the problem of actively learning multi-index functions of the form f (x) = g(Ax) = k i=1 g i (a T i x) from point evaluations of f. We assume that the function f is defined on an 2-ball in R d , g is twice continuously differen-tiable almost everywhere, and A ∈ R k×d is a rank k matrix, where k d. We propose a randomized, active sampling scheme… (More)

Numerous dimensionality reduction problems in data analysis involve the recovery of low-dimensional models or the learning of manifolds underlying sets of data. Many manifold learning methods require the estimation of the tangent space of the manifold at a point from locally available data samples. Local sampling conditions such as (i) the size of the… (More)

We consider the stochastic and adversarial settings of continuum armed bandits where the arms are indexed by [0, 1] d. The reward functions r : [0, 1] d → R are assumed to intrinsically depend on at most k to be fixed across time, we propose a simple modification of the CAB1 algorithm where we construct the discrete set of sampling points to obtain a bound… (More)

We consider the problem of learning sparse additive models, i.e., functions of the form: f (x) = l∈S φ l (x l), x ∈ R d from point queries of f. Here S is an unknown subset of coordinate variables with |S| = k d. Assuming φ l 's to be smooth, we propose a set of points at which to sample f and an efficient random-ized algorithm that recovers a uniform… (More)

A function f : R d → R is referred to as a Sparse Additive Model (SPAM), if it is of the form f (x) = l∈S φ l (x l), where S ⊂ [d], |S| d. Assuming φ l 's and S to be unknown, the problem of estimating f from its samples has been studied extensively. In this work, we consider a generalized SPAM, allowing for second order interaction terms. For some S 1 ⊂… (More)

We consider the problem of continuum armed bandits where the arms are indexed by a compact subset of ℝ d $\mathbb {R}^{d}$ . For large d, it is well known that mere smoothness assumptions on the reward functions lead to regret bounds that suffer from the curse of dimensionality. A typical way to tackle this in the literature has been to make further… (More)

We study the problem of learning ridge functions of the form f (x) = g(a T x), x ∈ R d , from random samples. Assuming g to be a twice continuously differentiable function, we leverage techniques from low rank matrix recovery literature to derive a uniform approximation guarantee for estimation of the ridge function f. Our new analysis removes the de facto… (More)

A function f : R d → R is a Sparse Additive Model (SPAM), if it is of the form f (x) = l∈S φ l (x l) where S ⊂ [d], |S| d. Assuming φ's, S to be unknown, there exists extensive work for estimating f from its samples. In this work, we consider a generalized version of SPAMs, that also allows for the presence of a sparse number of second order interaction… (More)

We consider a stochastic continuum armed bandit problem where the arms are indexed by the ℓ2 ball B d (1+ν) of radius 1+ν in R d. The reward functions r : B d (1+ν) → R are considered to intrinsically depend on k ≪ d unknown linear parameters so that r(x) = g(Ax) where A is a full rank k × d matrix. Assuming the mean reward function to be smooth we make use… (More)

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