Research on nearest-neighbor methods tends to focus somewhat dichotomous...
We show that a recently proposed 1-nearest-neighbor-based multiclass lea...
Learning by demonstration is a versatile and rapid mechanism for transfe...
We address the problem of estimating the mixing time t_mix of an
arbitra...
We exhibit an efficient procedure for testing, based on a single long st...
We consider a model of robust learning in an adversarial environment. Th...
We obtain the first positive results for bounded sample compression in t...
We compute the finite-sample minimax (modulo logarithmic factors) sample...
We present a near-optimal algorithm for properly learning convex polytop...
We give an algorithmically efficient version of the learner-to-compressi...
We establish a tight characterization of the worst-case rates for the ex...
The vehicular connectivity revolution is fueling the automotive industry...
The spectral gap γ of a finite, ergodic, and reversible Markov chain
is ...
We show that a simple modification of the 1-nearest neighbor classifier
...
We revisit the classical decision-theoretic problem of weighted expert v...
We informally call a stochastic process learnable if it admits a
general...
Recent advances in large-margin classification of data residing in gener...
We present a novel approach for learning an HMM whose outputs are distri...
We study adaptive data-dependent dimensionality reduction in the context...