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Optimistic bounds for multi-output learning
We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set.
Statistical optimality conditions for compressive ensembles
For each of these tasks, our strategy is to develop a tight upper bound on the compressibility function, and by doing so we discover distributional conditions of geometric nature under which the compressive algorithm attains minimax-optimal rates up to at most poly-logarithmic factors.
Optimistic bounds for multi-output prediction
We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set.
Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise
We consider classification in the presence of class-dependent asymmetric label noise with unknown noise probabilities.
Classification with unknown class-conditional label noise on non-compact feature spaces
In order to obtain finite sample rates, previous approaches to classification with unknown class-conditional label noise have required that the regression function is close to its extrema on sets of large measure.
