10 citations · 12 across the 4 of their papers we have counts for
5 papers · 1 filter
When Does Confidence-Based Cascade Deferral Suffice?
Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon +3
Cascades are a classical strategy to enable inference cost to vary adaptively across samples, wherein a sequence of classifiers are invoked in turn. A deferral rule determines whet…
Estimating decision tree learnability with polylogarithmic sample complexity
Guy Blanc, Neha Gupta, Jane Lange +1
We show that top-down decision tree learning heuristics are amenable to highly efficient learnability estimation: for monotone target functions, the error of the decision tree hypo…
Universal guarantees for decision tree induction via a higher-order splitting criterion
Guy Blanc, Neha Gupta, Jane Lange +1
We propose a simple extension of top-down decision tree learning heuristics such as ID3, C4.5, and CART. Our algorithm achieves provable guarantees for all target functions $f: \{-…
Active Local Learning
Arturs Backurs, Avrim Blum, Neha Gupta
In this work we consider active local learning: given a query point , and active access to an unlabeled training set , output the prediction of a near-optimal $h \in H…
Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process
Guy Blanc, Neha Gupta, Gregory Valiant +1
We consider networks, trained via stochastic gradient descent to minimize loss, with the training labels perturbed by independent noise at each iteration. We characterize…