8 citations · 18 across the 23 of their papers we have counts for
9 papers · 1 filter
A Distributional-Lifting Theorem for PAC Learning
Guy Blanc, Jane Lange, Carmen Strassle +1
The apparent difficulty of efficient distribution-free PAC learning has led to a large body of work on distribution-specific learning. Distributional assumptions facilitate the des…
Harnessing the Power of Choices in Decision Tree Learning
Guy Blanc, Jane Lange, Chirag Pabbaraju +3
We propose a simple generalization of standard and empirically successful decision tree learning algorithms such as ID3, C4.5, and CART. These algorithms, which have been central t…
Popular decision tree algorithms are provably noise tolerant
Guy Blanc, Jane Lange, Ali Malik +1
Using the framework of boosting, we prove that all impurity-based decision tree learning algorithms, including the classic ID3, C4.5, and CART, are highly noise tolerant. Our guara…
On the power of adaptivity in statistical adversaries
Guy Blanc, Jane Lange, Ali Malik +1
We study a fundamental question concerning adversarial noise models in statistical problems where the algorithm receives i.i.d. draws from a distribution . The definit…
Provably efficient, succinct, and precise explanations
Guy Blanc, Jane Lange, Li-Yang Tan
We consider the problem of explaining the predictions of an arbitrary blackbox model : given query access to and an instance , output a small set of 's features that i…
Decision tree heuristics can fail, even in the smoothed setting
Guy Blanc, Jane Lange, Mingda Qiao +1
Greedy decision tree learning heuristics are mainstays of machine learning practice, but theoretical justification for their empirical success remains elusive. In fact, it has long…
Learning stochastic decision trees
Guy Blanc, Jane Lange, Li-Yang Tan
We give a quasipolynomial-time algorithm for learning stochastic decision trees that is optimally resilient to adversarial noise. Given an -corrupted set of uniform random sampl…
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: \{-…