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20192026
most citedProvably efficient, succinct, and precise explanations

8 citations · 18 across the 23 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.DS2020

Reconstructing decision trees

Guy Blanc, Jane Lange, Li-Yang Tan

We give the first {\sl reconstruction algorithm} for decision trees: given queries to a function that is -close to a size- decision tree, our algorithm provide…

cs.LG2020

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…

cs.DS2020

Query strategies for priced information, revisited

Guy Blanc, Jane Lange, Li-Yang Tan

We consider the problem of designing query strategies for priced information, introduced by Charikar et al. In this problem the algorithm designer is given a function $f : \{0,1\}^…

cs.LG2020★ 2 cited

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: \{-…

cs.DS2020

Provable guarantees for decision tree induction: the agnostic setting

Guy Blanc, Jane Lange, Li-Yang Tan

We give strengthened provable guarantees on the performance of widely employed and empirically successful {\sl top-down decision tree learning heuristics}. While prior works have f…

cs.LO2020

Politeness for the Theory of Algebraic Datatypes

Ying Sheng, Yoni Zohar, Christophe Ringeissen +3

Algebraic datatypes, and among them lists and trees, have attracted a lot of interest in automated reasoning and Satisfiability Modulo Theories (SMT). Since its latest stable versi…