1 citations · 1 across the 7 of their papers we have counts for
9 papers · 1 filter
Samplability makes learning easier
Guy Blanc, Caleb Koch, Jane Lange +2
The standard definition of PAC learning (Valiant 1984) requires learners to succeed under all distributions -- even ones that are intractable to sample from. This stands in contras…
Computational-Statistical Tradeoffs from NP-hardness
Guy Blanc, Caleb Koch, Carmen Strassle +1
A central question in computer science and statistics is whether efficient algorithms can achieve the information-theoretic limits of statistical problems. Many computational-stati…
Fast decision tree learning solves hard coding-theoretic problems
Caleb Koch, Carmen Strassle, Li-Yang Tan
We connect the problem of properly PAC learning decision trees to the parameterized Nearest Codeword Problem (-NCP). Despite significant effort by the respective communities, al…
Superconstant Inapproximability of Decision Tree Learning
Caleb Koch, Carmen Strassle, Li-Yang Tan
We consider the task of properly PAC learning decision trees with queries. Recent work of Koch, Strassle, and Tan showed that the strictest version of this task, where the hypothes…
A Strong Direct Sum Theorem for Distributional Query Complexity
Guy Blanc, Caleb Koch, Carmen Strassle +1
Consider the expected query complexity of computing the -fold direct product of a function to error with respect to a distribution . One s…
Properly Learning Decision Trees with Queries Is NP-Hard
Caleb Koch, Carmen Strassle, Li-Yang Tan
We prove that it is NP-hard to properly PAC learn decision trees with queries, resolving a longstanding open problem in learning theory (Bshouty 1993; Guijarro-Lavin-Raghavan 1999;…