most citedTop-down induction of decision trees: rigorous guarantees and inherent limitations

5 citations · 9 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ML20204 cited

Efficient hyperparameter optimization by way of PAC-Bayes bound minimization

John J. Cherian, Andrew G. Taube, Robert T. McGibbon +6

Identifying optimal values for a high-dimensional set of hyperparameters is a problem that has received growing attention given its importance to large-scale machine learning appli…

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.CC2019

Constructive derandomization of query algorithms

Guy Blanc, Jane Lange, Li-Yang Tan

We give efficient deterministic algorithms for converting randomized query algorithms into deterministic ones. We first give an algorithm that takes as input a randomized -query…

cs.DS20195 cited

Top-down induction of decision trees: rigorous guarantees and inherent limitations

Guy Blanc, Jane Lange, Li-Yang Tan

Consider the following heuristic for building a decision tree for a function . Place the most influential variable of at the root, and recurs…

cs.LG2019

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…