Decision trees are PAC-learnable from most product distributions: a
smoothed analysis

Decision trees are PAC-learnable from most product distributions: a smoothed analysis

4 December 2008

Adam Tauman Kalai

ArXiv (abs)PDF HTML

Papers citing "Decision trees are PAC-learnable from most product distributions: a smoothed analysis"

10 / 10 papers shown

Title
Learning Juntas under Markov Random Fields Gautam Chandrasekaran Adam R. Klivans 42 0 0 01 Jun 2025
Modifying Final Splits of Classification Tree for Fine-tuning Subpopulation Target in Policy Making Lei Bill Wang Zhenbang Jiao Fangyi Wang 97 0 0 24 Feb 2025
Backdoor defense, learnability and obfuscation Paul Christiano Jacob Hilton Victor Lecomte Mark Xu AAML 50 1 0 04 Sep 2024
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension Gautam Chandrasekaran Adam R. Klivans Vasilis Kontonis Raghu Meka Konstantinos Stavropoulos 117 4 0 01 Jul 2024
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy Amit Daniely Nathan Srebro Gal Vardi 96 5 0 15 Feb 2023
Smoothed Analysis with Adaptive Adversaries Nika Haghtalab Tim Roughgarden Abhishek Shetty AAML 128 54 0 16 Feb 2021
Bounded Memory Active Learning through Enriched Queries Max Hopkins D. Kane Shachar Lovett Michal Moshkovitz 49 7 0 09 Feb 2021
Smoothed Analysis of Online and Differentially Private Learning Nika Haghtalab Tim Roughgarden Abhishek Shetty 86 51 0 17 Jun 2020
On the Optimality of Trees Generated by ID3 Alon Brutzkus Amit Daniely Eran Malach 72 10 0 11 Jul 2019
ID3 Learns Juntas for Smoothed Product Distributions Alon Brutzkus Amit Daniely Eran Malach 67 21 0 20 Jun 2019