Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Provable guarantees for decision tree induction: the agnostic setting
Authors: Guy Blanc, Jane Lange, Li-Yang Tan
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give strengthened provable guarantees on the performance of widely employed and empirically successful top-down decision tree learning heuristics. While prior works have focused on the realizable setting, we consider the more realistic and challenging agnostic setting. We show that for all monotone functions f and s N, these heuristics O ((log s)/ε2 construct a decision tree of size s ) that achieves error opt + ε, where opt denotes s s the error of the optimal size-s decision tree for f. Previously such a guarantee was not known to be achievable by any algorithm, even one that is not based on top-down heuristics. We complement our algorithmic guarantee with a near-matching sΩ(log s) lower bound. |
| Researcher Affiliation | Academia | 1Stanford University. Corre spondence to: Guy Blanc <EMAIL>, Jane Lange <EMAIL>, Li-Yang Tan <EMAIL>. |
| Pseudocode | Yes | BUILDTOPDOWNDTG ,D(f, t): Initialize T to be the empty tree. while (size(T ) < t) { Grow T by splitting leaf with a query to 1[xi θ], where and 1[xi θ] maximize: G -impurityf,D(T ) G -impurityf,D(T ,1[xi θ]), the purity gain with respect to G and D. Output the f-completion of T .} |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code release related to the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets, thus no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with data splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |