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..
AdaBoost is not an Optimal Weak to Strong Learner
Authors: Mikael Møller Høgsgaard, Kasper Green Larsen, Martin Ritzert
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main contribution of this work is to show that Ada Boost is not always optimal. Concretely, we show that there exists a weak learner W, such that if Ada Boost is run with W as its weak learner, its sample complexity is sub-optimal by at least one logarithmic factor. This is stated in the following theorem: Theorem 1.1. For any 0 < gamma < C for C > 0 sufficiently small, any d = Omega(ln(1/gamma)), and any exp( exp(Omega(d))) epsilon C, there exists a gamma-weak learner W using a hypothesis set H of VC-dimension d and a distribution D, such that Ada Boost run with W is sub-optimal and needs m Ada(epsilon) = Omega d ln(1/epsilon) samples from D to output with constant probability, a hypothesis with error at most epsilon under D. |
| Researcher Affiliation | Academia | Mikael Møller Høgsgaard * 1 Kasper Green Larsen * 1 Martin Ritzert * 2 1Group of Algorithms, Data Structures and Foundations of Machine Learning, Aarhus University, Aarhus, Denmark 2Neural Data Science, Georg-August-Universit at G ottingen, G ottingen, Germany. |
| Pseudocode | Yes | Algorithm 1: Ada Boost |
| Open Source Code | No | The paper is theoretical and focuses on proofs and analysis; it does not mention or provide any open-source code for the work described. |
| Open Datasets | No | The paper is theoretical and discusses concepts like 'training data' and 'data distribution D' in a theoretical context, but it does not mention or provide access information for any specific publicly available datasets used in empirical experiments. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments that would involve training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on proofs and analysis, so it does not include details about an experimental setup, hyperparameters, or training configurations. |