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..
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions
Authors: Maria-Florina F. Balcan, Hongyang Zhang
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide new results for noise-tolerant and sample-efficient learning algorithms under s-concave distributions. ... In this work, we introduce new convex geometry tools to study the properties of s-concave distributions and use these properties to provide bounds on quantities of interest to learning including the probability of disagreement between two halfspaces, disagreement outside a band, and the disagreement coefficient. ... To prove Theorem 1, we introduce multiple new techniques... Our analysis of s-concave distributions bridges these algorithms to the strong guarantees of noise-tolerant and sample-efficient learning algorithms. |
| Researcher Affiliation | Academia | Maria-Florina Balcan Machine Learning Department Carnegie Mellon University, USA EMAIL Hongyang Zhang Machine Learning Department Carnegie Mellon University, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Margin Based Active Learning under S-Concave Distributions |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments that would involve training on a specific dataset. It discusses theoretical distributions rather than practical datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving datasets, thus it does not provide details on training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe specific software dependencies with version numbers, as it does not report empirical experiments requiring such details. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and algorithms. It does not describe an empirical experimental setup with specific hyperparameters or system-level training settings. |